---
OA_place: repository
OA_type: free access
_id: '21381'
abstract:
- lang: eng
text: 'The lack of long-range electrostatics is a key limitation of modern machine
learning interatomic potentials (MLIPs), hindering reliable applications to interfaces,
charge-transfer reactions, polar and ionic materials, and biomolecules. In this
Perspective, we distill two design principles behind the Latent Ewald Summation
framework, which can capture long-range interactions, charges, and electrical
response just by learning from standard energy and force training data: (i) use
a Coulomb functional form with environment-dependent charges to capture electrostatic
interactions, and (ii) avoid explicit training on ambiguous density functional
theory partial charges. When both principles are satisfied, substantial flexibility
remains: essentially any short-range MLIP can be augmented; charge equilibration
schemes can be added when desired; dipoles and Born effective charges can be inferred
or fine-tuned; and charge/spin-state embeddings or tensorial targets can be further
incorporated. We also discuss current limitations and open challenges. Together,
these minimal, physics-guided design rules suggest that incorporating long-range
electrostatics into MLIPs is simpler and perhaps more broadly applicable than
is commonly assumed.'
acknowledgement: "B.C. thanks Christoph Dellago for his mentorship and influence.
In addition to his seminal contributions to statistical mechanics, Christoph Dellago
is an early developer and adopter of machine learning interatomic potentials. B.C.
did two exchanges in the groups of Christoph Dellago and Jörg Behler in 2018, with
transformative impact on her research directions.\r\n\r\nWe thank Peichen Zhong
and Daniel S. King for useful feedback on the manuscript and for the collaborations
on the LES method.\r\n\r\nFunding acknowledgment: Research reported in this publication
was supported by the National Institute Of General Medical Sciences of the National
Institutes of Health under Award No. R35GM159986. The content is solely the responsibility
of the authors and does not necessarily represent the official views of the National
Institutes of Health."
article_number: '060901'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Dongjin
full_name: Kim, Dongjin
last_name: Kim
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Kim D, Cheng B. Long-range electrostatics for machine learning interatomic
potentials is easier than we thought. The Journal of Chemical Physics.
2026;164(6). doi:10.1063/5.0316886
apa: Kim, D., & Cheng, B. (2026). Long-range electrostatics for machine learning
interatomic potentials is easier than we thought. The Journal of Chemical Physics.
AIP Publishing. https://doi.org/10.1063/5.0316886
chicago: Kim, Dongjin, and Bingqing Cheng. “Long-Range Electrostatics for Machine
Learning Interatomic Potentials Is Easier than We Thought.” The Journal of
Chemical Physics. AIP Publishing, 2026. https://doi.org/10.1063/5.0316886.
ieee: D. Kim and B. Cheng, “Long-range electrostatics for machine learning interatomic
potentials is easier than we thought,” The Journal of Chemical Physics,
vol. 164, no. 6. AIP Publishing, 2026.
ista: Kim D, Cheng B. 2026. Long-range electrostatics for machine learning interatomic
potentials is easier than we thought. The Journal of Chemical Physics. 164(6),
060901.
mla: Kim, Dongjin, and Bingqing Cheng. “Long-Range Electrostatics for Machine Learning
Interatomic Potentials Is Easier than We Thought.” The Journal of Chemical
Physics, vol. 164, no. 6, 060901, AIP Publishing, 2026, doi:10.1063/5.0316886.
short: D. Kim, B. Cheng, The Journal of Chemical Physics 164 (2026).
corr_author: '1'
date_created: 2026-03-02T10:06:46Z
date_published: 2026-02-14T00:00:00Z
date_updated: 2026-03-02T14:46:24Z
day: '14'
department:
- _id: BiCh
doi: 10.1063/5.0316886
external_id:
arxiv:
- '2512.18029'
intvolume: ' 164'
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2512.18029
month: '02'
oa: 1
oa_version: Preprint
publication: The Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Long-range electrostatics for machine learning interatomic potentials is easier
than we thought
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 164
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
_id: '20011'
abstract:
- lang: eng
text: Heat transport in glasses over a wide temperature range is critical for applications
in gate dielectrics and thermal insulators but remains poorly understood due to
the challenges in modeling vibrational anharmonicity and configurational dynamics
across the glass transition. Recent predictions show an unusual decrease in thermal
conductivity (κ) with temperature in amorphous hafnia (a-HfO2), contrasting with
the typical trend in glasses. Using molecular dynamics with a machine-learning-based
neuroevolution potential, we compute κ of a-HfO2 from 50 K to 2000 K. At low temperatures,
the Wigner transport equation captures both anharmonicity and quantum statistics.
Above 1200 K, atomic diffusion invalidates the quasiparticle picture, and we resort
to the Green–Kubo method to capture convective transport. We further extend the
Wigner transport equation to supercooled a-HfO2, revealing the crucial role of
low-frequency modes in facilitating heat transport. The computed κ, based on both
Green–Kubo and Wigner transport theories, increases continuously with temperature
up to 2000 K.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: We thank Ludovic Berthier for fruitful discussions and Ting Liang
for providing the initial structures of a-SiO2. Z.Z. acknowledges funding from the
European Union’s Horizon 2020 Research and Innovation Programme, under Marie Skłodowska-Curie
grant agreement No. 101034413. The authors also acknowledge the research computing
facilities provided by HPC ISTA and ITS HKU.
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Zezhu
full_name: Zeng, Zezhu
id: 54a2c730-803f-11ed-ab7e-95b29d2680e7
last_name: Zeng
orcid: 0000-0001-5126-4928
- first_name: Xia
full_name: Liang, Xia
last_name: Liang
- first_name: Zheyong
full_name: Fan, Zheyong
last_name: Fan
- first_name: Yue
full_name: Chen, Yue
last_name: Chen
- first_name: Michele
full_name: Simoncelli, Michele
last_name: Simoncelli
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Zeng Z, Liang X, Fan Z, Chen Y, Simoncelli M, Cheng B. Thermal transport of
amorphous hafnia across the glass transition. ACS Materials Letters. 2025:2695-2701.
doi:10.1021/acsmaterialslett.5c00263
apa: Zeng, Z., Liang, X., Fan, Z., Chen, Y., Simoncelli, M., & Cheng, B. (2025).
Thermal transport of amorphous hafnia across the glass transition. ACS Materials
Letters. American Chemical Society. https://doi.org/10.1021/acsmaterialslett.5c00263
chicago: Zeng, Zezhu, Xia Liang, Zheyong Fan, Yue Chen, Michele Simoncelli, and
Bingqing Cheng. “Thermal Transport of Amorphous Hafnia across the Glass Transition.”
ACS Materials Letters. American Chemical Society, 2025. https://doi.org/10.1021/acsmaterialslett.5c00263.
ieee: Z. Zeng, X. Liang, Z. Fan, Y. Chen, M. Simoncelli, and B. Cheng, “Thermal
transport of amorphous hafnia across the glass transition,” ACS Materials Letters.
American Chemical Society, pp. 2695–2701, 2025.
ista: Zeng Z, Liang X, Fan Z, Chen Y, Simoncelli M, Cheng B. 2025. Thermal transport
of amorphous hafnia across the glass transition. ACS Materials Letters., 2695–2701.
mla: Zeng, Zezhu, et al. “Thermal Transport of Amorphous Hafnia across the Glass
Transition.” ACS Materials Letters, American Chemical Society, 2025, pp.
2695–701, doi:10.1021/acsmaterialslett.5c00263.
short: Z. Zeng, X. Liang, Z. Fan, Y. Chen, M. Simoncelli, B. Cheng, ACS Materials
Letters (2025) 2695–2701.
corr_author: '1'
date_created: 2025-07-13T22:01:24Z
date_published: 2025-06-30T00:00:00Z
date_updated: 2025-12-30T09:15:30Z
day: '30'
ddc:
- '530'
department:
- _id: BiCh
doi: 10.1021/acsmaterialslett.5c00263
ec_funded: 1
external_id:
isi:
- '001520226300001'
file:
- access_level: open_access
checksum: d61e63439ddeaef29e9a2ee0f65c4ec1
content_type: application/pdf
creator: dernst
date_created: 2025-12-30T09:13:06Z
date_updated: 2025-12-30T09:13:06Z
file_id: '20903'
file_name: 2025_ACSMaterialsLetters_Zeng.pdf
file_size: 2402059
relation: main_file
success: 1
file_date_updated: 2025-12-30T09:13:06Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 2695-2701
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: ACS Materials Letters
publication_identifier:
eissn:
- 2639-4979
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ZengZezhu/heat-conductivity-a-HfO2
scopus_import: '1'
status: public
title: Thermal transport of amorphous hafnia across the glass transition
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2025'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '20452'
abstract:
- lang: eng
text: Accurate modeling of long-range forces is critical in atomistic simulations,
as they play a central role in determining the properties of material and chemical
systems. However, standard machine learning interatomic potentials (MLIPs) often
rely on short-range approximations, limiting their applicability to systems with
significant electrostatics and dispersion forces. We recently introduced the Latent
Ewald Summation (LES) method, which captures long-range electrostatics without
explicitly learning atomic charges or charge equilibration. We benchmark LES on
diverse and challenging systems, including charged molecules, ionic liquids, electrolyte
solutions, polar dipeptides, surface adsorption, electrolyte/solid interfaces,
and solid-solid interfaces. Here we show that LES can reproduce the exact atomic
charges for classical systems with fixed charges and can infer dipole and quadrupole
moments, as well as the dipole derivative with respect to atomic positions, for
quantum mechanical systems. Moreover, LES can achieve better accuracy in energy
and force predictions compared to methods that explicitly learn from charges.
acknowledgement: We thank Chunyi Zhang for providing the TiO2(101)/NaCl+NaOH+HCl(aq)
dataset and for useful discussions. We thank Jia-Xin Zhu for providing the Pt(111)/KF(aq)
dataset. We thank Tsz Wai Ko and Jonas Finkler for useful discussions and for the
DFT-optimized Au2-MgO(001) structures. We thank Junmin Chen for discussions. D.K
and B.C. acknowledge funding from Toyota Research Institute Synthesis Advanced Research
Challenge. D.S.K. and P.Z. acknowledge funding from BIDMaP Postdoctoral Fellowship.
article_number: '8763'
article_processing_charge: Yes
article_type: original
author:
- first_name: Daniel S.
full_name: King, Daniel S.
last_name: King
- first_name: Dongjin
full_name: Kim, Dongjin
last_name: Kim
- first_name: Peichen
full_name: Zhong, Peichen
last_name: Zhong
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: King DS, Kim D, Zhong P, Cheng B. Machine learning of charges and long-range
interactions from energies and forces. Nature Communications. 2025;16.
doi:10.1038/s41467-025-63852-x
apa: King, D. S., Kim, D., Zhong, P., & Cheng, B. (2025). Machine learning of
charges and long-range interactions from energies and forces. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-025-63852-x
chicago: King, Daniel S., Dongjin Kim, Peichen Zhong, and Bingqing Cheng. “Machine
Learning of Charges and Long-Range Interactions from Energies and Forces.” Nature
Communications. Springer Nature, 2025. https://doi.org/10.1038/s41467-025-63852-x.
ieee: D. S. King, D. Kim, P. Zhong, and B. Cheng, “Machine learning of charges and
long-range interactions from energies and forces,” Nature Communications,
vol. 16. Springer Nature, 2025.
ista: King DS, Kim D, Zhong P, Cheng B. 2025. Machine learning of charges and long-range
interactions from energies and forces. Nature Communications. 16, 8763.
mla: King, Daniel S., et al. “Machine Learning of Charges and Long-Range Interactions
from Energies and Forces.” Nature Communications, vol. 16, 8763, Springer
Nature, 2025, doi:10.1038/s41467-025-63852-x.
short: D.S. King, D. Kim, P. Zhong, B. Cheng, Nature Communications 16 (2025).
corr_author: '1'
date_created: 2025-10-12T22:01:25Z
date_published: 2025-10-01T00:00:00Z
date_updated: 2026-02-16T12:21:50Z
day: '01'
ddc:
- '000'
department:
- _id: BiCh
doi: 10.1038/s41467-025-63852-x
external_id:
isi:
- '001586620700015'
pmid:
- '41034200'
file:
- access_level: open_access
checksum: 34b6005d349bbff85839c4e51d6c8725
content_type: application/pdf
creator: dernst
date_created: 2025-10-13T07:54:51Z
date_updated: 2025-10-13T07:54:51Z
file_id: '20460'
file_name: 2025_NatureComm_King.pdf
file_size: 4907055
relation: main_file
success: 1
file_date_updated: 2025-10-13T07:54:51Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Machine learning of charges and long-range interactions from energies and forces
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '20492'
abstract:
- lang: eng
text: The glassy thermal conductivities observed in crystalline inorganic perovskites
such as Cs3Bi2I6Cl3 are perplexing and lacking theoretical explanations. Here,
we first experimentally measure its thermal transport behavior from 20 to 300 K,
after synthesizing Cs3Bi2I6Cl3 single crystals. Using path-integral molecular
dynamics simulations driven by machine learning potentials, we reveal that Cs3Bi2I6Cl3
has large lattice distortions at low temperatures, which may be related to the
large atomic size mismatch. Employing the Wigner formulation of thermal transport,
we reproduce theexperimental thermal conductivities based on lattice-distorted
structures. This studythus provides a framework for predicting and understanding
glassy thermal transportin materials with strong lattice disorder.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: Z.Z. acknowledges the European Union’s Horizon2020 research and innovation
programme under the Marie Skłodowska-Curie Grant Agreement No. 101034413. We acknowledge
the high-performance computing facilities offered by Institute of Science and Technology
Austria and The University of Hong Kong.
article_processing_charge: No
article_type: original
author:
- first_name: Zezhu
full_name: Zeng, Zezhu
id: 54a2c730-803f-11ed-ab7e-95b29d2680e7
last_name: Zeng
orcid: 0000-0001-5126-4928
- first_name: Zheyong
full_name: Fan, Zheyong
last_name: Fan
- first_name: Michele
full_name: Simoncelli, Michele
last_name: Simoncelli
- first_name: Chen
full_name: Chen, Chen
last_name: Chen
- first_name: Ting
full_name: Liang, Ting
last_name: Liang
- first_name: Yue
full_name: Chen, Yue
last_name: Chen
- first_name: Geoff
full_name: Thornton, Geoff
last_name: Thornton
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Zeng Z, Fan Z, Simoncelli M, et al. Lattice distortion leads to glassy thermal
transport in crystalline Cs3Bi2I6Cl3. Proceedings of the National Academy of
Sciences. 2025;122(41):e2415664122. doi:10.1073/pnas.2415664122
apa: Zeng, Z., Fan, Z., Simoncelli, M., Chen, C., Liang, T., Chen, Y., … Cheng,
B. (2025). Lattice distortion leads to glassy thermal transport in crystalline
Cs3Bi2I6Cl3. Proceedings of the National Academy of Sciences. National
Academy of Sciences. https://doi.org/10.1073/pnas.2415664122
chicago: Zeng, Zezhu, Zheyong Fan, Michele Simoncelli, Chen Chen, Ting Liang, Yue
Chen, Geoff Thornton, and Bingqing Cheng. “Lattice Distortion Leads to Glassy
Thermal Transport in Crystalline Cs3Bi2I6Cl3.” Proceedings of the National
Academy of Sciences. National Academy of Sciences, 2025. https://doi.org/10.1073/pnas.2415664122.
ieee: Z. Zeng et al., “Lattice distortion leads to glassy thermal transport
in crystalline Cs3Bi2I6Cl3,” Proceedings of the National Academy of Sciences,
vol. 122, no. 41. National Academy of Sciences, p. e2415664122, 2025.
ista: Zeng Z, Fan Z, Simoncelli M, Chen C, Liang T, Chen Y, Thornton G, Cheng B.
2025. Lattice distortion leads to glassy thermal transport in crystalline Cs3Bi2I6Cl3.
Proceedings of the National Academy of Sciences. 122(41), e2415664122.
mla: Zeng, Zezhu, et al. “Lattice Distortion Leads to Glassy Thermal Transport in
Crystalline Cs3Bi2I6Cl3.” Proceedings of the National Academy of Sciences,
vol. 122, no. 41, National Academy of Sciences, 2025, p. e2415664122, doi:10.1073/pnas.2415664122.
short: Z. Zeng, Z. Fan, M. Simoncelli, C. Chen, T. Liang, Y. Chen, G. Thornton,
B. Cheng, Proceedings of the National Academy of Sciences 122 (2025) e2415664122.
corr_author: '1'
date_created: 2025-10-19T22:01:31Z
date_published: 2025-10-14T00:00:00Z
date_updated: 2026-02-16T12:32:11Z
day: '14'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1073/pnas.2415664122
ec_funded: 1
external_id:
isi:
- '001600415200001'
pmid:
- '41052324'
file:
- access_level: open_access
checksum: 3f9cd0d67ffe9110fb238407671584b7
content_type: application/pdf
creator: dernst
date_created: 2025-10-21T10:02:15Z
date_updated: 2025-10-21T10:02:15Z
file_id: '20513'
file_name: 2025_PNAS_Zeng.pdf
file_size: 12244843
relation: main_file
success: 1
file_date_updated: 2025-10-21T10:02:15Z
has_accepted_license: '1'
intvolume: ' 122'
isi: 1
issue: '41'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: e2415664122
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Proceedings of the National Academy of Sciences
publication_identifier:
eissn:
- 1091-6490
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ZengZezhu/Cs3Bi2I6Cl3_heat_conductivity
scopus_import: '1'
status: public
title: Lattice distortion leads to glassy thermal transport in crystalline Cs3Bi2I6Cl3
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 122
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
_id: '20702'
abstract:
- lang: eng
text: Qualitative and quantitative orbital properties such as bonding/antibonding
character, localization, and orbital energies are critical to how chemists understand
reactivity, catalysis, and excited-state behavior. Despite this, representations
of orbitals in deep learning models have been very underdeveloped relative to
representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art
equivariant deep learning architectures to the task of assigning global labels
to orbitals, namely energies characterizations, given the molecular coefficients
from Hartree–Fock or density functional theory. The architecture we have developed,
the Cartesian Equivariant Orbital Network (CEONET), shows how molecular orbital
coefficients are readily featurized as equivariant node features common to all
graph-based machine-learned potentials. We find that CEONET performs well at predicting
difficult quantitative labels such as the orbital energy and orbital entropy.
Furthermore, we find that the CEONET representation provides an intuitive latent
space for differentiating orbital character for the qualitative assignment of
e.g. bonding or antibonding character. In addition to providing a useful representation
for further integrating deep learning with electronic structure theory, we expect
CEONET to be useful for automatizing and interpreting the results of advanced
electronic structure methods such as complete active space self-consistent field
theory. In particular, the ability of CEONET to infer multireference character
via the orbital entropy paves the way toward the machine-learned selection of
active spaces.
acknowledgement: This work is supported as part of the Catalyst Design for Decarbonization
Center, an Energy Frontier Research Center funded by the U.S. Department of Energy,
Office of Science, Basic Energy Sciences under award no. DE-SC0023383. We thank
the Research Computing Center at the University of Chicago and for access to computational
resources. Additionally, this research used the Savio computational cluster resource
provided by the Berkeley Research Computing program at the University of California
(UC), Berkeley (supported by the UC Berkeley Chancellor, Vice Chancellor for Research,
and Chief Information Officer). Furthermore, we thank Matthew Hennefarth and Matt
Hermes for useful discussions.
article_number: e2510235122
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Daniel S.
full_name: King, Daniel S.
last_name: King
- first_name: Daniel
full_name: Grzenda, Daniel
last_name: Grzenda
- first_name: Ray
full_name: Zhu, Ray
last_name: Zhu
- first_name: Nathaniel
full_name: Hudson, Nathaniel
last_name: Hudson
- first_name: Ian
full_name: Foster, Ian
last_name: Foster
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Laura
full_name: Gagliardi, Laura
last_name: Gagliardi
citation:
ama: King DS, Grzenda D, Zhu R, et al. Cartesian equivariant representations for
learning and understanding molecular orbitals. Proceedings of the National
Academy of Sciences. 2025;122(48). doi:10.1073/pnas.2510235122
apa: King, D. S., Grzenda, D., Zhu, R., Hudson, N., Foster, I., Cheng, B., &
Gagliardi, L. (2025). Cartesian equivariant representations for learning and understanding
molecular orbitals. Proceedings of the National Academy of Sciences. National
Academy of Sciences. https://doi.org/10.1073/pnas.2510235122
chicago: King, Daniel S., Daniel Grzenda, Ray Zhu, Nathaniel Hudson, Ian Foster,
Bingqing Cheng, and Laura Gagliardi. “Cartesian Equivariant Representations for
Learning and Understanding Molecular Orbitals.” Proceedings of the National
Academy of Sciences. National Academy of Sciences, 2025. https://doi.org/10.1073/pnas.2510235122.
ieee: D. S. King et al., “Cartesian equivariant representations for learning
and understanding molecular orbitals,” Proceedings of the National Academy
of Sciences, vol. 122, no. 48. National Academy of Sciences, 2025.
ista: King DS, Grzenda D, Zhu R, Hudson N, Foster I, Cheng B, Gagliardi L. 2025.
Cartesian equivariant representations for learning and understanding molecular
orbitals. Proceedings of the National Academy of Sciences. 122(48), e2510235122.
mla: King, Daniel S., et al. “Cartesian Equivariant Representations for Learning
and Understanding Molecular Orbitals.” Proceedings of the National Academy
of Sciences, vol. 122, no. 48, e2510235122, National Academy of Sciences,
2025, doi:10.1073/pnas.2510235122.
short: D.S. King, D. Grzenda, R. Zhu, N. Hudson, I. Foster, B. Cheng, L. Gagliardi,
Proceedings of the National Academy of Sciences 122 (2025).
corr_author: '1'
date_created: 2025-11-30T23:02:06Z
date_published: 2025-12-02T00:00:00Z
date_updated: 2026-02-16T12:31:49Z
day: '02'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1073/pnas.2510235122
external_id:
pmid:
- '41269783'
file:
- access_level: open_access
checksum: 58051539a884c7a97306fd3afdb539ac
content_type: application/pdf
creator: dernst
date_created: 2025-12-01T08:41:32Z
date_updated: 2025-12-01T08:41:32Z
file_id: '20719'
file_name: 2025_PNAS_King.pdf
file_size: 27607870
relation: main_file
success: 1
file_date_updated: 2025-12-01T08:41:32Z
has_accepted_license: '1'
intvolume: ' 122'
issue: '48'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
pmid: 1
publication: Proceedings of the National Academy of Sciences
publication_identifier:
eissn:
- 1091-6490
publication_status: published
publisher: National Academy of Sciences
quality_controlled: '1'
related_material:
link:
- relation: software
url: 'https://github.com/GagliardiGroup/CEONet '
scopus_import: '1'
status: public
title: Cartesian equivariant representations for learning and understanding molecular
orbitals
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 122
year: '2025'
...
---
OA_type: closed access
_id: '20704'
abstract:
- lang: eng
text: Generative models have advanced significantly in sampling material systems
with continuous variables, such as atomistic structures. However, their application
to discrete variables, like atom types or spin states, remains underexplored.
In this work, we introduce a discrete flow matching model, tailored for systems
with discrete phase-space coordinates (e.g., the Ising model or a multicomponent
system on a lattice). This approach enables a single model to sample free energy
surfaces over a wide temperature range with minimal training overhead, and the
model generation is scalable to larger lattice sizes than those in the training
set. We demonstrate our approach on the 2D Ising model, showing efficient and
reliable free energy sampling. These results highlight the potential of flow matching
for low-cost, scalable free energy sampling in discrete systems and suggest promising
extensions to alchemical degrees of freedom in crystalline materials. The codebase
developed for this work is openly available at https://github.com/tuoping/alchemicalFES.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: P.T. acknowledges funding from FFG MAGNIFICO and the BIDMaP Postdoctoral
Fellowship. Z.Z. acknowledges funding from the European Union’s Horizon 2020 research
and innovation program under the Marie Skłodowska-Curie grant agreement No. 101034413.
The authors acknowledge the research computing facilities provided by the Institute
of Science and Technology Austria (ISTA), and resources of the National Energy Research
Scientific Computing Center (NERSC), a Department of Energy Office of Science User
Facility using NERSC award DOEERCAP0031751 ’GenAI@NERSC’. P.T. acknowledges valued
discussions with Dr. Daniel King, Dr. Lei Wang, and Dr. Fuzhi Dai.
article_processing_charge: No
article_type: original
author:
- first_name: Ping
full_name: Tuo, Ping
id: 6e5644c0-c180-11ed-a2da-facc4c9f4f09
last_name: Tuo
- first_name: Zezhu
full_name: Zeng, Zezhu
id: 54a2c730-803f-11ed-ab7e-95b29d2680e7
last_name: Zeng
orcid: 0000-0001-5126-4928
- first_name: Jiale
full_name: Chen, Jiale
id: 4d0a9064-1ff6-11ee-9fa6-ec046c604785
last_name: Chen
orcid: 0000-0001-5337-5875
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Tuo P, Zeng Z, Chen J, Cheng B. Scalable multitemperature free energy sampling
of classical Ising spin states. Journal of Chemical Theory and Computation.
2025;21(22):11427-11435. doi:10.1021/acs.jctc.5c01248
apa: Tuo, P., Zeng, Z., Chen, J., & Cheng, B. (2025). Scalable multitemperature
free energy sampling of classical Ising spin states. Journal of Chemical Theory
and Computation. American Chemical Society. https://doi.org/10.1021/acs.jctc.5c01248
chicago: Tuo, Ping, Zezhu Zeng, Jiale Chen, and Bingqing Cheng. “Scalable Multitemperature
Free Energy Sampling of Classical Ising Spin States.” Journal of Chemical Theory
and Computation. American Chemical Society, 2025. https://doi.org/10.1021/acs.jctc.5c01248.
ieee: P. Tuo, Z. Zeng, J. Chen, and B. Cheng, “Scalable multitemperature free energy
sampling of classical Ising spin states,” Journal of Chemical Theory and Computation,
vol. 21, no. 22. American Chemical Society, pp. 11427–11435, 2025.
ista: Tuo P, Zeng Z, Chen J, Cheng B. 2025. Scalable multitemperature free energy
sampling of classical Ising spin states. Journal of Chemical Theory and Computation.
21(22), 11427–11435.
mla: Tuo, Ping, et al. “Scalable Multitemperature Free Energy Sampling of Classical
Ising Spin States.” Journal of Chemical Theory and Computation, vol. 21,
no. 22, American Chemical Society, 2025, pp. 11427–35, doi:10.1021/acs.jctc.5c01248.
short: P. Tuo, Z. Zeng, J. Chen, B. Cheng, Journal of Chemical Theory and Computation
21 (2025) 11427–11435.
corr_author: '1'
date_created: 2025-11-30T23:02:06Z
date_published: 2025-10-31T00:00:00Z
date_updated: 2025-12-01T15:40:27Z
day: '31'
department:
- _id: BiCh
- _id: DaAl
doi: 10.1021/acs.jctc.5c01248
ec_funded: 1
external_id:
isi:
- '001605927900001'
pmid:
- '41172130'
intvolume: ' 21'
isi: 1
issue: '22'
language:
- iso: eng
month: '10'
oa_version: None
page: 11427-11435
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Journal of Chemical Theory and Computation
publication_identifier:
eissn:
- 1549-9626
issn:
- 1549-9618
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/tuoping/alchemicalFES
scopus_import: '1'
status: public
title: Scalable multitemperature free energy sampling of classical Ising spin states
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 21
year: '2025'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '18820'
abstract:
- lang: eng
text: 'Feature selection is essential in the analysis of molecular systems and many
other fields, but several uncertainties remain: What is the optimal number of
features for a simplified, interpretable model that retains essential information?
How should features with different units be aligned, and how should their relative
importance be weighted? Here, we introduce the Differentiable Information Imbalance
(DII), an automated method to rank information content between sets of features.
Using distances in a ground truth feature space, DII identifies a low-dimensional
subset of features that best preserves these relationships. Each feature is scaled
by a weight, which is optimized by minimizing the DII through gradient descent.
This allows simultaneously performing unit alignment and relative importance scaling,
while preserving interpretability. DII can also produce sparse solutions and determine
the optimal size of the reduced feature space. We demonstrate the usefulness of
this approach on two benchmark molecular problems: (1) identifying collective
variables that describe conformations of a biomolecule, and (2) selecting features
for training a machine-learning force field. These results show the potential
of DII in addressing feature selection challenges and optimizing dimensionality
in various applications. The method is available in the Python library DADApy.'
acknowledgement: The authors thank Dr. Matteo Carli for providing the CLN025 replica
exchange MD trajectory and Matteo Allione for the fruitful discussions connected
with the idea of the linear scaling estimator. This work was partially funded by
NextGenerationEU through the Italian National Centre for HPC, Big Data, and Quantum
Computing (Grant No. CN00000013 received by A.L.). A.L. also acknowledges financial
support by the region Friuli Venezia Giulia (project F53C22001770002 received by
A.L.).
article_number: '270'
article_processing_charge: Yes
article_type: original
author:
- first_name: Romina
full_name: Wild, Romina
last_name: Wild
- first_name: Felix
full_name: Wodaczek, Felix
id: 8b4b6a9f-32b0-11ee-9fa8-bbe85e26258e
last_name: Wodaczek
orcid: 0009-0000-1457-795X
- first_name: Vittorio
full_name: Del Tatto, Vittorio
last_name: Del Tatto
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Alessandro
full_name: Laio, Alessandro
last_name: Laio
citation:
ama: Wild R, Wodaczek F, Del Tatto V, Cheng B, Laio A. Automatic feature selection
and weighting in molecular systems using Differentiable Information Imbalance.
Nature Communications. 2025;16. doi:10.1038/s41467-024-55449-7
apa: Wild, R., Wodaczek, F., Del Tatto, V., Cheng, B., & Laio, A. (2025). Automatic
feature selection and weighting in molecular systems using Differentiable Information
Imbalance. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-024-55449-7
chicago: Wild, Romina, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng, and Alessandro
Laio. “Automatic Feature Selection and Weighting in Molecular Systems Using Differentiable
Information Imbalance.” Nature Communications. Springer Nature, 2025. https://doi.org/10.1038/s41467-024-55449-7.
ieee: R. Wild, F. Wodaczek, V. Del Tatto, B. Cheng, and A. Laio, “Automatic feature
selection and weighting in molecular systems using Differentiable Information
Imbalance,” Nature Communications, vol. 16. Springer Nature, 2025.
ista: Wild R, Wodaczek F, Del Tatto V, Cheng B, Laio A. 2025. Automatic feature
selection and weighting in molecular systems using Differentiable Information
Imbalance. Nature Communications. 16, 270.
mla: Wild, Romina, et al. “Automatic Feature Selection and Weighting in Molecular
Systems Using Differentiable Information Imbalance.” Nature Communications,
vol. 16, 270, Springer Nature, 2025, doi:10.1038/s41467-024-55449-7.
short: R. Wild, F. Wodaczek, V. Del Tatto, B. Cheng, A. Laio, Nature Communications
16 (2025).
date_created: 2025-01-12T23:04:00Z
date_published: 2025-01-02T00:00:00Z
date_updated: 2025-02-27T12:41:25Z
day: '02'
ddc:
- '570'
department:
- _id: AnSa
- _id: BiCh
doi: 10.1038/s41467-024-55449-7
external_id:
isi:
- '001389959100009'
pmid:
- '39747013'
file:
- access_level: open_access
checksum: b3d0f3568d9a87c494cf231a5324029a
content_type: application/pdf
creator: dernst
date_created: 2025-01-14T06:59:25Z
date_updated: 2025-01-14T06:59:25Z
file_id: '18846'
file_name: 2025_NatureComm_Wild.pdf
file_size: 1216738
relation: main_file
success: 1
file_date_updated: 2025-01-14T06:59:25Z
has_accepted_license: '1'
intvolume: ' 16'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Automatic feature selection and weighting in molecular systems using Differentiable
Information Imbalance
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 16
year: '2025'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '19495'
abstract:
- lang: eng
text: Machine learning interatomic potentials (MLIPs) often neglect long-range interactions,
such as electrostatic and dispersion forces. In this work, we introduce a straightforward
and efficient method to account for long-range interactions by learning a hidden
variable from local atomic descriptors and applying an Ewald summation to this
variable. We demonstrate that in systems including charged and polar molecular
dimers, bulk water, and water-vapor interface, standard short-ranged MLIPs can
lead to unphysical predictions even when employing message passing. The long-range
models effectively eliminate these artifacts, with only about twice the computational
cost of short-range MLIPs.
acknowledgement: B. C. thanks David Limmer for providing the water slab dataset, and
Carolin Faller for the NaCl dataset.
article_number: '80'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Cheng B. Latent Ewald summation for machine learning of long-range interactions.
npj Computational Materials. 2025;11. doi:10.1038/s41524-025-01577-7
apa: Cheng, B. (2025). Latent Ewald summation for machine learning of long-range
interactions. Npj Computational Materials. Springer Nature. https://doi.org/10.1038/s41524-025-01577-7
chicago: Cheng, Bingqing. “Latent Ewald Summation for Machine Learning of Long-Range
Interactions.” Npj Computational Materials. Springer Nature, 2025. https://doi.org/10.1038/s41524-025-01577-7.
ieee: B. Cheng, “Latent Ewald summation for machine learning of long-range interactions,”
npj Computational Materials, vol. 11. Springer Nature, 2025.
ista: Cheng B. 2025. Latent Ewald summation for machine learning of long-range interactions.
npj Computational Materials. 11, 80.
mla: Cheng, Bingqing. “Latent Ewald Summation for Machine Learning of Long-Range
Interactions.” Npj Computational Materials, vol. 11, 80, Springer Nature,
2025, doi:10.1038/s41524-025-01577-7.
short: B. Cheng, Npj Computational Materials 11 (2025).
corr_author: '1'
date_created: 2025-04-06T22:01:32Z
date_published: 2025-03-26T00:00:00Z
date_updated: 2025-09-30T11:31:47Z
day: '26'
ddc:
- '000'
department:
- _id: BiCh
doi: 10.1038/s41524-025-01577-7
external_id:
arxiv:
- '2408.15165'
isi:
- '001453622900002'
file:
- access_level: open_access
checksum: cc99b7407a12139d9b2d8457961935ae
content_type: application/pdf
creator: dernst
date_created: 2025-04-08T09:34:58Z
date_updated: 2025-04-08T09:34:58Z
file_id: '19528'
file_name: 2025_npjCompMaterials_Cheng.pdf
file_size: 1608315
relation: main_file
success: 1
file_date_updated: 2025-04-08T09:34:58Z
has_accepted_license: '1'
intvolume: ' 11'
isi: 1
language:
- iso: eng
month: '03'
oa: 1
oa_version: Published Version
publication: npj Computational Materials
publication_identifier:
eissn:
- 2057-3960
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Latent Ewald summation for machine learning of long-range interactions
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 11
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '20926'
abstract:
- lang: eng
text: Most current machine learning interatomic potentials (MLIPs) rely on short-range
approximations, without explicit treatment of long-range electrostatics. To address
this, we recently developed the Latent Ewald Summation (LES) method, which infers
electrostatic interactions, polarization, and Born effective charges (BECs), just
by learning from energy and force training data. Here, we present LES as a standalone
library, compatible with any short-range MLIP, and demonstrate its integration
with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark
LES-enhanced models on distinct systems, including bulk water, polar dipeptides,
and gold dimer adsorption on defective substrates, and show that LES not only
captures correct electrostatics but also improves accuracy. Additionally, we scale
LES to large and chemically diverse data by training MACELES-OFF on the SPICE
set containing molecules and clusters, making a universal MLIP with electrostatics
for organic systems, including biomolecules. MACELES-OFF is more accurate than
its short-range counterpart (MACE-OFF) trained on the same data set, predicts
dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling
efficient long-range electrostatics without directly training on electrical properties,
LES paves the way for electrostatic foundation MLIPs.
acknowledgement: Research reported in this publication was supported by the National
Institute Of General Medical Sciences of the National Institutes of Health under
Award Number R35GM159986. The content is solely the responsibility of the authors
and does not necessarily represent the official views of the National Institutes
of Health. D.K. and B.C. acknowledge funding from Toyota Research Institute Synthesis
Advanced Research Challenge. T.J.I., D.S.K. and P.Z. acknowledge funding from BIDMaP
Postdoctoral Fellowship. T.J.I. used resources of the National Energy Research Scientific
Computing Center (NERSC), a Department of Energy Office of Science User Facility
using NERSC award DOEERCAP0031751 ′GenAI@NERSC’. The authors thank Bowen Deng for
valuable discussions on MatGL implementation, and thank Gabor Csanyi for stimulating
discussions.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Dongjin
full_name: Kim, Dongjin
last_name: Kim
- first_name: Xiaoyu
full_name: Wang, Xiaoyu
id: 8dff9c62-32b0-11ee-9fa8-fc73025e10f3
last_name: Wang
- first_name: Santiago
full_name: Vargas, Santiago
last_name: Vargas
- first_name: Peichen
full_name: Zhong, Peichen
last_name: Zhong
- first_name: Daniel S.
full_name: King, Daniel S.
last_name: King
- first_name: Theo Jaffrelot
full_name: Inizan, Theo Jaffrelot
last_name: Inizan
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Kim D, Wang X, Vargas S, et al. A universal augmentation framework for long-range
electrostatics in machine learning interatomic potentials. Journal of Chemical
Theory and Computation. 2025;21(24):12709-12724. doi:10.1021/acs.jctc.5c01400
apa: Kim, D., Wang, X., Vargas, S., Zhong, P., King, D. S., Inizan, T. J., &
Cheng, B. (2025). A universal augmentation framework for long-range electrostatics
in machine learning interatomic potentials. Journal of Chemical Theory and
Computation. American Chemical Society. https://doi.org/10.1021/acs.jctc.5c01400
chicago: Kim, Dongjin, Xiaoyu Wang, Santiago Vargas, Peichen Zhong, Daniel S. King,
Theo Jaffrelot Inizan, and Bingqing Cheng. “A Universal Augmentation Framework
for Long-Range Electrostatics in Machine Learning Interatomic Potentials.” Journal
of Chemical Theory and Computation. American Chemical Society, 2025. https://doi.org/10.1021/acs.jctc.5c01400.
ieee: D. Kim et al., “A universal augmentation framework for long-range electrostatics
in machine learning interatomic potentials,” Journal of Chemical Theory and
Computation, vol. 21, no. 24. American Chemical Society, pp. 12709–12724,
2025.
ista: Kim D, Wang X, Vargas S, Zhong P, King DS, Inizan TJ, Cheng B. 2025. A universal
augmentation framework for long-range electrostatics in machine learning interatomic
potentials. Journal of Chemical Theory and Computation. 21(24), 12709–12724.
mla: Kim, Dongjin, et al. “A Universal Augmentation Framework for Long-Range Electrostatics
in Machine Learning Interatomic Potentials.” Journal of Chemical Theory and
Computation, vol. 21, no. 24, American Chemical Society, 2025, pp. 12709–24,
doi:10.1021/acs.jctc.5c01400.
short: D. Kim, X. Wang, S. Vargas, P. Zhong, D.S. King, T.J. Inizan, B. Cheng, Journal
of Chemical Theory and Computation 21 (2025) 12709–12724.
corr_author: '1'
date_created: 2026-01-04T23:01:33Z
date_published: 2025-12-10T00:00:00Z
date_updated: 2026-01-05T11:34:21Z
day: '10'
department:
- _id: GradSch
- _id: BiCh
doi: 10.1021/acs.jctc.5c01400
external_id:
arxiv:
- '2507.14302'
pmid:
- '41368735 '
intvolume: ' 21'
issue: '24'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2507.14302
month: '12'
oa: 1
oa_version: Preprint
page: 12709-12724
pmid: 1
publication: Journal of Chemical Theory and Computation
publication_identifier:
eissn:
- 1549-9626
issn:
- 1549-9618
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: A universal augmentation framework for long-range electrostatics in machine
learning interatomic potentials
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 21
year: '2025'
...
---
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '20990'
abstract:
- lang: eng
text: Modeling the response of material and chemical systems to electric fields
remains a longstanding challenge. Machine learning interatomic potentials (MLIPs)
offer an efficient and scalable alternative to quantum mechanical methods, but
do not by themselves incorporate electrical response. Here, we show that polarization
and Born effective charge (BEC) tensors can be directly extracted from long-range
MLIPs within the Latent Ewald Summation (LES) framework, solely by learning from
energy and force data. Using this approach, we predict the infrared spectra of
bulk water under zero or finite external electric fields, ionic conductivities
of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric
PbTiO3 perovskite. This work thus extends the capability of MLIPs to predict electrical
response –without training on charges or polarization or BECs– and enables accurate
modeling of electric-field-driven processes in diverse systems at scale.
acknowledgement: The authors thank for valuable discussions with Pinchen Xie, David
Limmer, Jeff Neaton, and Greg Voth. The authors thank Sebastien Hamel for providing
the DFT MD trajectories for superionic water, and help clarifying questions related
to the pseudopotentials. The authors thank Federico Grasselli and Stefano Baroni
for providing data and notebooks for computing the conductivity of a molten salt.
This research used the Savio computational cluster resource provided by the Berkeley
Research Computing program at the University of California, Berkeley (supported
by the UC Berkeley Chancellor, Vice Chancellor for Research, and Chief Information
Officer). D.S.K. and P.Z. acknowledge funding from the BIDMaP Postdoctoral Fellowship.
article_number: '384'
article_processing_charge: Yes
article_type: original
author:
- first_name: Peichen
full_name: Zhong, Peichen
last_name: Zhong
- first_name: Dongjin
full_name: Kim, Dongjin
last_name: Kim
- first_name: Daniel S.
full_name: King, Daniel S.
last_name: King
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Zhong P, Kim D, King DS, Cheng B. Machine learning interatomic potential can
infer electrical response. npj Computational Materials. 2025;11. doi:10.1038/s41524-025-01911-z
apa: Zhong, P., Kim, D., King, D. S., & Cheng, B. (2025). Machine learning interatomic
potential can infer electrical response. Npj Computational Materials. Springer
Nature. https://doi.org/10.1038/s41524-025-01911-z
chicago: Zhong, Peichen, Dongjin Kim, Daniel S. King, and Bingqing Cheng. “Machine
Learning Interatomic Potential Can Infer Electrical Response.” Npj Computational
Materials. Springer Nature, 2025. https://doi.org/10.1038/s41524-025-01911-z.
ieee: P. Zhong, D. Kim, D. S. King, and B. Cheng, “Machine learning interatomic
potential can infer electrical response,” npj Computational Materials,
vol. 11. Springer Nature, 2025.
ista: Zhong P, Kim D, King DS, Cheng B. 2025. Machine learning interatomic potential
can infer electrical response. npj Computational Materials. 11, 384.
mla: Zhong, Peichen, et al. “Machine Learning Interatomic Potential Can Infer Electrical
Response.” Npj Computational Materials, vol. 11, 384, Springer Nature,
2025, doi:10.1038/s41524-025-01911-z.
short: P. Zhong, D. Kim, D.S. King, B. Cheng, Npj Computational Materials 11 (2025).
corr_author: '1'
date_created: 2026-01-15T12:17:07Z
date_published: 2025-12-29T00:00:00Z
date_updated: 2026-01-20T07:23:34Z
day: '29'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1038/s41524-025-01911-z
file:
- access_level: open_access
checksum: cc999804ba3bfed809ae46c73869e4e3
content_type: application/pdf
creator: dernst
date_created: 2026-01-20T07:22:04Z
date_updated: 2026-01-20T07:22:04Z
file_id: '21005'
file_name: 2025_npj_Zhong.pdf
file_size: 2686255
relation: main_file
success: 1
file_date_updated: 2026-01-20T07:22:04Z
has_accepted_license: '1'
intvolume: ' 11'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
publication: npj Computational Materials
publication_identifier:
eissn:
- 2057-3960
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Machine learning interatomic potential can infer electrical response
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
_id: '18452'
abstract:
- lang: eng
text: 'Diffusion models have recently emerged as powerful tools for the generation
of new molecular and material structures. The key insight is that the noise in
these models is related to the response of the atoms to displacement, and the
denoising step is thus analogous to the geometry relaxation of atomistic systems
starting from a random structure. Building on this, we present a generative method
called Response Matching (RM), which leverages the fact that each stable material
or molecule exists at the minimum of its potential energy surface. Any perturbation
induces a response in energy and stress, driving the structure back to equilibrium.
Matching this response is closely related to score matching in diffusion models.
Another important aspect of state-of-the-art diffusion models is the incorporation
of physical symmetries such as translation, rotation, and periodicity. RM employs
a machine learning interatomic potential and random structure search as the denoising
model, inherently respecting these symmetries and exploiting the locality of atomic
interactions. RM handles both molecules and bulk materials under the same framework.
Its efficiency and generalization are demonstrated on three systems: a small organic
molecular data set, stable crystals from the Materials Project, and one-shot learning
on a single diamond configuration.'
acknowledgement: B.C. thanks Chris Pickard for enlightening discussions.
article_processing_charge: Yes (in subscription journal)
article_type: original
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Cheng B. Response matching for generating materials and molecules. Journal
of Chemical Theory and Computation. 2024;20(20):9259-9266. doi:10.1021/acs.jctc.4c00998
apa: Cheng, B. (2024). Response matching for generating materials and molecules.
Journal of Chemical Theory and Computation. American Chemical Society.
https://doi.org/10.1021/acs.jctc.4c00998
chicago: Cheng, Bingqing. “Response Matching for Generating Materials and Molecules.”
Journal of Chemical Theory and Computation. American Chemical Society,
2024. https://doi.org/10.1021/acs.jctc.4c00998.
ieee: B. Cheng, “Response matching for generating materials and molecules,” Journal
of Chemical Theory and Computation, vol. 20, no. 20. American Chemical Society,
pp. 9259–9266, 2024.
ista: Cheng B. 2024. Response matching for generating materials and molecules. Journal
of Chemical Theory and Computation. 20(20), 9259–9266.
mla: Cheng, Bingqing. “Response Matching for Generating Materials and Molecules.”
Journal of Chemical Theory and Computation, vol. 20, no. 20, American Chemical
Society, 2024, pp. 9259–66, doi:10.1021/acs.jctc.4c00998.
short: B. Cheng, Journal of Chemical Theory and Computation 20 (2024) 9259–9266.
corr_author: '1'
date_created: 2024-10-20T22:02:07Z
date_published: 2024-10-22T00:00:00Z
date_updated: 2025-09-08T14:21:30Z
day: '22'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1021/acs.jctc.4c00998
external_id:
arxiv:
- '2405.09057'
isi:
- '001330001500001'
pmid:
- '39365029'
file:
- access_level: open_access
checksum: aca0011bba4846140809b5af583daa9a
content_type: application/pdf
creator: dernst
date_created: 2025-01-13T09:11:09Z
date_updated: 2025-01-13T09:11:09Z
file_id: '18832'
file_name: 2024_JCTC_Cheng.pdf
file_size: 4758251
relation: main_file
success: 1
file_date_updated: 2025-01-13T09:11:09Z
has_accepted_license: '1'
intvolume: ' 20'
isi: 1
issue: '20'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 9259-9266
pmid: 1
publication: Journal of Chemical Theory and Computation
publication_identifier:
eissn:
- 1549-9626
issn:
- 1549-9618
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/BingqingCheng/cace
scopus_import: '1'
status: public
title: Response matching for generating materials and molecules
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 20
year: '2024'
...
---
OA_place: publisher
OA_type: hybrid
_id: '18952'
abstract:
- lang: eng
text: 'A seventh blind test of crystal structure prediction was organized by the
Cambridge Crystallographic Data Centre featuring seven target systems of varying
complexity: a silicon and iodine-containing molecule, a copper coordination complex,
a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly
flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this
first of two parts focusing on structure generation methods, many crystal structure
prediction (CSP) methods performed well for the small but flexible agrochemical
compound, successfully reproducing the experimentally observed crystal structures,
while few groups were successful for the systems of higher complexity. A powder
X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully
determining a crystal structure from a low-quality PXRD pattern. The use of CSP
in the prediction of likely cocrystal stoichiometry was also explored, demonstrating
multiple possible approaches. Crystallographic disorder emerged as an important
theme throughout the test as both a challenge for analysis and a major achievement
where two groups blindly predicted the existence of disorder for the first time.
Additionally, large-scale comparisons of the sets of predicted crystal structures
also showed that some methods yield sets that largely contain the same crystal
structures.'
acknowledgement: "The CCDC Blind Test Team. The CCDC organizers (L. M. Hunnisett,
J. Nyman, N. Francia, I. Sugden, G. Sadiq, and J. C. Cole) gratefully acknowledge
numerous CCDC colleagues for\r\ntheir helpful feedback and suggestions on the manuscript
(P. McCabe, E. Pidcock, P. Martinez-Bulit, C. Kingsbury), providing useful python
knowledge (A. Moldovan), providing and maintaining internal compute resources (K.
Taylor, M. Burling, J. Swift, L. Wallis), monitoring and depositing structures in
the CSD (S. Ward, K. Orzechowska, V. Menon), support in organization of the blind
test meeting (E. Clarke),and improvements to the Crystal Packing Similarity tool
(M.\r\nRead). Data analysis was performed using resources provided by the Cambridge
Service for Data Driven Discovery (CSD3) operated by the University of Cambridge
Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel
using Tier-2 funding from the Engineering and Physical Sciences Research Council
(capital grant EP/T022159/1), and DiRAC funding from the Science and Technology
Facilities Council (www.dirac.ac.uk). N. Francia thanks M. Salvalaglio for advice
on the metadynamics simulations and the University College London for providing
access to the Kathleen High Performance Computing Facility Kathleen@UCL) on which
simulations were performed. N. Francia also thanks V. Kurlin and D. E. Widdowson
for counselling on crystal structure similarity. I. Sugden and N. Francia participated
in the blind test as members of Groups 1 and 24, respectively. They were involved
in the analysis of the results.\r\nand in writing this paper only after all results
were made\r\navailable to participants."
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Lily M.
full_name: Hunnisett, Lily M.
last_name: Hunnisett
- first_name: Jonas
full_name: Nyman, Jonas
last_name: Nyman
- first_name: Nicholas
full_name: Francia, Nicholas
last_name: Francia
- first_name: Nathan S.
full_name: Abraham, Nathan S.
last_name: Abraham
- first_name: Claire S.
full_name: Adjiman, Claire S.
last_name: Adjiman
- first_name: Srinivasulu
full_name: Aitipamula, Srinivasulu
last_name: Aitipamula
- first_name: Tamador
full_name: Alkhidir, Tamador
last_name: Alkhidir
- first_name: Mubarak
full_name: Almehairbi, Mubarak
last_name: Almehairbi
- first_name: Andrea
full_name: Anelli, Andrea
last_name: Anelli
- first_name: Dylan M.
full_name: Anstine, Dylan M.
last_name: Anstine
- first_name: John E.
full_name: Anthony, John E.
last_name: Anthony
- first_name: Joseph E.
full_name: Arnold, Joseph E.
last_name: Arnold
- first_name: Faezeh
full_name: Bahrami, Faezeh
last_name: Bahrami
- first_name: Michael A.
full_name: Bellucci, Michael A.
last_name: Bellucci
- first_name: Rajni M.
full_name: Bhardwaj, Rajni M.
last_name: Bhardwaj
- first_name: Imanuel
full_name: Bier, Imanuel
last_name: Bier
- first_name: Joanna A.
full_name: Bis, Joanna A.
last_name: Bis
- first_name: A. Daniel
full_name: Boese, A. Daniel
last_name: Boese
- first_name: David H.
full_name: Bowskill, David H.
last_name: Bowskill
- first_name: James
full_name: Bramley, James
last_name: Bramley
- first_name: Jan Gerit
full_name: Brandenburg, Jan Gerit
last_name: Brandenburg
- first_name: Doris E.
full_name: Braun, Doris E.
last_name: Braun
- first_name: Patrick W. V.
full_name: Butler, Patrick W. V.
last_name: Butler
- first_name: Joseph
full_name: Cadden, Joseph
last_name: Cadden
- first_name: Stephen
full_name: Carino, Stephen
last_name: Carino
- first_name: Eric J.
full_name: Chan, Eric J.
last_name: Chan
- first_name: Chao
full_name: Chang, Chao
last_name: Chang
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Sarah M.
full_name: Clarke, Sarah M.
last_name: Clarke
- first_name: Simon J.
full_name: Coles, Simon J.
last_name: Coles
- first_name: Richard I.
full_name: Cooper, Richard I.
last_name: Cooper
- first_name: Ricky
full_name: Couch, Ricky
last_name: Couch
- first_name: Ramon
full_name: Cuadrado, Ramon
last_name: Cuadrado
- first_name: Tom
full_name: Darden, Tom
last_name: Darden
- first_name: Graeme M.
full_name: Day, Graeme M.
last_name: Day
- first_name: Hanno
full_name: Dietrich, Hanno
last_name: Dietrich
- first_name: Yiming
full_name: Ding, Yiming
last_name: Ding
- first_name: Antonio
full_name: DiPasquale, Antonio
last_name: DiPasquale
- first_name: Bhausaheb
full_name: Dhokale, Bhausaheb
last_name: Dhokale
- first_name: Bouke P.
full_name: van Eijck, Bouke P.
last_name: van Eijck
- first_name: Mark R. J.
full_name: Elsegood, Mark R. J.
last_name: Elsegood
- first_name: Dzmitry
full_name: Firaha, Dzmitry
last_name: Firaha
- first_name: Wenbo
full_name: Fu, Wenbo
last_name: Fu
- first_name: Kaori
full_name: Fukuzawa, Kaori
last_name: Fukuzawa
- first_name: Joseph
full_name: Glover, Joseph
last_name: Glover
- first_name: Hitoshi
full_name: Goto, Hitoshi
last_name: Goto
- first_name: Chandler
full_name: Greenwell, Chandler
last_name: Greenwell
- first_name: Rui
full_name: Guo, Rui
last_name: Guo
- first_name: Jürgen
full_name: Harter, Jürgen
last_name: Harter
- first_name: Julian
full_name: Helfferich, Julian
last_name: Helfferich
- first_name: Detlef W. M.
full_name: Hofmann, Detlef W. M.
last_name: Hofmann
- first_name: Johannes
full_name: Hoja, Johannes
last_name: Hoja
- first_name: John
full_name: Hone, John
last_name: Hone
- first_name: Richard
full_name: Hong, Richard
last_name: Hong
- first_name: Geoffrey
full_name: Hutchison, Geoffrey
last_name: Hutchison
- first_name: Yasuhiro
full_name: Ikabata, Yasuhiro
last_name: Ikabata
- first_name: Olexandr
full_name: Isayev, Olexandr
last_name: Isayev
- first_name: Ommair
full_name: Ishaque, Ommair
last_name: Ishaque
- first_name: Varsha
full_name: Jain, Varsha
last_name: Jain
- first_name: Yingdi
full_name: Jin, Yingdi
last_name: Jin
- first_name: Aling
full_name: Jing, Aling
last_name: Jing
- first_name: Erin R.
full_name: Johnson, Erin R.
last_name: Johnson
- first_name: Ian
full_name: Jones, Ian
last_name: Jones
- first_name: K. V. Jovan
full_name: Jose, K. V. Jovan
last_name: Jose
- first_name: Elena A.
full_name: Kabova, Elena A.
last_name: Kabova
- first_name: Adam
full_name: Keates, Adam
last_name: Keates
- first_name: Paul F.
full_name: Kelly, Paul F.
last_name: Kelly
- first_name: Dmitry
full_name: Khakimov, Dmitry
last_name: Khakimov
- first_name: Stefanos
full_name: Konstantinopoulos, Stefanos
last_name: Konstantinopoulos
- first_name: Liudmila N.
full_name: Kuleshova, Liudmila N.
last_name: Kuleshova
- first_name: He
full_name: Li, He
last_name: Li
- first_name: Xiaolu
full_name: Lin, Xiaolu
last_name: Lin
- first_name: Alexander
full_name: List, Alexander
last_name: List
- first_name: Congcong
full_name: Liu, Congcong
last_name: Liu
- first_name: Yifei Michelle
full_name: Liu, Yifei Michelle
last_name: Liu
- first_name: Zenghui
full_name: Liu, Zenghui
last_name: Liu
- first_name: Zhi-Pan
full_name: Liu, Zhi-Pan
last_name: Liu
- first_name: Joseph W.
full_name: Lubach, Joseph W.
last_name: Lubach
- first_name: Noa
full_name: Marom, Noa
last_name: Marom
- first_name: Alexander A.
full_name: Maryewski, Alexander A.
last_name: Maryewski
- first_name: Hiroyuki
full_name: Matsui, Hiroyuki
last_name: Matsui
- first_name: Alessandra
full_name: Mattei, Alessandra
last_name: Mattei
- first_name: R. Alex
full_name: Mayo, R. Alex
last_name: Mayo
- first_name: John W.
full_name: Melkumov, John W.
last_name: Melkumov
- first_name: Sharmarke
full_name: Mohamed, Sharmarke
last_name: Mohamed
- first_name: Zahrasadat
full_name: Momenzadeh Abardeh, Zahrasadat
last_name: Momenzadeh Abardeh
- first_name: Hari S.
full_name: Muddana, Hari S.
last_name: Muddana
- first_name: Naofumi
full_name: Nakayama, Naofumi
last_name: Nakayama
- first_name: Kamal Singh
full_name: Nayal, Kamal Singh
last_name: Nayal
- first_name: Marcus A.
full_name: Neumann, Marcus A.
last_name: Neumann
- first_name: Rahul
full_name: Nikhar, Rahul
last_name: Nikhar
- first_name: Shigeaki
full_name: Obata, Shigeaki
last_name: Obata
- first_name: Dana
full_name: O'Connor, Dana
last_name: O'Connor
- first_name: Artem R.
full_name: Oganov, Artem R.
last_name: Oganov
- first_name: Koji
full_name: Okuwaki, Koji
last_name: Okuwaki
- first_name: Alberto
full_name: Otero-de-la-Roza, Alberto
last_name: Otero-de-la-Roza
- first_name: Constantinos C.
full_name: Pantelides, Constantinos C.
last_name: Pantelides
- first_name: Sean
full_name: Parkin, Sean
last_name: Parkin
- first_name: Chris J.
full_name: Pickard, Chris J.
last_name: Pickard
- first_name: Luca
full_name: Pilia, Luca
last_name: Pilia
- first_name: Tatyana
full_name: Pivina, Tatyana
last_name: Pivina
- first_name: Rafał
full_name: Podeszwa, Rafał
last_name: Podeszwa
- first_name: Alastair J. A.
full_name: Price, Alastair J. A.
last_name: Price
- first_name: Louise S.
full_name: Price, Louise S.
last_name: Price
- first_name: Sarah L.
full_name: Price, Sarah L.
last_name: Price
- first_name: Michael R.
full_name: Probert, Michael R.
last_name: Probert
- first_name: Angeles
full_name: Pulido, Angeles
last_name: Pulido
- first_name: Gunjan Rajendra
full_name: Ramteke, Gunjan Rajendra
last_name: Ramteke
- first_name: Atta Ur
full_name: Rehman, Atta Ur
last_name: Rehman
- first_name: Susan M.
full_name: Reutzel-Edens, Susan M.
last_name: Reutzel-Edens
- first_name: Jutta
full_name: Rogal, Jutta
last_name: Rogal
- first_name: Marta J.
full_name: Ross, Marta J.
last_name: Ross
- first_name: Adrian F.
full_name: Rumson, Adrian F.
last_name: Rumson
- first_name: Ghazala
full_name: Sadiq, Ghazala
last_name: Sadiq
- first_name: Zeinab M.
full_name: Saeed, Zeinab M.
last_name: Saeed
- first_name: Alireza
full_name: Salimi, Alireza
last_name: Salimi
- first_name: Matteo
full_name: Salvalaglio, Matteo
last_name: Salvalaglio
- first_name: Leticia
full_name: Sanders de Almada, Leticia
last_name: Sanders de Almada
- first_name: Kiran
full_name: Sasikumar, Kiran
last_name: Sasikumar
- first_name: Sivakumar
full_name: Sekharan, Sivakumar
last_name: Sekharan
- first_name: Cheng
full_name: Shang, Cheng
last_name: Shang
- first_name: Kenneth
full_name: Shankland, Kenneth
last_name: Shankland
- first_name: Kotaro
full_name: Shinohara, Kotaro
last_name: Shinohara
- first_name: Baimei
full_name: Shi, Baimei
last_name: Shi
- first_name: Xuekun
full_name: Shi, Xuekun
last_name: Shi
- first_name: A. Geoffrey
full_name: Skillman, A. Geoffrey
last_name: Skillman
- first_name: Hongxing
full_name: Song, Hongxing
last_name: Song
- first_name: Nina
full_name: Strasser, Nina
last_name: Strasser
- first_name: Jacco
full_name: van de Streek, Jacco
last_name: van de Streek
- first_name: Isaac J.
full_name: Sugden, Isaac J.
last_name: Sugden
- first_name: Guangxu
full_name: Sun, Guangxu
last_name: Sun
- first_name: Krzysztof
full_name: Szalewicz, Krzysztof
last_name: Szalewicz
- first_name: Benjamin I.
full_name: Tan, Benjamin I.
last_name: Tan
- first_name: Lu
full_name: Tan, Lu
last_name: Tan
- first_name: Frank
full_name: Tarczynski, Frank
last_name: Tarczynski
- first_name: Christopher R.
full_name: Taylor, Christopher R.
last_name: Taylor
- first_name: Alexandre
full_name: Tkatchenko, Alexandre
last_name: Tkatchenko
- first_name: Rithwik
full_name: Tom, Rithwik
last_name: Tom
- first_name: Mark E.
full_name: Tuckerman, Mark E.
last_name: Tuckerman
- first_name: Yohei
full_name: Utsumi, Yohei
last_name: Utsumi
- first_name: Leslie
full_name: Vogt-Maranto, Leslie
last_name: Vogt-Maranto
- first_name: Jake
full_name: Weatherston, Jake
last_name: Weatherston
- first_name: Luke J.
full_name: Wilkinson, Luke J.
last_name: Wilkinson
- first_name: Robert D.
full_name: Willacy, Robert D.
last_name: Willacy
- first_name: Lukasz
full_name: Wojtas, Lukasz
last_name: Wojtas
- first_name: Grahame R.
full_name: Woollam, Grahame R.
last_name: Woollam
- first_name: Zhuocen
full_name: Yang, Zhuocen
last_name: Yang
- first_name: Etsuo
full_name: Yonemochi, Etsuo
last_name: Yonemochi
- first_name: Xin
full_name: Yue, Xin
last_name: Yue
- first_name: Qun
full_name: Zeng, Qun
last_name: Zeng
- first_name: Yizu
full_name: Zhang, Yizu
last_name: Zhang
- first_name: Tian
full_name: Zhou, Tian
last_name: Zhou
- first_name: Yunfei
full_name: Zhou, Yunfei
last_name: Zhou
- first_name: Roman
full_name: Zubatyuk, Roman
last_name: Zubatyuk
- first_name: Jason C.
full_name: Cole, Jason C.
last_name: Cole
citation:
ama: 'Hunnisett LM, Nyman J, Francia N, et al. The seventh blind test of crystal
structure prediction: Structure generation methods. Acta Crystallographica
Section B Structural Science, Crystal Engineering and Materials. 2024;80(6):517-547.
doi:10.1107/s2052520624007492'
apa: 'Hunnisett, L. M., Nyman, J., Francia, N., Abraham, N. S., Adjiman, C. S.,
Aitipamula, S., … Cole, J. C. (2024). The seventh blind test of crystal structure
prediction: Structure generation methods. Acta Crystallographica Section B
Structural Science, Crystal Engineering and Materials. International Union
of Crystallography. https://doi.org/10.1107/s2052520624007492'
chicago: 'Hunnisett, Lily M., Jonas Nyman, Nicholas Francia, Nathan S. Abraham,
Claire S. Adjiman, Srinivasulu Aitipamula, Tamador Alkhidir, et al. “The Seventh
Blind Test of Crystal Structure Prediction: Structure Generation Methods.” Acta
Crystallographica Section B Structural Science, Crystal Engineering and Materials.
International Union of Crystallography, 2024. https://doi.org/10.1107/s2052520624007492.'
ieee: 'L. M. Hunnisett et al., “The seventh blind test of crystal structure
prediction: Structure generation methods,” Acta Crystallographica Section B
Structural Science, Crystal Engineering and Materials, vol. 80, no. 6. International
Union of Crystallography, pp. 517–547, 2024.'
ista: 'Hunnisett LM et al. 2024. The seventh blind test of crystal structure prediction:
Structure generation methods. Acta Crystallographica Section B Structural Science,
Crystal Engineering and Materials. 80(6), 517–547.'
mla: 'Hunnisett, Lily M., et al. “The Seventh Blind Test of Crystal Structure Prediction:
Structure Generation Methods.” Acta Crystallographica Section B Structural
Science, Crystal Engineering and Materials, vol. 80, no. 6, International
Union of Crystallography, 2024, pp. 517–47, doi:10.1107/s2052520624007492.'
short: L.M. Hunnisett, J. Nyman, N. Francia, N.S. Abraham, C.S. Adjiman, S. Aitipamula,
T. Alkhidir, M. Almehairbi, A. Anelli, D.M. Anstine, J.E. Anthony, J.E. Arnold,
F. Bahrami, M.A. Bellucci, R.M. Bhardwaj, I. Bier, J.A. Bis, A.D. Boese, D.H.
Bowskill, J. Bramley, J.G. Brandenburg, D.E. Braun, P.W.V. Butler, J. Cadden,
S. Carino, E.J. Chan, C. Chang, B. Cheng, S.M. Clarke, S.J. Coles, R.I. Cooper,
R. Couch, R. Cuadrado, T. Darden, G.M. Day, H. Dietrich, Y. Ding, A. DiPasquale,
B. Dhokale, B.P. van Eijck, M.R.J. Elsegood, D. Firaha, W. Fu, K. Fukuzawa, J.
Glover, H. Goto, C. Greenwell, R. Guo, J. Harter, J. Helfferich, D.W.M. Hofmann,
J. Hoja, J. Hone, R. Hong, G. Hutchison, Y. Ikabata, O. Isayev, O. Ishaque, V.
Jain, Y. Jin, A. Jing, E.R. Johnson, I. Jones, K.V.J. Jose, E.A. Kabova, A. Keates,
P.F. Kelly, D. Khakimov, S. Konstantinopoulos, L.N. Kuleshova, H. Li, X. Lin,
A. List, C. Liu, Y.M. Liu, Z. Liu, Z.-P. Liu, J.W. Lubach, N. Marom, A.A. Maryewski,
H. Matsui, A. Mattei, R.A. Mayo, J.W. Melkumov, S. Mohamed, Z. Momenzadeh Abardeh,
H.S. Muddana, N. Nakayama, K.S. Nayal, M.A. Neumann, R. Nikhar, S. Obata, D. O’Connor,
A.R. Oganov, K. Okuwaki, A. Otero-de-la-Roza, C.C. Pantelides, S. Parkin, C.J.
Pickard, L. Pilia, T. Pivina, R. Podeszwa, A.J.A. Price, L.S. Price, S.L. Price,
M.R. Probert, A. Pulido, G.R. Ramteke, A.U. Rehman, S.M. Reutzel-Edens, J. Rogal,
M.J. Ross, A.F. Rumson, G. Sadiq, Z.M. Saeed, A. Salimi, M. Salvalaglio, L. Sanders
de Almada, K. Sasikumar, S. Sekharan, C. Shang, K. Shankland, K. Shinohara, B.
Shi, X. Shi, A.G. Skillman, H. Song, N. Strasser, J. van de Streek, I.J. Sugden,
G. Sun, K. Szalewicz, B.I. Tan, L. Tan, F. Tarczynski, C.R. Taylor, A. Tkatchenko,
R. Tom, M.E. Tuckerman, Y. Utsumi, L. Vogt-Maranto, J. Weatherston, L.J. Wilkinson,
R.D. Willacy, L. Wojtas, G.R. Woollam, Z. Yang, E. Yonemochi, X. Yue, Q. Zeng,
Y. Zhang, T. Zhou, Y. Zhou, R. Zubatyuk, J.C. Cole, Acta Crystallographica Section
B Structural Science, Crystal Engineering and Materials 80 (2024) 517–547.
date_created: 2025-01-29T11:07:36Z
date_published: 2024-12-01T00:00:00Z
date_updated: 2025-09-09T12:12:31Z
day: '01'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1107/s2052520624007492
external_id:
isi:
- '001388840500003'
pmid:
- '39405196'
file:
- access_level: open_access
checksum: 33b8083e76564cc918182b0b0b2cc023
content_type: application/pdf
creator: dernst
date_created: 2025-01-29T11:09:48Z
date_updated: 2025-01-29T11:09:48Z
file_id: '18954'
file_name: 2024_ActaCrystallographicaB_Hunnisett.pdf
file_size: 10061037
relation: main_file
success: 1
file_date_updated: 2025-01-29T11:09:48Z
has_accepted_license: '1'
intvolume: ' 80'
isi: 1
issue: '6'
language:
- iso: eng
month: '12'
oa: 1
oa_version: Published Version
page: 517-547
pmid: 1
publication: Acta Crystallographica Section B Structural Science, Crystal Engineering
and Materials
publication_identifier:
issn:
- 2052-5206
publication_status: published
publisher: International Union of Crystallography
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'The seventh blind test of crystal structure prediction: Structure generation
methods'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 80
year: '2024'
...
---
_id: '17278'
abstract:
- lang: eng
text: An azeotrope is a constant boiling point mixture, and its behavior is important
for fluid separation processes. Predicting azeotropes from atomistic simulations
is difficult due to the complexities and convergence problems of Monte Carlo and
free-energy perturbation techniques. Here, we present a methodology for predicting
the azeotropes of binary mixtures, which computes the compositional dependence
of chemical potentials from molecular dynamics simulations using the S0 method
and employs experimental boiling point and vaporization enthalpy data. Using this
methodology, we reproduce the azeotropes, or lack thereof, in five case studies,
including ethanol/water, ethanol/isooctane, methanol/water, hydrazine/water, and
acetone/chloroform mixtures. We find that it is crucial to use the experimental
boiling point and vaporization enthalpy for reliable azeotrope predictions, as
empirical force fields are not accurate enough for these quantities. Finally,
we use regular solution models to rationalize the azeotropes and reveal that they
tend to form when the mixture components have similar boiling points and strong
interactions.
acknowledgement: B.C. thanks Alessandro Laio, who introduced the phenomenon of azeotrope
and suggested using the S0 method to compute it. B.C. and X.W. thank Felix Wodaczek
for the insightful comments and suggestions on the manuscript. B.C. and X.W. acknowledge
the resources provided by the Cambridge Tier-2 system operated by the University
of Cambridge Research Computing Service, funded by EPSRC Tier-2 capital (Grant No.
EP/P020259/1).
article_number: '034111'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Xiaoyu
full_name: Wang, Xiaoyu
id: 8dff9c62-32b0-11ee-9fa8-fc73025e10f3
last_name: Wang
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Wang X, Cheng B. Integrating molecular dynamics simulations and experimental
data for azeotrope predictions in binary mixtures. Journal of Chemical Physics.
2024;161(3). doi:10.1063/5.0217232
apa: Wang, X., & Cheng, B. (2024). Integrating molecular dynamics simulations
and experimental data for azeotrope predictions in binary mixtures. Journal
of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0217232
chicago: Wang, Xiaoyu, and Bingqing Cheng. “Integrating Molecular Dynamics Simulations
and Experimental Data for Azeotrope Predictions in Binary Mixtures.” Journal
of Chemical Physics. AIP Publishing, 2024. https://doi.org/10.1063/5.0217232.
ieee: X. Wang and B. Cheng, “Integrating molecular dynamics simulations and experimental
data for azeotrope predictions in binary mixtures,” Journal of Chemical Physics,
vol. 161, no. 3. AIP Publishing, 2024.
ista: Wang X, Cheng B. 2024. Integrating molecular dynamics simulations and experimental
data for azeotrope predictions in binary mixtures. Journal of Chemical Physics.
161(3), 034111.
mla: Wang, Xiaoyu, and Bingqing Cheng. “Integrating Molecular Dynamics Simulations
and Experimental Data for Azeotrope Predictions in Binary Mixtures.” Journal
of Chemical Physics, vol. 161, no. 3, 034111, AIP Publishing, 2024, doi:10.1063/5.0217232.
short: X. Wang, B. Cheng, Journal of Chemical Physics 161 (2024).
corr_author: '1'
date_created: 2024-07-21T22:01:00Z
date_published: 2024-07-14T00:00:00Z
date_updated: 2025-09-08T08:26:09Z
day: '14'
department:
- _id: BiCh
- _id: GradSch
doi: 10.1063/5.0217232
external_id:
arxiv:
- '2405.02216'
isi:
- '001281819100016'
pmid:
- '39007379'
intvolume: ' 161'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2405.02216
month: '07'
oa: 1
oa_version: Preprint
pmid: 1
publication: Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/Xiaoyu-Wang-Stone/Azeotrope_S0
scopus_import: '1'
status: public
title: Integrating molecular dynamics simulations and experimental data for azeotrope
predictions in binary mixtures
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 161
year: '2024'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '17322'
abstract:
- lang: eng
text: Machine learning interatomic potentials are revolutionizing large-scale, accurate
atomistic modeling in material science and chemistry. Many potentials use atomic
cluster expansion or equivariant message-passing frameworks. Such frameworks typically
use spherical harmonics as angular basis functions, followed by Clebsch-Gordan
contraction to maintain rotational symmetry. We propose a mathematically equivalent
and simple alternative that performs all operations in the Cartesian coordinates.
This approach provides a complete set of polynormially independent features of
atomic environments while maintaining interaction body orders. Additionally, we
integrate low-dimensional embeddings of various chemical elements, trainable radial
channel coupling, and inter-atomic message passing. The resulting potential, named
Cartesian Atomic Cluster Expansion (CACE), exhibits good accuracy, stability,
and generalizability. We validate its performance in diverse systems, including
bulk water, small molecules, and 25-element high-entropy alloys.
acknowledgement: B.C. thanks Ralf Drautz and Ngoc Cuong Nguyen for illuminating discussions.
article_number: '157'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Cheng B. Cartesian atomic cluster expansion for machine learning interatomic
potentials. npj Computational Materials. 2024;10. doi:10.1038/s41524-024-01332-4
apa: Cheng, B. (2024). Cartesian atomic cluster expansion for machine learning interatomic
potentials. Npj Computational Materials. Springer Nature. https://doi.org/10.1038/s41524-024-01332-4
chicago: Cheng, Bingqing. “Cartesian Atomic Cluster Expansion for Machine Learning
Interatomic Potentials.” Npj Computational Materials. Springer Nature,
2024. https://doi.org/10.1038/s41524-024-01332-4.
ieee: B. Cheng, “Cartesian atomic cluster expansion for machine learning interatomic
potentials,” npj Computational Materials, vol. 10. Springer Nature, 2024.
ista: Cheng B. 2024. Cartesian atomic cluster expansion for machine learning interatomic
potentials. npj Computational Materials. 10, 157.
mla: Cheng, Bingqing. “Cartesian Atomic Cluster Expansion for Machine Learning Interatomic
Potentials.” Npj Computational Materials, vol. 10, 157, Springer Nature,
2024, doi:10.1038/s41524-024-01332-4.
short: B. Cheng, Npj Computational Materials 10 (2024).
corr_author: '1'
date_created: 2024-07-28T22:01:08Z
date_published: 2024-07-18T00:00:00Z
date_updated: 2025-09-08T08:43:34Z
day: '18'
ddc:
- '000'
department:
- _id: BiCh
doi: 10.1038/s41524-024-01332-4
external_id:
arxiv:
- '2402.07472'
isi:
- '001271730700001'
file:
- access_level: open_access
checksum: e6b4d1a45a9ef1e9be35b313d96ebd6f
content_type: application/pdf
creator: dernst
date_created: 2025-01-09T12:36:48Z
date_updated: 2025-01-09T12:36:48Z
file_id: '18813'
file_name: 2024_npjComputationalMaterials_Cheng.pdf
file_size: 1659509
relation: main_file
success: 1
file_date_updated: 2025-01-09T12:36:48Z
has_accepted_license: '1'
intvolume: ' 10'
isi: 1
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
publication: npj Computational Materials
publication_identifier:
eissn:
- 2057-3960
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Cartesian atomic cluster expansion for machine learning interatomic potentials
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 10
year: '2024'
...
---
_id: '14425'
abstract:
- lang: eng
text: 'Water adsorption and dissociation processes on pristine low-index TiO2 interfaces
are important but poorly understood outside the well-studied anatase (101) and
rutile (110). To understand these, we construct three sets of machine learning
potentials that are simultaneously applicable to various TiO2 surfaces, based
on three density-functional-theory approximations. Here we show the water dissociation
free energies on seven pristine TiO2 surfaces, and predict that anatase (100),
anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase
(101) and rutile (100) have mostly molecular adsorption, while the simulations
of rutile (110) sensitively depend on the slab thickness and molecular adsorption
is preferred with thick slabs. Moreover, using an automated algorithm, we reveal
that these surfaces follow different types of atomistic mechanisms for proton
transfer and water dissociation: one-step, two-step, or both. These mechanisms
can be rationalized based on the arrangements of water molecules on the different
surfaces. Our finding thus demonstrates that the different pristine TiO2 surfaces
react with water in distinct ways, and cannot be represented using just the low-energy
anatase (101) and rutile (110) surfaces.'
acknowledgement: F.S., J.H., and B.C. thank the Swiss National Supercomputing Centre
(CSCS) for the generous allocation of CPU hours via production project s1108 at
the Piz Daint supercomputer. B.C. acknowledges resources provided by the Cambridge
Tier-2 system operated by the University of Cambridge Research Computing Service
funded by EPSRC Tier-2 capital grant EP/P020259/1. J.C. acknowledges the Beijing
Natural Science Foundation for support under grant No. JQ22001. F.S., and J.H. thank
the Swiss Platform for Advanced Scientific Computing (PASC) via the 2021-2024 “Ab
Initio Molecular Dynamics at the Exa-Scale” project. This project has received funding
from the European Union’s Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie grant agreement No 101034413.
article_number: '6131'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Zezhu
full_name: Zeng, Zezhu
id: 54a2c730-803f-11ed-ab7e-95b29d2680e7
last_name: Zeng
- first_name: Felix
full_name: Wodaczek, Felix
id: 8b4b6a9f-32b0-11ee-9fa8-bbe85e26258e
last_name: Wodaczek
orcid: 0009-0000-1457-795X
- first_name: Keyang
full_name: Liu, Keyang
last_name: Liu
- first_name: Frederick
full_name: Stein, Frederick
last_name: Stein
- first_name: Jürg
full_name: Hutter, Jürg
last_name: Hutter
- first_name: Ji
full_name: Chen, Ji
last_name: Chen
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Zeng Z, Wodaczek F, Liu K, et al. Mechanistic insight on water dissociation
on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations.
Nature Communications. 2023;14. doi:10.1038/s41467-023-41865-8
apa: Zeng, Z., Wodaczek, F., Liu, K., Stein, F., Hutter, J., Chen, J., & Cheng,
B. (2023). Mechanistic insight on water dissociation on pristine low-index TiO2
surfaces from machine learning molecular dynamics simulations. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-023-41865-8
chicago: Zeng, Zezhu, Felix Wodaczek, Keyang Liu, Frederick Stein, Jürg Hutter,
Ji Chen, and Bingqing Cheng. “Mechanistic Insight on Water Dissociation on Pristine
Low-Index TiO2 Surfaces from Machine Learning Molecular Dynamics Simulations.”
Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-41865-8.
ieee: Z. Zeng et al., “Mechanistic insight on water dissociation on pristine
low-index TiO2 surfaces from machine learning molecular dynamics simulations,”
Nature Communications, vol. 14. Springer Nature, 2023.
ista: Zeng Z, Wodaczek F, Liu K, Stein F, Hutter J, Chen J, Cheng B. 2023. Mechanistic
insight on water dissociation on pristine low-index TiO2 surfaces from machine
learning molecular dynamics simulations. Nature Communications. 14, 6131.
mla: Zeng, Zezhu, et al. “Mechanistic Insight on Water Dissociation on Pristine
Low-Index TiO2 Surfaces from Machine Learning Molecular Dynamics Simulations.”
Nature Communications, vol. 14, 6131, Springer Nature, 2023, doi:10.1038/s41467-023-41865-8.
short: Z. Zeng, F. Wodaczek, K. Liu, F. Stein, J. Hutter, J. Chen, B. Cheng, Nature
Communications 14 (2023).
corr_author: '1'
date_created: 2023-10-15T22:01:10Z
date_published: 2023-10-02T00:00:00Z
date_updated: 2025-04-14T07:54:53Z
day: '02'
ddc:
- '540'
- '000'
department:
- _id: BiCh
- _id: GradSch
doi: 10.1038/s41467-023-41865-8
ec_funded: 1
external_id:
arxiv:
- '2303.07433'
isi:
- '001084354900008'
pmid:
- '37783698'
file:
- access_level: open_access
checksum: 7d1dffd36b672ec679f08f70ce79da87
content_type: application/pdf
creator: dernst
date_created: 2023-10-16T07:34:49Z
date_updated: 2023-10-16T07:34:49Z
file_id: '14432'
file_name: 2023_NatureComm_Zeng.pdf
file_size: 3194116
relation: main_file
success: 1
file_date_updated: 2023-10-16T07:34:49Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/BingqingCheng/TiO2-water
scopus_import: '1'
status: public
title: Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces
from machine learning molecular dynamics simulations
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14
year: '2023'
...
---
_id: '14603'
abstract:
- lang: eng
text: Computing the solubility of crystals in a solvent using atomistic simulations
is notoriously challenging due to the complexities and convergence issues associated
with free-energy methods, as well as the slow equilibration in direct-coexistence
simulations. This paper introduces a molecular-dynamics workflow that simplifies
and robustly computes the solubility of molecular or ionic crystals. This method
is considerably more straightforward than the state-of-the-art, as we have streamlined
and optimised each step of the process. Specifically, we calculate the chemical
potential of the crystal using the gas-phase molecule as a reference state, and
employ the S0 method to determine the concentration dependence of the chemical
potential of the solute. We use this workflow to predict the solubilities of sodium
chloride in water, urea polymorphs in water, and paracetamol polymorphs in both
water and ethanol. Our findings indicate that the predicted solubility is sensitive
to the chosen potential energy surface. Furthermore, we note that the harmonic
approximation often fails for both molecular crystals and gas molecules at or
above room temperature, and that the assumption of an ideal solution becomes less
valid for highly soluble substances.
acknowledgement: A.R. and B.C. acknowledge resources provided by the Cambridge Tier-2
system operated by the University of Cambridge Research Computing Service funded
by EPSRC Tier-2 capital Grant No. EP/P020259/1. P.Y.C. acknowledges support from
the Ernest Oppenheimer Fund and the Winton Programme for the Physics of Sustainability.
article_number: '184110'
article_processing_charge: Yes (in subscription journal)
article_type: original
arxiv: 1
author:
- first_name: Aleks
full_name: Reinhardt, Aleks
last_name: Reinhardt
- first_name: Pin Yu
full_name: Chew, Pin Yu
last_name: Chew
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Reinhardt A, Chew PY, Cheng B. A streamlined molecular-dynamics workflow for
computing solubilities of molecular and ionic crystals. Journal of Chemical
Physics. 2023;159(18). doi:10.1063/5.0173341
apa: Reinhardt, A., Chew, P. Y., & Cheng, B. (2023). A streamlined molecular-dynamics
workflow for computing solubilities of molecular and ionic crystals. Journal
of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0173341
chicago: Reinhardt, Aleks, Pin Yu Chew, and Bingqing Cheng. “A Streamlined Molecular-Dynamics
Workflow for Computing Solubilities of Molecular and Ionic Crystals.” Journal
of Chemical Physics. AIP Publishing, 2023. https://doi.org/10.1063/5.0173341.
ieee: A. Reinhardt, P. Y. Chew, and B. Cheng, “A streamlined molecular-dynamics
workflow for computing solubilities of molecular and ionic crystals,” Journal
of Chemical Physics, vol. 159, no. 18. AIP Publishing, 2023.
ista: Reinhardt A, Chew PY, Cheng B. 2023. A streamlined molecular-dynamics workflow
for computing solubilities of molecular and ionic crystals. Journal of Chemical
Physics. 159(18), 184110.
mla: Reinhardt, Aleks, et al. “A Streamlined Molecular-Dynamics Workflow for Computing
Solubilities of Molecular and Ionic Crystals.” Journal of Chemical Physics,
vol. 159, no. 18, 184110, AIP Publishing, 2023, doi:10.1063/5.0173341.
short: A. Reinhardt, P.Y. Chew, B. Cheng, Journal of Chemical Physics 159 (2023).
corr_author: '1'
date_created: 2023-11-26T23:00:54Z
date_published: 2023-11-14T00:00:00Z
date_updated: 2025-09-09T13:32:46Z
day: '14'
ddc:
- '530'
- '540'
department:
- _id: BiCh
doi: 10.1063/5.0173341
external_id:
arxiv:
- '2308.10886'
isi:
- '001137066700001'
pmid:
- '37962445'
file:
- access_level: open_access
checksum: f668ee0d07096eef81159d05bc27aabc
content_type: application/pdf
creator: dernst
date_created: 2023-11-28T08:39:06Z
date_updated: 2023-11-28T08:39:06Z
file_id: '14620'
file_name: 2023_JourChemicalPhysics_Reinhardt.pdf
file_size: 6276059
relation: main_file
success: 1
file_date_updated: 2023-11-28T08:39:06Z
has_accepted_license: '1'
intvolume: ' 159'
isi: 1
issue: '18'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
pmid: 1
publication: Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
record:
- id: '14619'
relation: research_data
status: public
scopus_import: '1'
status: public
title: A streamlined molecular-dynamics workflow for computing solubilities of molecular
and ionic crystals
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 159
year: '2023'
...
---
_id: '14619'
abstract:
- lang: eng
text: Data underlying the publication "A streamlined molecular-dynamics workflow
for computing solubilities of molecular and ionic crystals" (DOI https://doi.org/10.1063/5.0173341).
article_processing_charge: No
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 'Cheng B. BingqingCheng/solubility: V1.0. 2023. doi:10.5281/ZENODO.8398094'
apa: 'Cheng, B. (2023). BingqingCheng/solubility: V1.0. Zenodo. https://doi.org/10.5281/ZENODO.8398094'
chicago: 'Cheng, Bingqing. “BingqingCheng/Solubility: V1.0.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.8398094.'
ieee: 'B. Cheng, “BingqingCheng/solubility: V1.0.” Zenodo, 2023.'
ista: 'Cheng B. 2023. BingqingCheng/solubility: V1.0, Zenodo, 10.5281/ZENODO.8398094.'
mla: 'Cheng, Bingqing. BingqingCheng/Solubility: V1.0. Zenodo, 2023, doi:10.5281/ZENODO.8398094.'
short: B. Cheng, (2023).
corr_author: '1'
date_created: 2023-11-28T08:32:18Z
date_published: 2023-10-02T00:00:00Z
date_updated: 2025-09-09T13:32:46Z
day: '02'
ddc:
- '530'
department:
- _id: BiCh
doi: 10.5281/ZENODO.8398094
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.8398094
month: '10'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '14603'
relation: used_in_publication
status: public
status: public
title: 'BingqingCheng/solubility: V1.0'
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '12702'
abstract:
- lang: eng
text: Hydrocarbon mixtures are extremely abundant in the Universe, and diamond formation
from them can play a crucial role in shaping the interior structure and evolution
of planets. With first-principles accuracy, we first estimate the melting line
of diamond, and then reveal the nature of chemical bonding in hydrocarbons at
extreme conditions. We finally establish the pressure-temperature phase boundary
where it is thermodynamically possible for diamond to form from hydrocarbon mixtures
with different atomic fractions of carbon. Notably, here we show a depletion zone
at pressures above 200 GPa and temperatures below 3000 K-3500 K where diamond
formation is thermodynamically favorable regardless of the carbon atomic fraction,
due to a phase separation mechanism. The cooler condition of the interior of Neptune
compared to Uranus means that the former is much more likely to contain the depletion
zone. Our findings can help explain the dichotomy of the two ice giants manifested
by the low luminosity of Uranus, and lead to a better understanding of (exo-)planetary
formation and evolution.
acknowledgement: BC thanks Daan Frenkel for stimulating discussions. We thank Aleks
Reinhardt, Daan Frenkel, Marius Millot, Federica Coppari, Rhys Bunting, and Chris
J. Pickard for critically reading the manuscript and providing useful suggestions.
BC acknowledges resources provided by the Cambridge Tier-2 system operated by the
University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital
grant EP/P020259/1. SH acknowledges support from LDRD 19-ERD-031 and computing support
from the Lawrence Livermore National Laboratory (LLNL) Institutional Computing Grand
Challenge program. Lawrence Livermore National Laboratory is operated by Lawrence
Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear
Security Administration under Contract DE-AC52-07NA27344. MB acknowledges support
by the European Horizon 2020 program within the Marie Skłodowska-Curie actions (xICE
grant number 894725), funding from the NOMIS foundation and computational resources
at the North-German Supercomputing Alliance (HLRN) facilities.
article_number: '1104'
article_processing_charge: No
article_type: original
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Sebastien
full_name: Hamel, Sebastien
last_name: Hamel
- first_name: Mandy
full_name: Bethkenhagen, Mandy
id: 201939f4-803f-11ed-ab7e-d8da4bd1517f
last_name: Bethkenhagen
orcid: 0000-0002-1838-2129
citation:
ama: Cheng B, Hamel S, Bethkenhagen M. Thermodynamics of diamond formation from
hydrocarbon mixtures in planets. Nature Communications. 2023;14. doi:10.1038/s41467-023-36841-1
apa: Cheng, B., Hamel, S., & Bethkenhagen, M. (2023). Thermodynamics of diamond
formation from hydrocarbon mixtures in planets. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-023-36841-1
chicago: Cheng, Bingqing, Sebastien Hamel, and Mandy Bethkenhagen. “Thermodynamics
of Diamond Formation from Hydrocarbon Mixtures in Planets.” Nature Communications.
Springer Nature, 2023. https://doi.org/10.1038/s41467-023-36841-1.
ieee: B. Cheng, S. Hamel, and M. Bethkenhagen, “Thermodynamics of diamond formation
from hydrocarbon mixtures in planets,” Nature Communications, vol. 14.
Springer Nature, 2023.
ista: Cheng B, Hamel S, Bethkenhagen M. 2023. Thermodynamics of diamond formation
from hydrocarbon mixtures in planets. Nature Communications. 14, 1104.
mla: Cheng, Bingqing, et al. “Thermodynamics of Diamond Formation from Hydrocarbon
Mixtures in Planets.” Nature Communications, vol. 14, 1104, Springer Nature,
2023, doi:10.1038/s41467-023-36841-1.
short: B. Cheng, S. Hamel, M. Bethkenhagen, Nature Communications 14 (2023).
corr_author: '1'
date_created: 2023-03-05T23:01:04Z
date_published: 2023-02-27T00:00:00Z
date_updated: 2025-04-15T07:39:24Z
day: '27'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1038/s41467-023-36841-1
external_id:
isi:
- '000939678300002'
pmid:
- '36843123'
file:
- access_level: open_access
checksum: 5ff61ad21511950c15abb73b18613883
content_type: application/pdf
creator: cchlebak
date_created: 2023-03-07T10:58:00Z
date_updated: 2023-03-07T10:58:00Z
file_id: '12713'
file_name: 2023_NatComm_Cheng.pdf
file_size: 1946443
relation: main_file
success: 1
file_date_updated: 2023-03-07T10:58:00Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 9B861AAC-BA93-11EA-9121-9846C619BF3A
name: NOMIS Fellowship Program
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Thermodynamics of diamond formation from hydrocarbon mixtures in planets
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 14
year: '2023'
...
---
_id: '12879'
abstract:
- lang: eng
text: Machine learning (ML) has been widely applied to chemical property prediction,
most prominently for the energies and forces in molecules and materials. The strong
interest in predicting energies in particular has led to a ‘local energy’-based
paradigm for modern atomistic ML models, which ensures size-extensivity and a
linear scaling of computational cost with system size. However, many electronic
properties (such as excitation energies or ionization energies) do not necessarily
scale linearly with system size and may even be spatially localized. Using size-extensive
models in these cases can lead to large errors. In this work, we explore different
strategies for learning intensive and localized properties, using HOMO energies
in organic molecules as a representative test case. In particular, we analyze
the pooling functions that atomistic neural networks use to predict molecular
properties, and suggest an orbital weighted average (OWA) approach that enables
the accurate prediction of orbital energies and locations.
acknowledgement: KC acknowledges funding from the China Scholarship Council. KC is
grateful for the TUM graduate school finance support to visit Bingqing Cheng's group
in IST for two months. We also thankfully acknowledge computational resources provided
by the MPCDF Supercomputing Centre.
article_processing_charge: No
article_type: original
author:
- first_name: Ke
full_name: Chen, Ke
id: c636c5ca-e8b8-11ed-b2d4-cc2c37613a8d
last_name: Chen
- first_name: Christian
full_name: Kunkel, Christian
last_name: Kunkel
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Karsten
full_name: Reuter, Karsten
last_name: Reuter
- first_name: Johannes T.
full_name: Margraf, Johannes T.
last_name: Margraf
citation:
ama: Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. Physics-inspired machine learning
of localized intensive properties. Chemical Science. 2023. doi:10.1039/d3sc00841j
apa: Chen, K., Kunkel, C., Cheng, B., Reuter, K., & Margraf, J. T. (2023). Physics-inspired
machine learning of localized intensive properties. Chemical Science. Royal
Society of Chemistry. https://doi.org/10.1039/d3sc00841j
chicago: Chen, Ke, Christian Kunkel, Bingqing Cheng, Karsten Reuter, and Johannes
T. Margraf. “Physics-Inspired Machine Learning of Localized Intensive Properties.”
Chemical Science. Royal Society of Chemistry, 2023. https://doi.org/10.1039/d3sc00841j.
ieee: K. Chen, C. Kunkel, B. Cheng, K. Reuter, and J. T. Margraf, “Physics-inspired
machine learning of localized intensive properties,” Chemical Science.
Royal Society of Chemistry, 2023.
ista: Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. 2023. Physics-inspired machine
learning of localized intensive properties. Chemical Science.
mla: Chen, Ke, et al. “Physics-Inspired Machine Learning of Localized Intensive
Properties.” Chemical Science, Royal Society of Chemistry, 2023, doi:10.1039/d3sc00841j.
short: K. Chen, C. Kunkel, B. Cheng, K. Reuter, J.T. Margraf, Chemical Science (2023).
date_created: 2023-04-30T22:01:06Z
date_published: 2023-04-10T00:00:00Z
date_updated: 2023-08-01T14:18:10Z
day: '10'
ddc:
- '000'
- '540'
department:
- _id: BiCh
doi: 10.1039/d3sc00841j
external_id:
isi:
- '000971508100001'
file:
- access_level: open_access
checksum: 5eeec69a51e192dcd94b955d84423836
content_type: application/pdf
creator: dernst
date_created: 2023-05-02T07:17:05Z
date_updated: 2023-05-02T07:17:05Z
file_id: '12883'
file_name: 2023_ChemialScience_Chen.pdf
file_size: 1515446
relation: main_file
success: 1
file_date_updated: 2023-05-02T07:17:05Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
publication: Chemical Science
publication_identifier:
eissn:
- 2041-6539
issn:
- 2041-6520
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Physics-inspired machine learning of localized intensive properties
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
short: CC BY (3.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2023'
...
---
_id: '12912'
abstract:
- lang: eng
text: The chemical potential of adsorbed or confined fluids provides insight into
their unique thermodynamic properties and determines adsorption isotherms. However,
it is often difficult to compute this quantity from atomistic simulations using
existing statistical mechanical methods. We introduce a computational framework
that utilizes static structure factors, thermodynamic integration, and free energy
perturbation for calculating the absolute chemical potential of fluids. For demonstration,
we apply the method to compute the adsorption isotherms of carbon dioxide in a
metal-organic framework and water in carbon nanotubes.
acknowledgement: We thank Aleks Reinhardt and Daan Frenkel for their insightful comments
and suggestions on the article. B.C. acknowledges the resources provided by the
Cambridge Tier-2 system operated by the University of Cambridge Research Computing
Service funded by EPSRC Tier-2 capital Grant No. EP/P020259/1.
article_number: '161101 '
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Rochus
full_name: Schmid, Rochus
last_name: Schmid
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Schmid R, Cheng B. Computing chemical potentials of adsorbed or confined fluids.
The Journal of Chemical Physics. 2023;158(16). doi:10.1063/5.0146711
apa: Schmid, R., & Cheng, B. (2023). Computing chemical potentials of adsorbed
or confined fluids. The Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0146711
chicago: Schmid, Rochus, and Bingqing Cheng. “Computing Chemical Potentials of Adsorbed
or Confined Fluids.” The Journal of Chemical Physics. AIP Publishing, 2023.
https://doi.org/10.1063/5.0146711.
ieee: R. Schmid and B. Cheng, “Computing chemical potentials of adsorbed or confined
fluids,” The Journal of Chemical Physics, vol. 158, no. 16. AIP Publishing,
2023.
ista: Schmid R, Cheng B. 2023. Computing chemical potentials of adsorbed or confined
fluids. The Journal of Chemical Physics. 158(16), 161101.
mla: Schmid, Rochus, and Bingqing Cheng. “Computing Chemical Potentials of Adsorbed
or Confined Fluids.” The Journal of Chemical Physics, vol. 158, no. 16,
161101, AIP Publishing, 2023, doi:10.1063/5.0146711.
short: R. Schmid, B. Cheng, The Journal of Chemical Physics 158 (2023).
corr_author: '1'
date_created: 2023-05-07T22:01:03Z
date_published: 2023-04-24T00:00:00Z
date_updated: 2024-10-09T21:05:04Z
day: '24'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1063/5.0146711
external_id:
arxiv:
- '2302.01297'
isi:
- '001010676000010'
pmid:
- '37093149'
file:
- access_level: open_access
checksum: 4ab8c965f2fa4e17920bfa846847f137
content_type: application/pdf
creator: dernst
date_created: 2023-05-08T07:44:49Z
date_updated: 2023-05-08T07:44:49Z
file_id: '12918'
file_name: 2023_JourChemicalPhysics_Schmid.pdf
file_size: 6499468
relation: main_file
success: 1
file_date_updated: 2023-05-08T07:44:49Z
has_accepted_license: '1'
intvolume: ' 158'
isi: 1
issue: '16'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
publication: The Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/BingqingCheng/mu-adsorption
- relation: software
url: https://github.com/BingqingCheng/S0
scopus_import: '1'
status: public
title: Computing chemical potentials of adsorbed or confined fluids
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 158
year: '2023'
...