---
_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'
das_tickbox: '1'
dataavailabilitystatement: Additional simulation details are provided in the supplementary
material. All Python scripts and simulation input files generated for the study
are in the supplementary material repository at https://github.com/BingqingCheng/mu-adsorption.
Scripts for the S0 analysis are at https://github.com/BingqingCheng/S0.
date_created: 2023-05-07T22:01:03Z
date_published: 2023-04-24T00:00:00Z
date_updated: 2026-08-07T10:55:19Z
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
fulldoi: https://doi.org/10.1063/5.0146711
has_accepted_license: '1'
intvolume: ' 158'
isi: 1
issue: '16'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
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
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 158
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).
das_tickbox: '1'
dataavailabilitystatement: Data and code for this paper are publicly available at
https://gitlab.mpcdf.mpg.de/kchen/localized-intensive-property-prediciton.git.
date_created: 2023-04-30T22:01:06Z
date_published: 2023-04-10T00:00:00Z
date_updated: 2026-08-07T10:53:37Z
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
fulldoi: https://doi.org/10.1039/d3sc00841j
has_accepted_license: '1'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/3.0/
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'
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2023'
...
---
_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
orcid: 0000-0001-5126-4928
- 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'
das_tickbox: '1'
dataavailabilitystatement: "The machine learning potentials, training sets, sample
DFT and metadynamics input files, PYTHON data analysis scripts and other necessary
source data files generated for this study are available in the SI repository (https://github.com/BingqingCheng/TiO2-water)
see ref 58.\r\n58. Zeng, Z. et al. Source data for Mechanistic insight on water
dissociation on pristine low-index TiO2 surfaces from machine learning molecular
dynamics simulations, Zenodo, https://zenodo.org/record/8301965 (2023)."
date_created: 2023-10-15T22:01:10Z
date_published: 2023-10-02T00:00:00Z
date_updated: 2026-08-07T11:00:29Z
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
fulldoi: https://doi.org/10.1038/s41467-023-41865-8
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
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
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'
das_tickbox: '1'
dataavailabilitystatement: Simulation input files necessary to reproduce the study
and Python data analysis scripts are available in the SI repository at https://github.com/BingqingCheng/solubility,
and on Zenodo at http://doi.org/10.5281/zenodo.8398093.
date_created: 2023-11-26T23:00:54Z
date_published: 2023-11-14T00:00:00Z
date_updated: 2026-08-07T11:07:46Z
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
fulldoi: https://doi.org/10.1063/5.0173341
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
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: no
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: '13231'
abstract:
- lang: eng
text: We study ab initio approaches for calculating x-ray Thomson scattering spectra
from density functional theory molecular dynamics simulations based on a modified
Chihara formula that expresses the inelastic contribution in terms of the dielectric
function. We study the electronic dynamic structure factor computed from the Mermin
dielectric function using an ab initio electron-ion collision frequency in comparison
to computations using a linear-response time-dependent density functional theory
(LR-TDDFT) framework for hydrogen and beryllium and investigate the dispersion
of free-free and bound-free contributions to the scattering signal. A separate
treatment of these contributions, where only the free-free part follows the Mermin
dispersion, shows good agreement with LR-TDDFT results for ambient-density beryllium,
but breaks down for highly compressed matter where the bound states become pressure
ionized. LR-TDDFT is used to reanalyze x-ray Thomson scattering experiments on
beryllium demonstrating strong deviations from the plasma conditions inferred
with traditional analytic models at small scattering angles.
acknowledgement: "We want to thank P. Sperling, B. Witte, M. French, G. Röpke, H.
J. Lee and A. Cangi for many helpful discussions. M. S. and R. R. acknowledge support
by the Deutsche Forschungsgemeinschaft (DFG) within the Research Unit FOR 2440.
All simulations and analyses were performed at the North-German Supercomputing Alliance
(HLRN) and the ITMZ of the University of Rostock. M. B. gratefully acknowledges
support by the European Horizon 2020 programme within the Marie Sklodowska-Curie
actions (xICE grant 894725) and the\r\nNOMIS foundation. The work of T. D. was performed
under the auspices of the U.S. Department of Energy by Lawrence Livermore National
Laboratory under Contract No. DE-AC52-07NA27344."
article_number: '065207'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Maximilian
full_name: Schörner, Maximilian
last_name: Schörner
- first_name: Mandy
full_name: Bethkenhagen, Mandy
id: 201939f4-803f-11ed-ab7e-d8da4bd1517f
last_name: Bethkenhagen
orcid: 0000-0002-1838-2129
- first_name: Tilo
full_name: Döppner, Tilo
last_name: Döppner
- first_name: Dominik
full_name: Kraus, Dominik
last_name: Kraus
- first_name: Luke B.
full_name: Fletcher, Luke B.
last_name: Fletcher
- first_name: Siegfried H.
full_name: Glenzer, Siegfried H.
last_name: Glenzer
- first_name: Ronald
full_name: Redmer, Ronald
last_name: Redmer
citation:
ama: Schörner M, Bethkenhagen M, Döppner T, et al. X-ray Thomson scattering spectra
from density functional theory molecular dynamics simulations based on a modified
Chihara formula. Physical Review E. 2023;107(6). doi:10.1103/PhysRevE.107.065207
apa: Schörner, M., Bethkenhagen, M., Döppner, T., Kraus, D., Fletcher, L. B., Glenzer,
S. H., & Redmer, R. (2023). X-ray Thomson scattering spectra from density
functional theory molecular dynamics simulations based on a modified Chihara formula.
Physical Review E. American Physical Society. https://doi.org/10.1103/PhysRevE.107.065207
chicago: Schörner, Maximilian, Mandy Bethkenhagen, Tilo Döppner, Dominik Kraus,
Luke B. Fletcher, Siegfried H. Glenzer, and Ronald Redmer. “X-Ray Thomson Scattering
Spectra from Density Functional Theory Molecular Dynamics Simulations Based on
a Modified Chihara Formula.” Physical Review E. American Physical Society,
2023. https://doi.org/10.1103/PhysRevE.107.065207.
ieee: M. Schörner et al., “X-ray Thomson scattering spectra from density
functional theory molecular dynamics simulations based on a modified Chihara formula,”
Physical Review E, vol. 107, no. 6. American Physical Society, 2023.
ista: Schörner M, Bethkenhagen M, Döppner T, Kraus D, Fletcher LB, Glenzer SH, Redmer
R. 2023. X-ray Thomson scattering spectra from density functional theory molecular
dynamics simulations based on a modified Chihara formula. Physical Review E. 107(6),
065207.
mla: Schörner, Maximilian, et al. “X-Ray Thomson Scattering Spectra from Density
Functional Theory Molecular Dynamics Simulations Based on a Modified Chihara Formula.”
Physical Review E, vol. 107, no. 6, 065207, American Physical Society,
2023, doi:10.1103/PhysRevE.107.065207.
short: M. Schörner, M. Bethkenhagen, T. Döppner, D. Kraus, L.B. Fletcher, S.H. Glenzer,
R. Redmer, Physical Review E 107 (2023).
das_tickbox: '0'
date_created: 2023-07-16T22:01:10Z
date_published: 2023-06-14T00:00:00Z
date_updated: 2026-08-07T10:59:03Z
day: '14'
department:
- _id: BiCh
doi: 10.1103/PhysRevE.107.065207
external_id:
arxiv:
- '2301.01545'
isi:
- '001020265000002'
pmid:
- '37464593'
fulldoi: https://doi.org/10.1103/PhysRevE.107.065207
intvolume: ' 107'
isi: 1
issue: '6'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.48550/arXiv.2301.01545
month: '06'
oa: 1
oa_version: Preprint
pmid: 1
publication: Physical Review E
publication_identifier:
eissn:
- 2470-0053
issn:
- 2470-0045
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: X-ray Thomson scattering spectra from density functional theory molecular dynamics
simulations based on a modified Chihara formula
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 107
year: '2023'
...
---
_id: '14605'
abstract:
- lang: eng
text: The phonon transport mechanisms and ultralow lattice thermal conductivities
(κL) in silver halide AgX (X=Cl,Br,I) compounds are not yet well understood. Herein,
we study the lattice dynamics and thermal property of AgX under the framework
of perturbation theory and the two-channel Wigner thermal transport model based
on accurate machine learning potentials. We find that an accurate extraction of
the third-order atomic force constants from largely displaced configurations is
significant for the calculation of the κL of AgX, and the coherence thermal transport
is also non-negligible. In AgI, however, the calculated κL still considerably
overestimates the experimental values even including four-phonon scatterings.
Molecular dynamics (MD) simulations using machine learning potential suggest an
important role of the higher-than-fourth-order lattice anharmonicity in the low-frequency
phonon linewidths of AgI at room temperature, which can be related to the simultaneous
restrictions of the three- and four-phonon phase spaces. The κL of AgI calculated
using MD phonon lifetimes including full-order lattice anharmonicity shows a better
agreement with experiments.
acknowledgement: This work is supported by the Research Grants Council of Hong Kong
(Grants No. 17318122 and No. 17306721). The authors are grateful for the research
computing facilities offered by ITS, HKU. Z.Z. acknowledges the European Union’s
Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant
Agreement No. 101034413.
article_number: '174302'
article_processing_charge: No
article_type: original
author:
- first_name: Niuchang
full_name: Ouyang, Niuchang
last_name: Ouyang
- first_name: Zezhu
full_name: Zeng, Zezhu
id: 54a2c730-803f-11ed-ab7e-95b29d2680e7
last_name: Zeng
orcid: 0000-0001-5126-4928
- first_name: Chen
full_name: Wang, Chen
last_name: Wang
- first_name: Qi
full_name: Wang, Qi
last_name: Wang
- first_name: Yue
full_name: Chen, Yue
last_name: Chen
citation:
ama: Ouyang N, Zeng Z, Wang C, Wang Q, Chen Y. Role of high-order lattice anharmonicity
in the phonon thermal transport of silver halide AgX (X=Cl,Br, I). Physical
Review B. 2023;108(17). doi:10.1103/PhysRevB.108.174302
apa: Ouyang, N., Zeng, Z., Wang, C., Wang, Q., & Chen, Y. (2023). Role of high-order
lattice anharmonicity in the phonon thermal transport of silver halide AgX (X=Cl,Br,
I). Physical Review B. American Physical Society. https://doi.org/10.1103/PhysRevB.108.174302
chicago: Ouyang, Niuchang, Zezhu Zeng, Chen Wang, Qi Wang, and Yue Chen. “Role of
High-Order Lattice Anharmonicity in the Phonon Thermal Transport of Silver Halide
AgX (X=Cl,Br, I).” Physical Review B. American Physical Society, 2023.
https://doi.org/10.1103/PhysRevB.108.174302.
ieee: N. Ouyang, Z. Zeng, C. Wang, Q. Wang, and Y. Chen, “Role of high-order lattice
anharmonicity in the phonon thermal transport of silver halide AgX (X=Cl,Br, I),”
Physical Review B, vol. 108, no. 17. American Physical Society, 2023.
ista: Ouyang N, Zeng Z, Wang C, Wang Q, Chen Y. 2023. Role of high-order lattice
anharmonicity in the phonon thermal transport of silver halide AgX (X=Cl,Br, I).
Physical Review B. 108(17), 174302.
mla: Ouyang, Niuchang, et al. “Role of High-Order Lattice Anharmonicity in the Phonon
Thermal Transport of Silver Halide AgX (X=Cl,Br, I).” Physical Review B,
vol. 108, no. 17, 174302, American Physical Society, 2023, doi:10.1103/PhysRevB.108.174302.
short: N. Ouyang, Z. Zeng, C. Wang, Q. Wang, Y. Chen, Physical Review B 108 (2023).
corr_author: '1'
das_tickbox: '0'
date_created: 2023-11-26T23:00:54Z
date_published: 2023-11-01T00:00:00Z
date_updated: 2026-08-07T11:09:12Z
day: '01'
department:
- _id: BiCh
doi: 10.1103/PhysRevB.108.174302
ec_funded: 1
external_id:
isi:
- '001101152500001'
fulldoi: https://doi.org/10.1103/PhysRevB.108.174302
intvolume: ' 108'
isi: 1
issue: '17'
language:
- iso: eng
month: '11'
oa_version: None
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Physical Review B
publication_identifier:
eissn:
- 2469-9969
issn:
- 2469-9950
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Role of high-order lattice anharmonicity in the phonon thermal transport of
silver halide AgX (X=Cl,Br, I)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 108
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: 2026-08-07T11:07:45Z
day: '02'
ddc:
- '530'
department:
- _id: BiCh
doi: 10.5281/ZENODO.8398094
fulldoi: https://doi.org/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: '13216'
abstract:
- lang: eng
text: Physical catalysts often have multiple sites where reactions can take place.
One prominent example is single-atom alloys, where the reactive dopant atoms can
preferentially locate in the bulk or at different sites on the surface of the
nanoparticle. However, ab initio modeling of catalysts usually only considers
one site of the catalyst, neglecting the effects of multiple sites. Here, nanoparticles
of copper doped with single-atom rhodium or palladium are modeled for the dehydrogenation
of propane. Single-atom alloy nanoparticles are simulated at 400–600 K, using
machine learning potentials trained on density functional theory calculations,
and then the occupation of different single-atom active sites is identified using
a similarity kernel. Further, the turnover frequency for all possible sites is
calculated for propane dehydrogenation to propene through microkinetic modeling
using density functional theory calculations. The total turnover frequencies of
the whole nanoparticle are then described from both the population and the individual
turnover frequency of each site. Under operating conditions, rhodium as a dopant
is found to almost exclusively occupy (111) surface sites while palladium as a
dopant occupies a greater variety of facets. Undercoordinated dopant surface sites
are found to tend to be more reactive for propane dehydrogenation compared to
the (111) surface. It is found that considering the dynamics of the single-atom
alloy nanoparticle has a profound effect on the calculated catalytic activity
of single-atom alloys by several orders of magnitude.
acknowledgement: "B.C. acknowledges resources provided by the Cambridge Tier2 system
operated by the University of Cambridge Research\r\nComputing Service funded by
EPSRC Tier-2 capital grant EP/\r\nP020259/1."
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Rhys
full_name: Bunting, Rhys
id: 91deeae8-1207-11ec-b130-c194ad5b50c6
last_name: Bunting
orcid: 0000-0001-6928-074X
- first_name: Felix
full_name: Wodaczek, Felix
id: 8b4b6a9f-32b0-11ee-9fa8-bbe85e26258e
last_name: Wodaczek
orcid: 0009-0000-1457-795X
- first_name: Tina
full_name: Torabi, Tina
last_name: Torabi
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 'Bunting R, Wodaczek F, Torabi T, Cheng B. Reactivity of single-atom alloy
nanoparticles: Modeling the dehydrogenation of propane. Journal of the American
Chemical Society. 2023;145(27):14894-14902. doi:10.1021/jacs.3c04030'
apa: 'Bunting, R., Wodaczek, F., Torabi, T., & Cheng, B. (2023). Reactivity
of single-atom alloy nanoparticles: Modeling the dehydrogenation of propane. Journal
of the American Chemical Society. American Chemical Society. https://doi.org/10.1021/jacs.3c04030'
chicago: 'Bunting, Rhys, Felix Wodaczek, Tina Torabi, and Bingqing Cheng. “Reactivity
of Single-Atom Alloy Nanoparticles: Modeling the Dehydrogenation of Propane.”
Journal of the American Chemical Society. American Chemical Society, 2023.
https://doi.org/10.1021/jacs.3c04030.'
ieee: 'R. Bunting, F. Wodaczek, T. Torabi, and B. Cheng, “Reactivity of single-atom
alloy nanoparticles: Modeling the dehydrogenation of propane,” Journal of the
American Chemical Society, vol. 145, no. 27. American Chemical Society, pp.
14894–14902, 2023.'
ista: 'Bunting R, Wodaczek F, Torabi T, Cheng B. 2023. Reactivity of single-atom
alloy nanoparticles: Modeling the dehydrogenation of propane. Journal of the American
Chemical Society. 145(27), 14894–14902.'
mla: 'Bunting, Rhys, et al. “Reactivity of Single-Atom Alloy Nanoparticles: Modeling
the Dehydrogenation of Propane.” Journal of the American Chemical Society,
vol. 145, no. 27, American Chemical Society, 2023, pp. 14894–902, doi:10.1021/jacs.3c04030.'
short: R. Bunting, F. Wodaczek, T. Torabi, B. Cheng, Journal of the American Chemical
Society 145 (2023) 14894–14902.
corr_author: '1'
das_tickbox: '0'
date_created: 2023-07-12T09:16:40Z
date_published: 2023-06-30T00:00:00Z
date_updated: 2026-08-07T11:37:26Z
day: '30'
ddc:
- '540'
department:
- _id: MaIb
- _id: BiCh
doi: 10.1021/jacs.3c04030
external_id:
isi:
- '001020623900001'
pmid:
- '37390457'
file:
- access_level: open_access
checksum: e07d5323f9c0e5cbd1ad6453f29440ab
content_type: application/pdf
creator: cchlebak
date_created: 2023-07-12T10:22:04Z
date_updated: 2023-07-12T10:22:04Z
file_id: '13219'
file_name: 2023_JACS_Bunting.pdf
file_size: 3155843
relation: main_file
success: 1
file_date_updated: 2023-07-12T10:22:04Z
fulldoi: https://doi.org/10.1021/jacs.3c04030
has_accepted_license: '1'
intvolume: ' 145'
isi: 1
issue: '27'
keyword:
- Colloid and Surface Chemistry
- Biochemistry
- General Chemistry
- Catalysis
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 14894-14902
pmid: 1
publication: Journal of the American Chemical Society
publication_identifier:
eissn:
- 1520-5126
issn:
- 0002-7863
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: yes
title: 'Reactivity of single-atom alloy nanoparticles: Modeling the dehydrogenation
of propane'
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: 145
year: '2023'
...
---
_id: '13118'
abstract:
- lang: eng
text: Under high pressures and temperatures, molecular systems with substantial
polarization charges, such as ammonia and water, are predicted to form superionic
phases and dense fluid states with dissociating molecules and high electrical
conductivity. This behaviour potentially plays a role in explaining the origin
of the multipolar magnetic fields of Uranus and Neptune, whose mantles are thought
to result from a mixture of H2O, NH3 and CH4 ices. Determining the stability domain,
melting curve and electrical conductivity of these superionic phases is therefore
crucial for modelling planetary interiors and dynamos. Here we report the melting
curve of superionic ammonia up to 300 GPa from laser-driven shock compression
of pre-compressed samples and atomistic calculations. We show that ammonia melts
at lower temperatures than water above 100 GPa and that fluid ammonia’s electrical
conductivity exceeds that of water at conditions predicted by hot, super-adiabatic
models for Uranus and Neptune, and enhances the conductivity in their fluid water-rich
dynamo layers.
acknowledgement: We acknowledge the crucial contribution of the LULI2000 laser and
support teams to the success of the experiments. We also thank S. Brygoo and P.
Loubeyre for useful discussions. This research was supported by the French National
Research Agency (ANR) through the projects POMPEI (grant no. ANR-16-CE31-0008) and
SUPER-ICES (grant ANR-15-CE30-008-01), and by the PLAS@PAR Federation. M.F. and
R.R. gratefully acknowledge support by the DFG within the Research Unit FOR 2440.
M.B. was supported by the European Union within the Marie Skłodowska-Curie actions
(xICE grant 894725) and the NOMIS foundation. The DFT-MD calculations were performed
at the North-German Supercomputing Alliance facilities.
article_processing_charge: No
article_type: original
author:
- first_name: J.-A.
full_name: Hernandez, J.-A.
last_name: Hernandez
- first_name: Mandy
full_name: Bethkenhagen, Mandy
id: 201939f4-803f-11ed-ab7e-d8da4bd1517f
last_name: Bethkenhagen
orcid: 0000-0002-1838-2129
- first_name: S.
full_name: Ninet, S.
last_name: Ninet
- first_name: M.
full_name: French, M.
last_name: French
- first_name: A.
full_name: Benuzzi-Mounaix, A.
last_name: Benuzzi-Mounaix
- first_name: F.
full_name: Datchi, F.
last_name: Datchi
- first_name: M.
full_name: Guarguaglini, M.
last_name: Guarguaglini
- first_name: F.
full_name: Lefevre, F.
last_name: Lefevre
- first_name: F.
full_name: Occelli, F.
last_name: Occelli
- first_name: R.
full_name: Redmer, R.
last_name: Redmer
- first_name: T.
full_name: Vinci, T.
last_name: Vinci
- first_name: A.
full_name: Ravasio, A.
last_name: Ravasio
citation:
ama: Hernandez J-A, Bethkenhagen M, Ninet S, et al. Melting curve of superionic
ammonia at planetary interior conditions. Nature Physics. 2023;19:1280-1285.
doi:10.1038/s41567-023-02074-8
apa: Hernandez, J.-A., Bethkenhagen, M., Ninet, S., French, M., Benuzzi-Mounaix,
A., Datchi, F., … Ravasio, A. (2023). Melting curve of superionic ammonia at planetary
interior conditions. Nature Physics. Springer Nature. https://doi.org/10.1038/s41567-023-02074-8
chicago: Hernandez, J.-A., Mandy Bethkenhagen, S. Ninet, M. French, A. Benuzzi-Mounaix,
F. Datchi, M. Guarguaglini, et al. “Melting Curve of Superionic Ammonia at Planetary
Interior Conditions.” Nature Physics. Springer Nature, 2023. https://doi.org/10.1038/s41567-023-02074-8.
ieee: J.-A. Hernandez et al., “Melting curve of superionic ammonia at planetary
interior conditions,” Nature Physics, vol. 19. Springer Nature, pp. 1280–1285,
2023.
ista: Hernandez J-A, Bethkenhagen M, Ninet S, French M, Benuzzi-Mounaix A, Datchi
F, Guarguaglini M, Lefevre F, Occelli F, Redmer R, Vinci T, Ravasio A. 2023. Melting
curve of superionic ammonia at planetary interior conditions. Nature Physics.
19, 1280–1285.
mla: Hernandez, J. A., et al. “Melting Curve of Superionic Ammonia at Planetary
Interior Conditions.” Nature Physics, vol. 19, Springer Nature, 2023, pp.
1280–85, doi:10.1038/s41567-023-02074-8.
short: J.-A. Hernandez, M. Bethkenhagen, S. Ninet, M. French, A. Benuzzi-Mounaix,
F. Datchi, M. Guarguaglini, F. Lefevre, F. Occelli, R. Redmer, T. Vinci, A. Ravasio,
Nature Physics 19 (2023) 1280–1285.
das_tickbox: '1'
dataavailabilitystatement: "Data supporting this study are available in the Supplementary
Information, from the online repository (ref 66) and from the corresponding author
upon request.\r\n66. Hernandez, J.-A. Melting curve of superionic ammonia at planetary
interior conditions. OSF https://doi.org/10.17605/OSF.IO/DETZM (2023)."
date_created: 2023-06-04T22:01:02Z
date_published: 2023-09-01T00:00:00Z
date_updated: 2026-08-07T11:34:35Z
day: '01'
department:
- _id: BiCh
doi: 10.1038/s41567-023-02074-8
external_id:
isi:
- '000996921200001'
fulldoi: https://doi.org/10.1038/s41567-023-02074-8
intvolume: ' 19'
isi: 1
language:
- iso: eng
month: '09'
oa_version: None
page: 1280-1285
publication: Nature Physics
publication_identifier:
eissn:
- 1745-2481
issn:
- 1745-2473
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: erratum
url: https://doi.org/10.1038/s41567-023-02130-3
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Melting curve of superionic ammonia at planetary interior conditions
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 19
year: '2023'
...
---
_id: '13039'
abstract:
- lang: eng
text: We calculate reflectivities of dynamically compressed water, water-ethanol
mixtures, and ammonia at infrared and optical wavelengths with density functional
theory and molecular dynamics simulations. The influence of the exchange-correlation
functional on the results is examined in detail. Our findings indicate that the
consistent use of the HSE hybrid functional reproduces experimental results much
better than the commonly used PBE functional. The HSE functional offers not only
a more accurate description of the electronic band gap but also shifts the onset
of molecular dissociation in the molecular dynamics simulations to significantly
higher pressures. We also highlight the importance of using accurate reference
standards in reflectivity experiments and reanalyze infrared and optical reflectivity
data from recent experiments. Thus, our combined theoretical and experimental
work explains and resolves lingering discrepancies between calculations and measurements
for the investigated molecular substances under shock compression.
acknowledgement: 'We thank R. Redmer for helpful discussions. M.F. acknowledges support
by the Deutsche Forschungsgemeinschaft (DFG) within the FOR 2440. M.B. gratefully
acknowledges support by the European Horizon 2020 programme within the Marie Skłodowska-Curie
actions (xICE Grant No. 894725) and the NOMIS foundation. A.R. and J.-A.H. acknowledge
support form the French National Research Agency (ANR) through the projects POMPEI
(Grant No. ANR-16-CE31-0008) and SUPER-ICES (Grant No. ANR-15-CE30-008-01). The
ab initio calculations were performed at the NorthGerman Supercomputing Alliance
(HLRN) facilities. '
article_number: '134109'
article_processing_charge: No
article_type: original
author:
- first_name: Martin
full_name: French, Martin
last_name: French
- first_name: Mandy
full_name: Bethkenhagen, Mandy
id: 201939f4-803f-11ed-ab7e-d8da4bd1517f
last_name: Bethkenhagen
orcid: 0000-0002-1838-2129
- first_name: Alessandra
full_name: Ravasio, Alessandra
last_name: Ravasio
- first_name: Jean Alexis
full_name: Hernandez, Jean Alexis
last_name: Hernandez
citation:
ama: French M, Bethkenhagen M, Ravasio A, Hernandez JA. Ab initio calculation of
the reflectivity of molecular fluids under shock compression. Physical Review
B. 2023;107(13). doi:10.1103/PhysRevB.107.134109
apa: French, M., Bethkenhagen, M., Ravasio, A., & Hernandez, J. A. (2023). Ab
initio calculation of the reflectivity of molecular fluids under shock compression.
Physical Review B. American Physical Society. https://doi.org/10.1103/PhysRevB.107.134109
chicago: French, Martin, Mandy Bethkenhagen, Alessandra Ravasio, and Jean Alexis
Hernandez. “Ab Initio Calculation of the Reflectivity of Molecular Fluids under
Shock Compression.” Physical Review B. American Physical Society, 2023.
https://doi.org/10.1103/PhysRevB.107.134109.
ieee: M. French, M. Bethkenhagen, A. Ravasio, and J. A. Hernandez, “Ab initio calculation
of the reflectivity of molecular fluids under shock compression,” Physical
Review B, vol. 107, no. 13. American Physical Society, 2023.
ista: French M, Bethkenhagen M, Ravasio A, Hernandez JA. 2023. Ab initio calculation
of the reflectivity of molecular fluids under shock compression. Physical Review
B. 107(13), 134109.
mla: French, Martin, et al. “Ab Initio Calculation of the Reflectivity of Molecular
Fluids under Shock Compression.” Physical Review B, vol. 107, no. 13, 134109,
American Physical Society, 2023, doi:10.1103/PhysRevB.107.134109.
short: M. French, M. Bethkenhagen, A. Ravasio, J.A. Hernandez, Physical Review B
107 (2023).
das_tickbox: '0'
date_created: 2023-05-21T22:01:04Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2026-08-07T11:29:21Z
day: '01'
department:
- _id: BiCh
doi: 10.1103/PhysRevB.107.134109
external_id:
isi:
- '000974672600001'
fulldoi: https://doi.org/10.1103/PhysRevB.107.134109
intvolume: ' 107'
isi: 1
issue: '13'
language:
- iso: eng
month: '04'
oa_version: None
publication: Physical Review B
publication_identifier:
eissn:
- 2469-9969
issn:
- 2469-9950
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Ab initio calculation of the reflectivity of molecular fluids under shock compression
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 107
year: '2023'
...
---
_id: '10827'
abstract:
- lang: eng
text: Titanium dioxide has been extensively studied in the rutile or anatase phase,
while its high-pressure phases are less well-understood, despite that many are
thought to have interesting optical, mechanical, and electrochemical properties.
First-principles methods, such as density functional theory (DFT), are often used
to compute the enthalpies of TiO2 phases at 0 K, but they are expensive and, thus,
impractical for long time scale and large system-size simulations at finite temperatures.
On the other hand, cheap empirical potentials fail to capture the relative stabilities
of various polymorphs. To model the thermodynamic behaviors of ambient and high-pressure
phases of TiO2, we design an empirical model as a baseline and then train a machine
learning potential based on the difference between the DFT data and the empirical
model. This so-called Δ-learning potential contains long-range electrostatic interactions
and predicts the 0 K enthalpies of stable TiO2 phases that are in good agreement
with DFT. We construct a pressure–temperature phase diagram of TiO2 in the range
0 < P < 70 GPa and 100 < T < 1500 K. We then simulate dynamic phase transition
processes by compressing anatase at different temperatures. At 300 K, we predominantly
observe an anatase-to-baddeleyite transformation at about 20 GPa via a martensitic
two-step mechanism with a highly ordered and collective atomic motion. At 2000
K, anatase can transform into cotunnite around 45–55 GPa in a thermally activated
and probabilistic manner, accompanied by diffusive movement of oxygen atoms. The
pressures computed for these transitions show good agreement with experiments.
Our results shed light on how to synthesize and stabilize high-pressure TiO2 phases,
and our method is generally applicable to other functional materials with multiple
polymorphs.
acknowledgement: J.G.L. and B.C. acknowledge the resources provided by the Cambridge
Tier-2 system operated by the University of Cambridge Research Computing Service
funded by the EPSRC Tier-2 capital (Grant No. EP/P020259/1).
article_number: '074106'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Jacob G.
full_name: Lee, Jacob G.
last_name: Lee
- first_name: Chris J.
full_name: Pickard, Chris J.
last_name: Pickard
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Lee JG, Pickard CJ, Cheng B. High-pressure phase behaviors of titanium dioxide
revealed by a Δ-learning potential. The Journal of chemical physics. 2022;156(7).
doi:10.1063/5.0079844
apa: Lee, J. G., Pickard, C. J., & Cheng, B. (2022). High-pressure phase behaviors
of titanium dioxide revealed by a Δ-learning potential. The Journal of Chemical
Physics. AIP Publishing. https://doi.org/10.1063/5.0079844
chicago: Lee, Jacob G., Chris J. Pickard, and Bingqing Cheng. “High-Pressure Phase
Behaviors of Titanium Dioxide Revealed by a Δ-Learning Potential.” The Journal
of Chemical Physics. AIP Publishing, 2022. https://doi.org/10.1063/5.0079844.
ieee: J. G. Lee, C. J. Pickard, and B. Cheng, “High-pressure phase behaviors of
titanium dioxide revealed by a Δ-learning potential,” The Journal of chemical
physics, vol. 156, no. 7. AIP Publishing, 2022.
ista: Lee JG, Pickard CJ, Cheng B. 2022. High-pressure phase behaviors of titanium
dioxide revealed by a Δ-learning potential. The Journal of chemical physics. 156(7),
074106.
mla: Lee, Jacob G., et al. “High-Pressure Phase Behaviors of Titanium Dioxide Revealed
by a Δ-Learning Potential.” The Journal of Chemical Physics, vol. 156,
no. 7, 074106, AIP Publishing, 2022, doi:10.1063/5.0079844.
short: J.G. Lee, C.J. Pickard, B. Cheng, The Journal of Chemical Physics 156 (2022).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: All original data generated for the study and the Δ-learning
potential for TiO2 constructed in this study are in the SI repository at https://github.com/jacobglee1/tio2-mlp.
date_created: 2022-03-06T23:01:53Z
date_published: 2022-02-16T00:00:00Z
date_updated: 2026-08-07T11:17:03Z
day: '16'
department:
- _id: BiCh
doi: 10.1063/5.0079844
external_id:
arxiv:
- '2111.12968'
isi:
- '000796704500014'
pmid:
- '35183078'
fulldoi: https://doi.org/10.1063/5.0079844
intvolume: ' 156'
isi: 1
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2111.12968
month: '02'
oa: 1
oa_version: Preprint
pmid: 1
publication: The Journal of chemical physics
publication_identifier:
eissn:
- 1089-7690
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: High-pressure phase behaviors of titanium dioxide revealed by a Δ-learning
potential
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 156
year: '2022'
...
---
_id: '12128'
abstract:
- lang: eng
text: We introduce a machine-learning (ML) framework for high-throughput benchmarking
of diverse representations of chemical systems against datasets of materials and
molecules. The guiding principle underlying the benchmarking approach is to evaluate
raw descriptor performance by limiting model complexity to simple regression schemes
while enforcing best ML practices, allowing for unbiased hyperparameter optimization,
and assessing learning progress through learning curves along series of synchronized
train-test splits. The resulting models are intended as baselines that can inform
future method development, in addition to indicating how easily a given dataset
can be learnt. Through a comparative analysis of the training outcome across a
diverse set of physicochemical, topological and geometric representations, we
glean insight into the relative merits of these representations as well as their
interrelatedness.
acknowledgement: 'C P acknowledges funding from Astex through the Sustaining Innovation
Program under the Milner Consortium. 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. F A F acknowledges funding
from the Swiss National Science Foundation (Grant No. P2BSP2_191736). '
article_number: '040501'
article_processing_charge: No
article_type: original
author:
- first_name: Carl
full_name: Poelking, Carl
last_name: Poelking
- first_name: Felix A
full_name: Faber, Felix A
last_name: Faber
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 'Poelking C, Faber FA, Cheng B. BenchML: An extensible pipelining framework
for benchmarking representations of materials and molecules at scale. Machine
Learning: Science and Technology. 2022;3(4). doi:10.1088/2632-2153/ac4d11'
apa: 'Poelking, C., Faber, F. A., & Cheng, B. (2022). BenchML: An extensible
pipelining framework for benchmarking representations of materials and molecules
at scale. Machine Learning: Science and Technology. IOP Publishing. https://doi.org/10.1088/2632-2153/ac4d11'
chicago: 'Poelking, Carl, Felix A Faber, and Bingqing Cheng. “BenchML: An Extensible
Pipelining Framework for Benchmarking Representations of Materials and Molecules
at Scale.” Machine Learning: Science and Technology. IOP Publishing, 2022.
https://doi.org/10.1088/2632-2153/ac4d11.'
ieee: 'C. Poelking, F. A. Faber, and B. Cheng, “BenchML: An extensible pipelining
framework for benchmarking representations of materials and molecules at scale,”
Machine Learning: Science and Technology, vol. 3, no. 4. IOP Publishing,
2022.'
ista: 'Poelking C, Faber FA, Cheng B. 2022. BenchML: An extensible pipelining framework
for benchmarking representations of materials and molecules at scale. Machine
Learning: Science and Technology. 3(4), 040501.'
mla: 'Poelking, Carl, et al. “BenchML: An Extensible Pipelining Framework for Benchmarking
Representations of Materials and Molecules at Scale.” Machine Learning: Science
and Technology, vol. 3, no. 4, 040501, IOP Publishing, 2022, doi:10.1088/2632-2153/ac4d11.'
short: 'C. Poelking, F.A. Faber, B. Cheng, Machine Learning: Science and Technology
3 (2022).'
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: The datasets used for this study are available at https://github.com/BingqingCheng/linear-regression-benchmarks.
All data that support the findings of this study are included within the article
(and any supplementary files).
date_created: 2023-01-12T12:02:21Z
date_published: 2022-11-17T00:00:00Z
date_updated: 2026-08-07T11:19:31Z
day: '17'
ddc:
- '000'
department:
- _id: BiCh
doi: 10.1088/2632-2153/ac4d11
external_id:
isi:
- '000886534000001'
file:
- access_level: open_access
checksum: 8930d4ad6ed9b47358c6f1a68666adb6
content_type: application/pdf
creator: dernst
date_created: 2023-01-23T10:42:04Z
date_updated: 2023-01-23T10:42:04Z
file_id: '12343'
file_name: 2022_MachLearning_Poelking.pdf
file_size: 13814559
relation: main_file
success: 1
file_date_updated: 2023-01-23T10:42:04Z
fulldoi: https://doi.org/10.1088/2632-2153/ac4d11
has_accepted_license: '1'
intvolume: ' 3'
isi: 1
issue: '4'
keyword:
- Artificial Intelligence
- Human-Computer Interaction
- Software
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: 'Machine Learning: Science and Technology'
publication_identifier:
issn:
- 2632-2153
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/capoe/benchml
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: 'BenchML: An extensible pipelining framework for benchmarking representations
of materials and molecules at scale'
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: 3
year: '2022'
...
---
_id: '12249'
abstract:
- lang: eng
text: 'The chemical potential of a component in a solution is defined as the free
energy change as the amount of that component changes. Computing this fundamental
thermodynamic property from atomistic simulations is notoriously difficult because
of the convergence issues involved in free energy methods and finite size effects.
This Communication presents the so-called S0 method, which can be used to obtain
chemical potentials from static structure factors computed from equilibrium molecular
dynamics simulations under the isothermal–isobaric ensemble. This new method is
demonstrated on the systems of binary Lennard-Jones particles, urea–water mixtures,
a NaCl aqueous solution, and a high-pressure carbon–hydrogen mixture. '
acknowledgement: I thank Daan Frenkel for providing feedback on an early draft and
for stimulating discussions, Debashish Mukherji and Robinson Cortes-Huerto for sharing
the trajectories for urea–water mixtures, and Aleks Reinhardt for useful suggestions
on the manuscript.
article_number: '121101'
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
citation:
ama: Cheng B. Computing chemical potentials of solutions from structure factors.
The Journal of Chemical Physics. 2022;157(12). doi:10.1063/5.0107059
apa: Cheng, B. (2022). Computing chemical potentials of solutions from structure
factors. The Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0107059
chicago: Cheng, Bingqing. “Computing Chemical Potentials of Solutions from Structure
Factors.” The Journal of Chemical Physics. AIP Publishing, 2022. https://doi.org/10.1063/5.0107059.
ieee: B. Cheng, “Computing chemical potentials of solutions from structure factors,”
The Journal of Chemical Physics, vol. 157, no. 12. AIP Publishing, 2022.
ista: Cheng B. 2022. Computing chemical potentials of solutions from structure factors.
The Journal of Chemical Physics. 157(12), 121101.
mla: Cheng, Bingqing. “Computing Chemical Potentials of Solutions from Structure
Factors.” The Journal of Chemical Physics, vol. 157, no. 12, 121101, AIP
Publishing, 2022, doi:10.1063/5.0107059.
short: B. Cheng, The Journal of Chemical Physics 157 (2022).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: "All PYTHON scripts and simulation input files generated
for the study are in the SI repository https://github.com/BingqingCheng/S0, Ref.
29.\r\n29. B. Cheng, https://github.com/BingqingCheng/S0 “Data repository for the
S0 method” (2020)."
date_created: 2023-01-16T09:56:20Z
date_published: 2022-09-30T00:00:00Z
date_updated: 2026-08-07T11:22:29Z
day: '30'
ddc:
- '530'
- '540'
department:
- _id: BiCh
doi: 10.1063/5.0107059
external_id:
isi:
- '000862856000003'
pmid:
- '36182422'
file:
- access_level: open_access
checksum: b0915b706568a663a9a372fca24adf35
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T09:07:00Z
date_updated: 2023-01-30T09:07:00Z
file_id: '12441'
file_name: 2022_JourChemPhysics_Cheng.pdf
file_size: 4402384
relation: main_file
success: 1
file_date_updated: 2023-01-30T09:07:00Z
fulldoi: https://doi.org/10.1063/5.0107059
has_accepted_license: '1'
intvolume: ' 157'
isi: 1
issue: '12'
keyword:
- Physical and Theoretical Chemistry
- General Physics and Astronomy
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
pmid: 1
publication: The 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/ BingqingCheng/S0
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Computing chemical potentials of solutions from structure factors
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: 157
year: '2022'
...
---
_id: '11937'
abstract:
- lang: eng
text: Most experimentally known high-pressure ice phases have a body-centred cubic
(bcc) oxygen lattice. Our large-scale molecular-dynamics simulations with a machine-learning
potential indicate that, amongst these bcc ice phases, ices VII, VII′ and X are
the same thermodynamic phase under different conditions, whereas superionic ice
VII″ has a first-order phase boundary with ice VII′. Moreover, at about 300 GPa,
the transformation between ice X and the Pbcm phase has a sharp structural change
but no apparent activation barrier, whilst at higher pressures the barrier gradually
increases. Our study thus clarifies the phase behaviour of the high-pressure ices
and reveals peculiar solid–solid transition mechanisms not known in other systems.
acknowledgement: We thank Chris Pickard for providing the initial structures of high-pressure
ice phases and for useful advice. 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 EP/P020259/1. M.B. was supported by
the European Union within the Marie Skłodowska-Curie actions (xICE grant 894725)
and acknowledges computational resources at North-German Supercomputing Alliance
(HLRN) facilities. S.H. and M.M. acknowledge support from LDRD 19-ERD-031 and computing
support from the Lawrence Livermore National Laboratory (LLNL) Institutional Computing
Grand Challenge programme. F.C. acknowledges support from the US DOE Office of Science,
Office of Fusion Energy Sciences. 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.
article_number: '4707'
article_processing_charge: No
article_type: original
author:
- first_name: Aleks
full_name: Reinhardt, Aleks
last_name: Reinhardt
- first_name: Mandy
full_name: Bethkenhagen, Mandy
last_name: Bethkenhagen
- first_name: Federica
full_name: Coppari, Federica
last_name: Coppari
- first_name: Marius
full_name: Millot, Marius
last_name: Millot
- first_name: Sebastien
full_name: Hamel, Sebastien
last_name: Hamel
- 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, Bethkenhagen M, Coppari F, Millot M, Hamel S, Cheng B. Thermodynamics
of high-pressure ice phases explored with atomistic simulations. Nature Communications.
2022;13. doi:10.1038/s41467-022-32374-1
apa: Reinhardt, A., Bethkenhagen, M., Coppari, F., Millot, M., Hamel, S., &
Cheng, B. (2022). Thermodynamics of high-pressure ice phases explored with atomistic
simulations. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-022-32374-1
chicago: Reinhardt, Aleks, Mandy Bethkenhagen, Federica Coppari, Marius Millot,
Sebastien Hamel, and Bingqing Cheng. “Thermodynamics of High-Pressure Ice Phases
Explored with Atomistic Simulations.” Nature Communications. Springer Nature,
2022. https://doi.org/10.1038/s41467-022-32374-1.
ieee: A. Reinhardt, M. Bethkenhagen, F. Coppari, M. Millot, S. Hamel, and B. Cheng,
“Thermodynamics of high-pressure ice phases explored with atomistic simulations,”
Nature Communications, vol. 13. Springer Nature, 2022.
ista: Reinhardt A, Bethkenhagen M, Coppari F, Millot M, Hamel S, Cheng B. 2022.
Thermodynamics of high-pressure ice phases explored with atomistic simulations.
Nature Communications. 13, 4707.
mla: Reinhardt, Aleks, et al. “Thermodynamics of High-Pressure Ice Phases Explored
with Atomistic Simulations.” Nature Communications, vol. 13, 4707, Springer
Nature, 2022, doi:10.1038/s41467-022-32374-1.
short: A. Reinhardt, M. Bethkenhagen, F. Coppari, M. Millot, S. Hamel, B. Cheng,
Nature Communications 13 (2022).
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: All original data generated for the study are in the SI
repository https://github.com/BingqingCheng/highP-ice.
date_created: 2022-08-21T22:01:55Z
date_published: 2022-08-10T00:00:00Z
date_updated: 2026-08-07T11:18:25Z
day: '10'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1038/s41467-022-32374-1
external_id:
isi:
- '000838655300022'
pmid:
- '35948550'
file:
- access_level: open_access
checksum: 8ff9b689cde59fd3a9959a9f01929dea
content_type: application/pdf
creator: dernst
date_created: 2022-08-22T06:33:02Z
date_updated: 2022-08-22T06:33:02Z
file_id: '11939'
file_name: 2022_NatureCommunications_Reinhardt.pdf
file_size: 1767206
relation: main_file
success: 1
file_date_updated: 2022-08-22T06:33:02Z
fulldoi: https://doi.org/10.1038/s41467-022-32374-1
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
language:
- iso: eng
month: '08'
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'
researchdata_availability: yes
scopus_import: '1'
status: public
supplementarymaterial: yes
title: Thermodynamics of high-pressure ice phases explored with atomistic 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: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 13
year: '2022'
...
---
_id: '9695'
abstract:
- lang: eng
text: Real-world data typically contain a large number of features that are often
heterogeneous in nature, relevance, and also units of measure. When assessing
the similarity between data points, one can build various distance measures using
subsets of these features. Using the fewest features but still retaining sufficient
information about the system is crucial in many statistical learning approaches,
particularly when data are sparse. We introduce a statistical test that can assess
the relative information retained when using two different distance measures,
and determine if they are equivalent, independent, or if one is more informative
than the other. This in turn allows finding the most informative distance measure
out of a pool of candidates. The approach is applied to find the most relevant
policy variables for controlling the Covid-19 epidemic and to find compact yet
informative representations of atomic structures, but its potential applications
are wide ranging in many branches of science.
acknowledgement: "A.G., C.Z., and A.L. gratefully acknowledge support from the European
Union’s Horizon 2020 research and innovation program (grant number 824143, MaX ’Materials
design at the eXascale’ Centre of Excellence). The authors would like to thank M.
Carli, D. Doimo, and I. Macocco (SISSA) for the discussions, M. Caro (Aalto University)
for the precious help in using the TurboGap code, and D. Frenkel (University of
Cambridge) and N. Bernstein (US Naval Research Laboratory) for useful feedback on
the manuscript.\r\nThis work is supported in part by funds from the European Union’s
Horizon 2020 research and innovation program (grant number 824143, MaX ’Materials
design at the eXascale’ Centre of Excellence)."
article_number: pgac039
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Aldo
full_name: Glielmo, Aldo
last_name: Glielmo
- first_name: Claudio
full_name: Zeni, Claudio
last_name: Zeni
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Gabor
full_name: Csanyi, Gabor
last_name: Csanyi
- first_name: Alessandro
full_name: Laio, Alessandro
last_name: Laio
citation:
ama: Glielmo A, Zeni C, Cheng B, Csanyi G, Laio A. Ranking the information content
of distance measures. PNAS Nexus. 2022;1(2). doi:10.1093/pnasnexus/pgac039
apa: Glielmo, A., Zeni, C., Cheng, B., Csanyi, G., & Laio, A. (2022). Ranking
the information content of distance measures. PNAS Nexus. Oxford University
Press. https://doi.org/10.1093/pnasnexus/pgac039
chicago: Glielmo, Aldo, Claudio Zeni, Bingqing Cheng, Gabor Csanyi, and Alessandro
Laio. “Ranking the Information Content of Distance Measures.” PNAS Nexus.
Oxford University Press, 2022. https://doi.org/10.1093/pnasnexus/pgac039.
ieee: A. Glielmo, C. Zeni, B. Cheng, G. Csanyi, and A. Laio, “Ranking the information
content of distance measures,” PNAS Nexus, vol. 1, no. 2. Oxford University
Press, 2022.
ista: Glielmo A, Zeni C, Cheng B, Csanyi G, Laio A. 2022. Ranking the information
content of distance measures. PNAS Nexus. 1(2), pgac039.
mla: Glielmo, Aldo, et al. “Ranking the Information Content of Distance Measures.”
PNAS Nexus, vol. 1, no. 2, pgac039, Oxford University Press, 2022, doi:10.1093/pnasnexus/pgac039.
short: A. Glielmo, C. Zeni, B. Cheng, G. Csanyi, A. Laio, PNAS Nexus 1 (2022).
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title: Ranking the information content of distance measures
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