---
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'
...
---
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: 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'
...
---
_id: '9680'
abstract:
- lang: eng
text: Atomistic modeling of phase transitions, chemical reactions, or other rare
events that involve overcoming high free energy barriers usually entails prohibitively
long simulation times. Introducing a bias potential as a function of an appropriately
chosen set of collective variables can significantly accelerate the exploration
of phase space, albeit at the price of distorting the distribution of microstates.
Efficient reweighting to recover the unbiased distribution can be nontrivial when
employing adaptive sampling techniques such as metadynamics, variationally enhanced
sampling, or parallel bias metadynamics, in which the system evolves in a quasi-equilibrium
manner under a time-dependent bias. We introduce an iterative unbiasing scheme
that makes efficient use of all the trajectory data and that does not require
the distribution to be evaluated on a grid. The method can thus be used even when
the bias has a high dimensionality. We benchmark this approach against some of
the existing schemes on model systems with different complexity and dimensionality.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: F.
full_name: Giberti, F.
last_name: Giberti
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: G. A.
full_name: Tribello, G. A.
last_name: Tribello
- first_name: M.
full_name: Ceriotti, M.
last_name: Ceriotti
citation:
ama: Giberti F, Cheng B, Tribello GA, Ceriotti M. Iterative unbiasing of quasi-equilibrium
sampling. Journal of Chemical Theory and Computation. 2019;16(1):100-107.
doi:10.1021/acs.jctc.9b00907
apa: Giberti, F., Cheng, B., Tribello, G. A., & Ceriotti, M. (2019). Iterative
unbiasing of quasi-equilibrium sampling. Journal of Chemical Theory and Computation.
American Chemical Society. https://doi.org/10.1021/acs.jctc.9b00907
chicago: Giberti, F., Bingqing Cheng, G. A. Tribello, and M. Ceriotti. “Iterative
Unbiasing of Quasi-Equilibrium Sampling.” Journal of Chemical Theory and Computation.
American Chemical Society, 2019. https://doi.org/10.1021/acs.jctc.9b00907.
ieee: F. Giberti, B. Cheng, G. A. Tribello, and M. Ceriotti, “Iterative unbiasing
of quasi-equilibrium sampling,” Journal of Chemical Theory and Computation,
vol. 16, no. 1. American Chemical Society, pp. 100–107, 2019.
ista: Giberti F, Cheng B, Tribello GA, Ceriotti M. 2019. Iterative unbiasing of
quasi-equilibrium sampling. Journal of Chemical Theory and Computation. 16(1),
100–107.
mla: Giberti, F., et al. “Iterative Unbiasing of Quasi-Equilibrium Sampling.” Journal
of Chemical Theory and Computation, vol. 16, no. 1, American Chemical Society,
2019, pp. 100–07, doi:10.1021/acs.jctc.9b00907.
short: F. Giberti, B. Cheng, G.A. Tribello, M. Ceriotti, Journal of Chemical Theory
and Computation 16 (2019) 100–107.
date_created: 2021-07-19T06:56:45Z
date_published: 2019-01-14T00:00:00Z
date_updated: 2021-08-09T12:37:37Z
day: '14'
doi: 10.1021/acs.jctc.9b00907
extern: '1'
external_id:
arxiv:
- '1911.01140'
pmid:
- '31743021'
intvolume: ' 16'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1911.01140
month: '01'
oa: 1
oa_version: Preprint
page: 100-107
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: Iterative unbiasing of quasi-equilibrium sampling
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 16
year: '2019'
...
---
OA_type: closed access
_id: '17967'
abstract:
- lang: eng
text: We report a systematic computational search of molecular frameworks for intrinsic
rectification of electron transport. The screening of molecular rectifiers includes
52 molecules and conformers spanning over 9 series of structural motifs. N-Phenylbenzamide
is found to be a promising framework with both suitable conductance and rectification
properties. A targeted screening performed on 30 additional derivatives and conformers
of N-phenylbenzamide yielded enhanced rectification based on asymmetric functionalization.
We demonstrate that electron-donating substituent groups that maintain an asymmetric
distribution of charge in the dominant transport channel (e.g., HOMO) enhance
rectification by raising the channel closer to the Fermi level. These findings
are particularly valuable for the design of molecular assemblies that could ensure
directionality of electron transport in a wide range of applications, from molecular
electronics to catalytic reactions.
article_processing_charge: No
article_type: original
author:
- first_name: Wendu
full_name: Ding, Wendu
last_name: Ding
- first_name: Matthieu
full_name: Koepf, Matthieu
last_name: Koepf
- first_name: Christopher
full_name: Koenigsmann, Christopher
last_name: Koenigsmann
- first_name: Arunabh
full_name: Batra, Arunabh
last_name: Batra
- first_name: Latha
full_name: Venkataraman, Latha
id: 9ebb78a5-cc0d-11ee-8322-fae086a32caf
last_name: Venkataraman
orcid: 0000-0002-6957-6089
- first_name: Christian F. A.
full_name: Negre, Christian F. A.
last_name: Negre
- first_name: Gary W.
full_name: Brudvig, Gary W.
last_name: Brudvig
- first_name: Robert H.
full_name: Crabtree, Robert H.
last_name: Crabtree
- first_name: Charles A.
full_name: Schmuttenmaer, Charles A.
last_name: Schmuttenmaer
- first_name: Victor S.
full_name: Batista, Victor S.
last_name: Batista
citation:
ama: Ding W, Koepf M, Koenigsmann C, et al. Computational design of intrinsic molecular
rectifiers based on asymmetric functionalization of N-Phenylbenzamide. Journal
of Chemical Theory and Computation. 2015;11(12):5888-5896. doi:10.1021/acs.jctc.5b00823
apa: Ding, W., Koepf, M., Koenigsmann, C., Batra, A., Venkataraman, L., Negre, C.
F. A., … Batista, V. S. (2015). Computational design of intrinsic molecular rectifiers
based on asymmetric functionalization of N-Phenylbenzamide. Journal of Chemical
Theory and Computation. American Chemical Society. https://doi.org/10.1021/acs.jctc.5b00823
chicago: Ding, Wendu, Matthieu Koepf, Christopher Koenigsmann, Arunabh Batra, Latha
Venkataraman, Christian F. A. Negre, Gary W. Brudvig, Robert H. Crabtree, Charles
A. Schmuttenmaer, and Victor S. Batista. “Computational Design of Intrinsic Molecular
Rectifiers Based on Asymmetric Functionalization of N-Phenylbenzamide.” Journal
of Chemical Theory and Computation. American Chemical Society, 2015. https://doi.org/10.1021/acs.jctc.5b00823.
ieee: W. Ding et al., “Computational design of intrinsic molecular rectifiers
based on asymmetric functionalization of N-Phenylbenzamide,” Journal of Chemical
Theory and Computation, vol. 11, no. 12. American Chemical Society, pp. 5888–5896,
2015.
ista: Ding W, Koepf M, Koenigsmann C, Batra A, Venkataraman L, Negre CFA, Brudvig
GW, Crabtree RH, Schmuttenmaer CA, Batista VS. 2015. Computational design of intrinsic
molecular rectifiers based on asymmetric functionalization of N-Phenylbenzamide.
Journal of Chemical Theory and Computation. 11(12), 5888–5896.
mla: Ding, Wendu, et al. “Computational Design of Intrinsic Molecular Rectifiers
Based on Asymmetric Functionalization of N-Phenylbenzamide.” Journal of Chemical
Theory and Computation, vol. 11, no. 12, American Chemical Society, 2015,
pp. 5888–96, doi:10.1021/acs.jctc.5b00823.
short: W. Ding, M. Koepf, C. Koenigsmann, A. Batra, L. Venkataraman, C.F.A. Negre,
G.W. Brudvig, R.H. Crabtree, C.A. Schmuttenmaer, V.S. Batista, Journal of Chemical
Theory and Computation 11 (2015) 5888–5896.
date_created: 2024-09-09T09:42:20Z
date_published: 2015-11-03T00:00:00Z
date_updated: 2024-12-18T10:33:40Z
day: '03'
doi: 10.1021/acs.jctc.5b00823
extern: '1'
external_id:
pmid:
- '26642992'
intvolume: ' 11'
issue: '12'
language:
- iso: eng
month: '11'
oa_version: None
page: 5888-5896
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: Computational design of intrinsic molecular rectifiers based on asymmetric
functionalization of N-Phenylbenzamide
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 11
year: '2015'
...