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Journal Articles

Thermodynamic and kinetic modeling of electrocatalytic reactions using a first-principles approach

Vasanthapandiyan M, Shagun Singh, Fernanda Bononi, Oliviero Andreussi, Naiwrit Karmodak
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 111001 (2023)
https://doi.org/10.1063/5.0165835
Published: September 2023
View article titled, Thermodynamic and kinetic modeling of electrocatalytic reactions using a first-principles approach
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Journal Articles

Recent advances in the SISSO method and their implementation in the SISSO++ code

Thomas A. R. Purcell, Matthias Scheffler, Luca M. Ghiringhelli
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 114110 (2023)
https://doi.org/10.1063/5.0156620
Published: September 2023
View article titled, Recent advances in the SISSO method and their implementation in the SISSO++ code
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Journal Articles

Synergy of semiempirical models and machine learning in computational chemistry

Nikita Fedik, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Maksim Kulichenko, Ying Wai Li, Roman Zubatyuk, Richard Messerly, Olexandr Isayev, Sergei Tretiak
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 110901 (2023)
https://doi.org/10.1063/5.0151833
Published: September 2023
View article titled, Synergy of semiempirical models and machine learning in computational chemistry
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Journal Articles

Physics-informed Bayesian inference of external potentials in classical density-functional theory

Antonio Malpica-Morales, Peter Yatsyshin, Miguel A. Durán-Olivencia, Serafim Kalliadasis
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 104109 (2023)
https://doi.org/10.1063/5.0146920
Published: September 2023
View article titled, Physics-informed Bayesian inference of external potentials in classical density-functional theory
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Journal Articles

Global analysis of energy landscapes for materials modeling: A test case for C60

Gábor Csányi, John W. R. Morgan, David J. Wales
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 104107 (2023)
https://doi.org/10.1063/5.0167857
Published: September 2023
View article titled, Global analysis of energy landscapes for materials modeling: A test case for C<sub>60</sub>
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Journal Articles

Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors

Badal Mondal, Julia Westermayr, Ralf Tonner-Zech
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 104702 (2023)
https://doi.org/10.1063/5.0159604
Published: September 2023
View article titled, Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors
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Includes: Supplementary data
Journal Articles

Construct exchange-correlation functional via machine learning

Jiang Wu, Sai-Mang Pun, Xiao Zheng, GuanHua Chen
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 090901 (2023)
https://doi.org/10.1063/5.0150587
Published: September 2023
View article titled, Construct exchange-correlation functional via machine learning
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Journal Articles

DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions

Z. Schätzle, P. B. Szabó, M. Mezera, J. Hermann, F. Noé
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 094108 (2023)
https://doi.org/10.1063/5.0157512
Published: September 2023
View article titled, DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions
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Journal Articles

Machine-learned correction to ensemble-averaged wave packet dynamics

Yannick Holtkamp, Markus Kowalewski, Jens Jasche, Ulrich Kleinekathöfer
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 094107 (2023)
https://doi.org/10.1063/5.0166694
Published: September 2023
View article titled, Machine-learned correction to ensemble-averaged wave packet dynamics
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Journal Articles

Optimizing the architecture of Behler–Parrinello neural network potentials

Lukáš Kývala, Christoph Dellago
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 094105 (2023)
https://doi.org/10.1063/5.0167260
Published: September 2023
View article titled, Optimizing the architecture of Behler–Parrinello neural network potentials
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Includes: Supplementary data
Journal Articles

ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys

Yu-Xin Guo, Yong-Bin Zhuang, Jueli Shi, Jun Cheng
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 094801 (2023)
https://doi.org/10.1063/5.0166858
Published: September 2023
View article titled, ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys
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Journal Articles

Neural network predicts ion concentration profiles under nanoconfinement

Zhonglin Cao, Yuyang Wang, Cooper Lorsung, Amir Barati Farimani
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 094702 (2023)
https://doi.org/10.1063/5.0147119
Published: September 2023
View article titled, Neural network predicts ion concentration profiles under nanoconfinement
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Journal Articles

GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics

Bojun Liu, Mingyi Xue, Yunrui Qiu, Kirill A. Konovalov, Michael S. O’Connor, Xuhui Huang
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 094901 (2023)
https://doi.org/10.1063/5.0158903
Published: September 2023
View article titled, GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics
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Includes: Supplementary data
Journal Articles

PANNA 2.0: Efficient neural network interatomic potentials and new architectures

Franco Pellegrini, Ruggero Lot, Yusuf Shaidu, Emine Küçükbenli
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 084117 (2023)
https://doi.org/10.1063/5.0158075
Published: August 2023
View article titled, PANNA 2.0: Efficient neural network interatomic potentials and new architectures
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Includes: Supplementary data
Journal Articles

Neural potentials of proteins extrapolate beyond training data

Geemi P. Wellawatte, Glen M. Hocky, Andrew D. White
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 085103 (2023)
https://doi.org/10.1063/5.0147240
Published: August 2023
View article titled, Neural potentials of proteins extrapolate beyond training data
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Includes: Supplementary data
Journal Articles

MLIP-3: Active learning on atomic environments with moment tensor potentials

Evgeny Podryabinkin, Kamil Garifullin, Alexander Shapeev, Ivan Novikov
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 084112 (2023)
https://doi.org/10.1063/5.0155887
Published: August 2023
View article titled, MLIP-3: Active learning on atomic environments with moment tensor potentials
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Includes: Supplementary data
Journal Articles

Chemical evolution in nitrogen shocked beyond the molecular stability limit

Rebecca K. Lindsey, Sorin Bastea, Yanjun Lyu, Sebastien Hamel, Nir Goldman, Laurence E. Fried
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 084502 (2023)
https://doi.org/10.1063/5.0157238
Published: August 2023
View article titled, Chemical evolution in nitrogen shocked beyond the molecular stability limit
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Includes: Supplementary data
Journal Articles

Persistent homology-based descriptor for machine-learning potential of amorphous structures

Emi Minamitani, Ippei Obayashi, Koji Shimizu, Satoshi Watanabe
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 084101 (2023)
https://doi.org/10.1063/5.0159349
Published: August 2023
View article titled, Persistent homology-based descriptor for machine-learning potential of amorphous structures
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Includes: Supplementary data
Journal Articles

Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning

Atreyee Banerjee, Aysenur Iscen, Kurt Kremer, Oleksandra Kukharenko
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 074108 (2023)
https://doi.org/10.1063/5.0151156
Published: August 2023
View article titled, Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning
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Includes: Supplementary data
Journal Articles

Efficient approximation of molecular kinetics using random Fourier features

Feliks Nüske, Stefan Klus
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 074105 (2023)
https://doi.org/10.1063/5.0162619
Published: August 2023
View article titled, Efficient approximation of molecular kinetics using random Fourier features
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