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Journal Articles

Splitting probabilities as optimal controllers of rare reactive events

Aditya N. Singh, David T. Limmer

J. Chem. Phys. 161, 054113 (2024)

https://doi.org/10.1063/5.0203840

Published: August 2024

View article titled, Splitting probabilities as optimal controllers of rare reactive events

Journal Articles

Simulating multipulse NMR spectra of polycrystalline solids in the frequency domain

Deepansh J. Srivastava, Philip J. Grandinetti

J. Chem. Phys. 160, 234110 (2024)

https://doi.org/10.1063/5.0209887

Published: June 2024

View article titled, Simulating multipulse NMR spectra of polycrystalline solids in the frequency domain

Journal Articles

Amorphous MoS₂ from a machine learning inter-atomic potential

Kossi Kety, Tsogbadrakh Namsrai, Huma Nawaz, Samare Rostami, Nicola Seriani

J. Chem. Phys. 160, 204709 (2024)

https://doi.org/10.1063/5.0211841

Published: May 2024

View article titled, Amorphous MoS<sub>2</sub> from a machine learning inter-atomic potential

Journal Articles

Classification of complex local environments in systems of particle shapes through shape symmetry-encoded data augmentation

Shih-Kuang (Alex) Lee, Sun-Ting Tsai, Sharon C. Glotzer

J. Chem. Phys. 160, 154102 (2024)

https://doi.org/10.1063/5.0194820

Published: April 2024

View article titled, Classification of complex local environments in systems of particle shapes through shape symmetry-encoded data augmentation

Includes: Supplementary data

Journal Articles

Local-environment-guided selection of atomic structures for the development of machine-learning potentials

Renzhe Li, Chuan Zhou, Akksay Singh, Yong Pei, Graeme Henkelman, Lei Li

J. Chem. Phys. 160, 074109 (2024)

https://doi.org/10.1063/5.0187892

Published: February 2024

View article titled, Local-environment-guided selection of atomic structures for the development of machine-learning potentials

Includes: Supplementary data

Journal Articles

A geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure

Xiao Jiang, Zean Tian, Kenli Li, Wangyu Hu

J. Chem. Phys. 159, 144504 (2023)

https://doi.org/10.1063/5.0162463

Published: October 2023

View article titled, A geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure

Includes: Supplementary data

Journal Articles

First-principles molten salt phase diagrams through thermodynamic integration

Tanooj Shah, Kamron Fazel, Jie Lian, Liping Huang, Yunfeng Shi, Ravishankar Sundararaman

J. Chem. Phys. 159, 124502 (2023)

https://doi.org/10.1063/5.0164824

Published: September 2023

View article titled, First-principles molten salt phase diagrams through thermodynamic integration

Includes: Supplementary data

Journal Articles

Mapping of Hückel zigzag carbon nanotubes onto independent polyene chains: Application to periodic nanotubes

Grégoire François, Celestino Angeli, Gian Luigi Bendazzoli, Véronique Brumas, Stefano Evangelisti, J. Arjan Berger

J. Chem. Phys. 159, 094106 (2023)

https://doi.org/10.1063/5.0153075

Published: September 2023

View article titled, Mapping of Hückel zigzag carbon nanotubes onto independent polyene chains: Application to periodic nanotubes

Journal Articles

Optimizing the architecture of Behler–Parrinello neural network potentials

Lukáš Kývala, Christoph Dellago

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

Includes: Supplementary data

Journal Articles

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions

Gerardo Campos-Villalobos, Giuliana Giunta, Susana Marín-Aguilar, Marjolein Dijkstra

J. Chem. Phys. 157, 024902 (2022)

https://doi.org/10.1063/5.0091319

Published: July 2022

View article titled, Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions

Includes: Supplementary data

Journal Articles

Critical assessment of machine-learned repulsive potentials for the density functional based tight-binding method: A case study for pure silicon

D. Bissuel, T. Albaret, T. A. Niehaus

J. Chem. Phys. 156, 064101 (2022)

https://doi.org/10.1063/5.0081159

Published: February 2022

View article titled, Critical assessment of machine-learned repulsive potentials for the density functional based tight-binding method: A case study for pure silicon

Includes: Supplementary data

Journal Articles

Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites

Wei Bin How, Bipeng Wang, Weibin Chu, Sergiy M. Kovalenko, Alexandre Tkatchenko, Oleg V. Prezhdo

J. Chem. Phys. 156, 054110 (2022)

https://doi.org/10.1063/5.0078473

Published: February 2022

View article titled, Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites

Journal Articles

Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions

Behnam Parsaeifard, Stefan Goedecker

J. Chem. Phys. 156, 034302 (2022)

https://doi.org/10.1063/5.0070488

Published: January 2022

View article titled, Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions

Journal Articles

Modeling of effective interactions between ligand coated nanoparticles through symmetry functions

Dinesh Chintha, Shivanand Kumar Veesam, Emanuele Boattini, Laura Filion, Sudeep N. Punnathanam

J. Chem. Phys. 155, 244901 (2021)

https://doi.org/10.1063/5.0072272

Published: December 2021

View article titled, Modeling of effective interactions between ligand coated nanoparticles through symmetry functions

Journal Articles

Machine learning many-body potentials for colloidal systems

Gerardo Campos-Villalobos, Emanuele Boattini, Laura Filion, Marjolein Dijkstra

J. Chem. Phys. 155, 174902 (2021)

https://doi.org/10.1063/5.0063377

Published: November 2021

View article titled, Machine learning many-body potentials for colloidal systems

Journal Articles

Learning intermolecular forces at liquid–vapor interfaces

Samuel P. Niblett, Mirza Galib, David T. Limmer

J. Chem. Phys. 155, 164101 (2021)

https://doi.org/10.1063/5.0067565

Published: October 2021

View article titled, Learning intermolecular forces at liquid–vapor interfaces

Includes: Supplementary data

Journal Articles

Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides

Kohei Shimamura, Yusuke Takeshita, Shogo Fukushima, Akihide Koura, Fuyuki Shimojo

J. Chem. Phys. 153, 234301 (2020)

https://doi.org/10.1063/5.0027058

Published: December 2020

View article titled, Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides

Includes: Supplementary data

Journal Articles

Training atomic neural networks using fragment-based data generated in virtual reality

Silvia Amabilino, Lars A. Bratholm, Simon J. Bennie, Michael B. O’Connor, David R. Glowacki

J. Chem. Phys. 153, 154105 (2020)

https://doi.org/10.1063/5.0015950

Published: October 2020

View article titled, Training atomic neural networks using fragment-based data generated in virtual reality

Includes: Supplementary data

Journal Articles

Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials

Berk Onat, Christoph Ortner, James R. Kermode

J. Chem. Phys. 153, 144106 (2020)

https://doi.org/10.1063/5.0016005

Published: October 2020

View article titled, Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials

Journal Articles

Committee neural network potentials control generalization errors and enable active learning

Christoph Schran, Krystof Brezina, Ondrej Marsalek

J. Chem. Phys. 153, 104105 (2020)

https://doi.org/10.1063/5.0016004

Published: September 2020

View article titled, Committee neural network potentials control generalization errors and enable active learning

Includes: Supplementary data

NARROW

Topics

Splitting probabilities as optimal controllers of rare reactive events

Simulating multipulse NMR spectra of polycrystalline solids in the frequency domain

Amorphous MoS₂ from a machine learning inter-atomic potential

Classification of complex local environments in systems of particle shapes through shape symmetry-encoded data augmentation

Local-environment-guided selection of atomic structures for the development of machine-learning potentials

A geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure

First-principles molten salt phase diagrams through thermodynamic integration

Mapping of Hückel zigzag carbon nanotubes onto independent polyene chains: Application to periodic nanotubes

Optimizing the architecture of Behler–Parrinello neural network potentials

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions

Critical assessment of machine-learned repulsive potentials for the density functional based tight-binding method: A case study for pure silicon

Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites

Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions

Modeling of effective interactions between ligand coated nanoparticles through symmetry functions

Machine learning many-body potentials for colloidal systems

Learning intermolecular forces at liquid–vapor interfaces

Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides

Training atomic neural networks using fragment-based data generated in virtual reality

Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials

Committee neural network potentials control generalization errors and enable active learning

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