Page 1 | Search Results | The Journal of Chemical Physics

1-20 of 41

Sort by

Sort Order Select

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

A neural network potential with self-trained atomic fingerprints: A test with the mW water potential

Francesco Guidarelli Mattioli, Francesco Sciortino, John Russo

J. Chem. Phys. 158, 104501 (2023)

https://doi.org/10.1063/5.0139245

Published: March 2023

View article titled, A neural network potential with self-trained atomic fingerprints: A test with the mW water potential

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

Journal Articles

Modeling of many-body interactions between elastic spheres through symmetry functions

Emanuele Boattini, Nina Bezem, Sudeep N. Punnathanam, Frank Smallenburg, Laura Filion

J. Chem. Phys. 153, 064902 (2020)

https://doi.org/10.1063/5.0015606

Published: August 2020

View article titled, Modeling of many-body interactions between elastic spheres through symmetry functions

Journal Articles

An accurate machine-learning calculator for optimization of Li-ion battery cathodes

Gregory Houchins, Venkatasubramanian Viswanathan

J. Chem. Phys. 153, 054124 (2020)

https://doi.org/10.1063/5.0015872

Published: August 2020

View article titled, An accurate machine-learning calculator for optimization of Li-ion battery cathodes

Includes: Supplementary data

Journal Articles

Pair-distribution-function guided optimization of fingerprints for atom-centered neural network potentials

Lei Li, Hao Li, Ieuan D. Seymour, Lucas Koziol, Graeme Henkelman

J. Chem. Phys. 152, 224102 (2020)

https://doi.org/10.1063/5.0007391

Published: June 2020

View article titled, Pair-distribution-function guided optimization of fingerprints for atom-centered neural network potentials

Includes: Supplementary data

Journal Articles

Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory

Derek P. Metcalf, Alexios Koutsoukas, Steven A. Spronk, Brian L. Claus, Deborah A. Loughney, Stephen R. Johnson, Daniel L. Cheney, C. David Sherrill

J. Chem. Phys. 152, 074103 (2020)

https://doi.org/10.1063/1.5142636

Published: February 2020

View article titled, Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory

Includes: Supplementary data

Journal Articles

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α -Ag₂Se

Kohei Shimamura, Shogo Fukushima, Akihide Koura, Fuyuki Shimojo, Masaaki Misawa, Rajiv K. Kalia, Aiichiro Nakano, Priya Vashishta, Takashi Matsubara, Shigenori Tanaka

J. Chem. Phys. 151, 124303 (2019)

https://doi.org/10.1063/1.5116420

Published: September 2019

View article titled, Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α -Ag<sub>2</sub>Se

Includes: Supplementary data

Journal Articles

A novel approach to describe chemical environments in high-dimensional neural network potentials

Emir Kocer, Jeremy K. Mason, Hakan Erturk

J. Chem. Phys. 150, 154102 (2019)

https://doi.org/10.1063/1.5086167

Published: April 2019

View article titled, A novel approach to describe chemical environments in high-dimensional neural network potentials

Includes: Supplementary data

NARROW

Topics

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

Optimizing the architecture of Behler–Parrinello neural network potentials

A neural network potential with self-trained atomic fingerprints: A test with the mW water potential

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

Modeling of many-body interactions between elastic spheres through symmetry functions

An accurate machine-learning calculator for optimization of Li-ion battery cathodes

Pair-distribution-function guided optimization of fingerprints for atom-centered neural network potentials

Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α -Ag₂Se

A novel approach to describe chemical environments in high-dimensional neural network potentials

Resources

Explore

pubs.aip.org

Connect with AIP Publishing

Resources

Explore

pubs.aip.org

Connect with AIP Publishing

This Feature Is Available To Subscribers Only