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Journal Articles

Transferable performance of machine learning potentials across graphene–water systems of different sizes: Insights from numerical metrics and physical characteristics

Dongfei Liu, Jianzhong Wu, Diannan Lu

J. Chem. Phys. 161, 194710 (2024)

https://doi.org/10.1063/5.0233395

Published: November 2024

View article titled, Transferable performance of machine learning potentials across graphene–water systems of different sizes: Insights from numerical metrics and physical characteristics

Journal Articles

Relative cooperativity in neutral and charged molecular clusters using QM/MM calculations

Jorge Nochebuena, Shubin Liu, G. Andrés Cisneros

J. Chem. Phys. 160, 134301 (2024)

https://doi.org/10.1063/5.0203020

Published: April 2024

View article titled, Relative cooperativity in neutral and charged molecular clusters using QM/MM calculations

Includes: Supplementary data

Journal Articles

Subspace recursive Fermi-operator expansion strategies for large-scale DFT eigenvalue problems on HPC architectures

Sameer Khadatkar, Phani Motamarri

J. Chem. Phys. 159, 031102 (2023)

https://doi.org/10.1063/5.0150287

Published: July 2023

View article titled, Subspace recursive Fermi-operator expansion strategies for large-scale DFT eigenvalue problems on HPC architectures

Journal Articles

Beyond independent error assumptions in large GNN atomistic models

Janghoon Ock, Tian Tian, John Kitchin, Zachary Ulissi

J. Chem. Phys. 158, 214702 (2023)

https://doi.org/10.1063/5.0151159

Published: June 2023

View article titled, Beyond independent error assumptions in large GNN atomistic models

Includes: Supplementary data

Journal Articles

A machine learning based computational approach for prediction of cation distribution in spinel crystal

Ying Fang, Paul R. Ohodnicki, Guofeng Wang

J. Chem. Phys. 158, 194102 (2023)

https://doi.org/10.1063/5.0146056

Published: May 2023

View article titled, A machine learning based computational approach for prediction of cation distribution in spinel crystal

Journal Articles

Relative energies without electronic perturbations via alchemical integral transform

Simon León Krug, Guido Falk von Rudorff, O. Anatole von Lilienfeld

J. Chem. Phys. 157, 164109 (2022)

https://doi.org/10.1063/5.0111511

Published: October 2022

View article titled, Relative energies without electronic perturbations via alchemical integral transform

Includes: Supplementary data

Journal Articles

Challenges in predicting Δ_rxnG in solution: The chelate effect

A. A. Mukadam, A. L. L. East

J. Chem. Phys. 157, 034109 (2022)

https://doi.org/10.1063/5.0097291

Published: July 2022

View article titled, Challenges in predicting ΔrxnG in solution: The chelate effect

Includes: Supplementary data

Journal Articles

Fermi–Löwdin orbital self-interaction correction of adsorption energies on transition metal ions

Kushantha P. K. Withanage, Kamal Sharkas, J. Karl Johnson, John P. Perdew, Juan E. Peralta, Koblar A. Jackson

J. Chem. Phys. 156, 134102 (2022)

https://doi.org/10.1063/5.0078970

Published: April 2022

View article titled, Fermi–Löwdin orbital self-interaction correction of adsorption energies on transition metal ions

Includes: Supplementary data

Journal Articles

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao, Michael B. O’Connor, Daniel G. A. Smith, Feizhi Ding, Peter J. Bygrave, Animashree Anandkumar, Matthew Welborn, Frederick R. Manby, Thomas F. Miller, III

J. Chem. Phys. 155, 204103 (2021)

https://doi.org/10.1063/5.0061990

Published: November 2021

View article titled, OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Includes: Supplementary data

Journal Articles

Entropy-based active learning of graph neural network surrogate models for materials properties

Johannes Allotey, Keith T. Butler, Jeyan Thiyagalingam

J. Chem. Phys. 155, 174116 (2021)

https://doi.org/10.1063/5.0065694

Published: November 2021

View article titled, Entropy-based active learning of graph neural network surrogate models for materials properties

Journal Articles

Accurate prediction of mutation-induced frequency shifts in chlorophyll proteins with a simple electrostatic model

Amit Srivastava, Safa Ahad, Jacob H. Wat, Mike Reppert

J. Chem. Phys. 155, 151102 (2021)

https://doi.org/10.1063/5.0064567

Published: October 2021

View article titled, Accurate prediction of mutation-induced frequency shifts in chlorophyll proteins with a simple electrostatic model

Includes: Supplementary data

Journal Articles

Spin-free formulation of the multireference driven similarity renormalization group: A benchmark study of first-row diatomic molecules and spin-crossover energetics

Chenyang Li, Francesco A. Evangelista

J. Chem. Phys. 155, 114111 (2021)

https://doi.org/10.1063/5.0059362

Published: September 2021

View article titled, Spin-free formulation of the multireference driven similarity renormalization group: A benchmark study of first-row diatomic molecules and spin-crossover energetics

Includes: Supplementary data

Journal Articles

Relative energetics of CH₃CH₂O, CH₃CHOH, and CH₂CH₂OH radical products from ethanol dehydrogenation

Ashley E. Williams, Nathan I. Hammer, Gregory S. Tschumper

J. Chem. Phys. 155, 114306 (2021)

https://doi.org/10.1063/5.0062809

Published: September 2021

View article titled, Relative energetics of CH3CH2O, CH3CHOH, and CH2CH2OH radical products from ethanol dehydrogenation

Includes: Supplementary data

Journal Articles

Engineered nanoparticle network models for autonomous computing

Xingfei Wei, Yinong Zhao, Yi Zhuang, Rigoberto Hernandez

J. Chem. Phys. 154, 214702 (2021)

https://doi.org/10.1063/5.0048898

Published: June 2021

View article titled, Engineered nanoparticle network models for autonomous computing

Includes: Supplementary data

Journal Articles

Analytical gradients for molecular-orbital-based machine learning

Sebastian J. R. Lee, Tamara Husch, Feizhi Ding, Thomas F. Miller, III

J. Chem. Phys. 154, 124120 (2021)

https://doi.org/10.1063/5.0040782

Published: March 2021

View article titled, Analytical gradients for molecular-orbital-based machine learning

Includes: Supplementary data

Journal Articles

Computational catalyst discovery: Active classification through myopic multiscale sampling

Kevin Tran, Willie Neiswanger, Kirby Broderick, Eric Xing, Jeff Schneider, Zachary W. Ulissi

J. Chem. Phys. 154, 124118 (2021)

https://doi.org/10.1063/5.0044989

Published: March 2021

View article titled, Computational catalyst discovery: Active classification through myopic multiscale sampling

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

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller, III

J. Chem. Phys. 153, 124111 (2020)

https://doi.org/10.1063/5.0021955

Published: September 2020

View article titled, OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

Includes: Supplementary data

Journal Articles

Wavelet scattering networks for atomistic systems with extrapolation of material properties

Paul Sinz, Michael W. Swift, Xavier Brumwell, Jialin Liu, Kwang Jin Kim, Yue Qi, Matthew Hirn

J. Chem. Phys. 153, 084109 (2020)

https://doi.org/10.1063/5.0016020

Published: August 2020

View article titled, Wavelet scattering networks for atomistic systems with extrapolation of material properties

Includes: Supplementary data

Journal Articles

Structural evolution in gold nanoparticles using artificial neural network based interatomic potentials

Shweta Jindal, Satya S. Bulusu

J. Chem. Phys. 152, 154302 (2020)

https://doi.org/10.1063/1.5142903

Published: April 2020

View article titled, Structural evolution in gold nanoparticles using artificial neural network based interatomic potentials

Includes: Supplementary data

NARROW

Topics

Transferable performance of machine learning potentials across graphene–water systems of different sizes: Insights from numerical metrics and physical characteristics

Relative cooperativity in neutral and charged molecular clusters using QM/MM calculations

Subspace recursive Fermi-operator expansion strategies for large-scale DFT eigenvalue problems on HPC architectures

Beyond independent error assumptions in large GNN atomistic models

A machine learning based computational approach for prediction of cation distribution in spinel crystal

Relative energies without electronic perturbations via alchemical integral transform

Challenges in predicting Δ_rxnG in solution: The chelate effect

Fermi–Löwdin orbital self-interaction correction of adsorption energies on transition metal ions

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Entropy-based active learning of graph neural network surrogate models for materials properties

Accurate prediction of mutation-induced frequency shifts in chlorophyll proteins with a simple electrostatic model

Spin-free formulation of the multireference driven similarity renormalization group: A benchmark study of first-row diatomic molecules and spin-crossover energetics

Relative energetics of CH₃CH₂O, CH₃CHOH, and CH₂CH₂OH radical products from ethanol dehydrogenation

Engineered nanoparticle network models for autonomous computing

Analytical gradients for molecular-orbital-based machine learning

Computational catalyst discovery: Active classification through myopic multiscale sampling

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

OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

Wavelet scattering networks for atomistic systems with extrapolation of material properties

Structural evolution in gold nanoparticles using artificial neural network based interatomic potentials

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