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Journal Articles

X marks the spot: Accurate energies from intersecting extrapolations of continuum quantum Monte Carlo data

Seyed Mohammadreza Hosseini, Ali Alavi, Pablo López Ríos

J. Chem. Phys. 160, 194104 (2024)

https://doi.org/10.1063/5.0206478

Published: May 2024

View article titled, X marks the spot: Accurate energies from intersecting extrapolations of continuum quantum Monte Carlo data

Journal Articles

Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks

Pablo Montero de Hijes, Christoph Dellago, Ryosuke Jinnouchi, Bernhard Schmiedmayer, Georg Kresse

J. Chem. Phys. 160, 114107 (2024)

https://doi.org/10.1063/5.0197105

Published: March 2024

View article titled, Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks

Journal Articles

A slow feature analysis approach for the optimization of collective variables

Shuai Gong, Zheng Zheng

J. Chem. Phys. 160, 094104 (2024)

https://doi.org/10.1063/5.0191014

Published: March 2024

View article titled, A slow feature analysis approach for the optimization of collective variables

Includes: Supplementary data

Journal Articles

Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics

Zun Wang, Hongfei Wu, Lixin Sun, Xinheng He, Zhirong Liu, Bin Shao, Tong Wang, Tie-Yan Liu

J. Chem. Phys. 159, 035101 (2023)

https://doi.org/10.1063/5.0147023

Published: July 2023

View article titled, Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics

Includes: Supplementary data

Journal Articles

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Nawavi Naleem, Charlles R. A. Abreu, Krzysztof Warmuz, Muchen Tong, Serdal Kirmizialtin, Mark E. Tuckerman

J. Chem. Phys. 159, 034102 (2023)

https://doi.org/10.1063/5.0147597

Published: July 2023

View article titled, An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Includes: Supplementary data

Journal Articles

Linear scaling computation of forces for the domain-decomposition linear Poisson–Boltzmann method

Abhinav Jha, Michele Nottoli, Aleksandr Mikhalev, Chaoyu Quan, Benjamin Stamm

J. Chem. Phys. 158, 104105 (2023)

https://doi.org/10.1063/5.0141025

Published: March 2023

View article titled, Linear scaling computation of forces for the domain-decomposition linear Poisson–Boltzmann method

Journal Articles

The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces

Sergei Manzhos, Manabu Ihara

J. Chem. Phys. 158, 044111 (2023)

https://doi.org/10.1063/5.0136156

Published: January 2023

View article titled, The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces

Includes: Supplementary data

Journal Articles

Reliable emulation of complex functionals by active learning with error control

Xinyi Fang, Mengyang Gu, Jianzhong Wu

J. Chem. Phys. 157, 214109 (2022)

https://doi.org/10.1063/5.0121805

Published: December 2022

View article titled, Reliable emulation of complex functionals by active learning with error control

Includes: Supplementary data

Journal Articles

Measuring proteins in H₂O using 2D-IR spectroscopy: pre-processing steps and applications toward a protein library

Samantha H. Rutherford, Gregory M. Greetham, Anthony W. Parker, Alison Nordon, Matthew J. Baker, Neil T. Hunt

J. Chem. Phys. 157, 205102 (2022)

https://doi.org/10.1063/5.0127680

Published: November 2022

View article titled, Measuring proteins in H<sub>2</sub>O using 2D-IR spectroscopy: pre-processing steps and applications toward a protein library

Includes: Supplementary data

Journal Articles

Bypassing backmapping: Coarse-grained electronic property distributions using heteroscedastic Gaussian processes

J. Charlie Maier, Nicholas E. Jackson

J. Chem. Phys. 157, 174102 (2022)

https://doi.org/10.1063/5.0101038

Published: November 2022

View article titled, Bypassing backmapping: Coarse-grained electronic property distributions using heteroscedastic Gaussian processes

Includes: Supplementary data

Journal Articles

Resolving the odd–even oscillation of water dissociation at rutile TiO₂(110)–water interface by machine learning accelerated molecular dynamics

Yong-Bin Zhuang, Rui-Hao Bi, Jun Cheng

J. Chem. Phys. 157, 164701 (2022)

https://doi.org/10.1063/5.0126333

Published: October 2022

View article titled, Resolving the odd–even oscillation of water dissociation at rutile TiO<sub>2</sub>(110)–water interface by machine learning accelerated molecular dynamics

Includes: Supplementary data

Journal Articles

Benchmarking CASPT3 vertical excitation energies

Martial Boggio-Pasqua, Denis Jacquemin, Pierre-François Loos

J. Chem. Phys. 157, 014103 (2022)

https://doi.org/10.1063/5.0095887

Published: July 2022

View article titled, Benchmarking CASPT3 vertical excitation energies

Includes: Supplementary data

Journal Articles

Tailoring nanoporous graphene via machine learning: Predicting probabilities and formation times of arbitrary nanopore shapes

Rahul Sheshanarayana, Ananth Govind Rajan

J. Chem. Phys. 156, 204703 (2022)

https://doi.org/10.1063/5.0089469

Published: May 2022

View article titled, Tailoring nanoporous graphene via machine learning: Predicting probabilities and formation times of arbitrary nanopore shapes

Includes: Supplementary data

Journal Articles

Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen

Emily A. Eikey, Alex M. Maldonado, Charles D. Griego, Guido Falk von Rudorff, John A. Keith

J. Chem. Phys. 156, 204111 (2022)

https://doi.org/10.1063/5.0079487

Published: May 2022

View article titled, Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen

Includes: Supplementary data

Journal Articles

Basis-set correction for coupled-cluster estimation of dipole moments

Diata Traore, Julien Toulouse, Emmanuel Giner

J. Chem. Phys. 156, 174101 (2022)

https://doi.org/10.1063/5.0087794

Published: May 2022

View article titled, Basis-set correction for coupled-cluster estimation of dipole moments

Includes: Supplementary data

Journal Articles

A rapid feature selection method for catalyst design: Iterative Bayesian additive regression trees (iBART)

Chun-Yen Liu, Shengbin Ye, Meng Li, Thomas P. Senftle

J. Chem. Phys. 156, 164105 (2022)

https://doi.org/10.1063/5.0090055

Published: April 2022

View article titled, A rapid feature selection method for catalyst design: Iterative Bayesian additive regression trees (iBART)

Includes: Supplementary data

Journal Articles

Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)

Takuma Kikutsuji, Yusuke Mori, Kei-ichi Okazaki, Toshifumi Mori, Kang Kim, Nobuyuki Matubayasi

J. Chem. Phys. 156, 154108 (2022)

https://doi.org/10.1063/5.0087310

Published: April 2022

View article titled, Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)

Journal Articles

Small angle scattering of diblock copolymers profiled by machine learning

Chi-Huan Tung, Shou-Yi Chang, Hsin-Lung Chen, Yangyang Wang, Kunlun Hong, Jan Michael Carrillo, Bobby G. Sumpter, Yuya Shinohara, Changwoo Do, Wei-Ren Chen

J. Chem. Phys. 156, 131101 (2022)

https://doi.org/10.1063/5.0086311

Published: April 2022

View article titled, Small angle scattering of diblock copolymers profiled by machine learning

Journal Articles

Benchmarking isotropic hyperfine coupling constants using (QTP) DFT functionals and coupled cluster theory

Zachary W. Windom, Ajith Perera, Rodney J. Bartlett

J. Chem. Phys. 156, 094107 (2022)

https://doi.org/10.1063/5.0069928

Published: March 2022

View article titled, Benchmarking isotropic hyperfine coupling constants using (QTP) DFT functionals and coupled cluster theory

Includes: Supplementary data

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

NARROW

Topics

X marks the spot: Accurate energies from intersecting extrapolations of continuum quantum Monte Carlo data

Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks

A slow feature analysis approach for the optimization of collective variables

Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics

An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions

Linear scaling computation of forces for the domain-decomposition linear Poisson–Boltzmann method

The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces

Reliable emulation of complex functionals by active learning with error control

Measuring proteins in H₂O using 2D-IR spectroscopy: pre-processing steps and applications toward a protein library

Bypassing backmapping: Coarse-grained electronic property distributions using heteroscedastic Gaussian processes

Resolving the odd–even oscillation of water dissociation at rutile TiO₂(110)–water interface by machine learning accelerated molecular dynamics

Benchmarking CASPT3 vertical excitation energies

Tailoring nanoporous graphene via machine learning: Predicting probabilities and formation times of arbitrary nanopore shapes

Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen

Basis-set correction for coupled-cluster estimation of dipole moments

A rapid feature selection method for catalyst design: Iterative Bayesian additive regression trees (iBART)

Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI)

Small angle scattering of diblock copolymers profiled by machine learning

Benchmarking isotropic hyperfine coupling constants using (QTP) DFT functionals and coupled cluster theory

Modeling of effective interactions between ligand coated nanoparticles through symmetry functions

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