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Journal Articles

Exploring torsional conformer space with physical prior mean function-driven meta-Gaussian processes

Chong Teng, Daniel Huang, Elizabeth Donahue, Junwei Lucas Bao
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 214111 (2023)
https://doi.org/10.1063/5.0176709
Published: December 2023
View article titled, Exploring torsional conformer space with physical prior mean function-driven meta-Gaussian processes
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Includes: Supplementary data
Journal Articles

Gaussian approximation potentials: Theory, software implementation and application examples

Sascha Klawohn, James P. Darby, James R. Kermode, Gábor Csányi, Miguel A. Caro, Albert P. Bartók
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 174108 (2023)
https://doi.org/10.1063/5.0160898
Published: November 2023
View article titled, Gaussian approximation potentials: Theory, software implementation and application examples
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Includes: Supplementary data
Journal Articles

Different glassy characteristics are related to either caging or dynamical heterogeneity

Puneet Pareek, Monoj Adhikari, Chandan Dasgupta, Saroj Kumar Nandi
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 174503 (2023)
https://doi.org/10.1063/5.0166404
Published: November 2023
View article titled, Different glassy characteristics are related to either caging or dynamical heterogeneity
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Journal Articles

Molecular dynamics-driven global potential energy surfaces: Application to the AlF dimer

Xiangyue Liu, Weiqi Wang, Jesús Pérez-Ríos
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 144103 (2023)
https://doi.org/10.1063/5.0169080
Published: October 2023
View article titled, Molecular dynamics-driven global potential energy surfaces: Application to the AlF dimer
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Journal Articles

Nuclear induction lineshape modeling via hybrid SDE and MD approach

Mohamad Niknam, Louis-S. Bouchard
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 124201 (2023)
https://doi.org/10.1063/5.0163782
Published: September 2023
View article titled, Nuclear induction lineshape modeling via hybrid SDE and MD approach
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Journal Articles

Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation

Ryan Snyder, Bryant Kim, Xiaoliang Pan, Yihan Shao, Jingzhi Pu
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 054107 (2023)
https://doi.org/10.1063/5.0156327
Published: August 2023
View article titled, Bridging semiempirical and <em>ab initio</em> QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation
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Includes: Supplementary data
Journal Articles

Comparison of multifidelity machine learning models for potential energy surfaces

Stephen M. Goodlett, Justin M. Turney, Henry F. Schaefer, III
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 044111 (2023)
https://doi.org/10.1063/5.0158919
Published: July 2023
View article titled, Comparison of multifidelity machine learning models for potential energy surfaces
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Journal Articles

Analysis of photon trajectories from diffusing single molecules

Irina V. Gopich, Jae-Yeol Kim, Hoi Sung Chung
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 024119 (2023)
https://doi.org/10.1063/5.0153114
Published: July 2023
View article titled, Analysis of photon trajectories from diffusing single molecules
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Journal Articles

Symmetry- and gradient-enhanced Gaussian process regression for the active learning of potential energy surfaces in porous materials

Johannes K. Krondorfer, Christian W. Binder, Andreas W. Hauser
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 014115 (2023)
https://doi.org/10.1063/5.0154989
Published: July 2023
View article titled, Symmetry- and gradient-enhanced Gaussian process regression for the active learning of potential energy surfaces in porous materials
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Journal Articles

Predicting the electronic density response of condensed-phase systems to electric field perturbations

Alan M. Lewis, Paolo Lazzaroni, Mariana Rossi
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 014103 (2023)
https://doi.org/10.1063/5.0154710
Published: July 2023
View article titled, Predicting the electronic density response of condensed-phase systems to electric field perturbations
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Includes: Supplementary data
Journal Articles

Mode-coupling theory for mixtures of athermal self-propelled particles

Vincent E. Debets, Liesbeth M. C. Janssen
Journal: The Journal of Chemical Physics
J. Chem. Phys. 159, 014502 (2023)
https://doi.org/10.1063/5.0155142
Published: July 2023
View article titled, Mode-coupling theory for mixtures of athermal self-propelled particles
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Journal Articles

Black box vs gray box: Comparing GAP and GPrep-DFTB for ruthenium and ruthenium oxide

C. Panosetti, Y. Lee, A. Samtsevych, C. Scheurer
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 224115 (2023)
https://doi.org/10.1063/5.0141233
Published: June 2023
View article titled, Black box vs gray box: Comparing GAP and GPrep-DFTB for ruthenium and ruthenium oxide
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Journal Articles

Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data

William Z. Van Benschoten, Laura Weiler, Gabriel J. Smith, Songhang Man, Taylor DeMello, James J. Shepherd
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 214115 (2023)
https://doi.org/10.1063/5.0150702
Published: June 2023
View article titled, Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data
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Journal Articles

Exciton diffusion in amorphous organic semiconductors: Reducing simulation overheads with machine learning

Chayanit Wechwithayakhlung, Geoffrey R. Weal, Yu Kaneko, Paul A. Hume, Justin M. Hodgkiss, Daniel M. Packwood
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 204106 (2023)
https://doi.org/10.1063/5.0144573
Published: May 2023
View article titled, Exciton diffusion in amorphous organic semiconductors: Reducing simulation overheads with machine learning
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Includes: Supplementary data
Journal Articles

NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule

Mikhail Tsitsvero, Jenny Pirillo, Yuh Hijikata, Tamiki Komatsuzaki
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 194108 (2023)
https://doi.org/10.1063/5.0147398
Published: May 2023
View article titled, NMR spectrum prediction for dynamic molecules by machine learning: A case study of trefoil knot molecule
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Includes: Supplementary data
Journal Articles

Nuclear magnetic resonance signal decay in the presence of a background gradient: Normal and anomalous diffusion

G. Costantini, S. Capuani, F. A. Farrelly, A. Taloni
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 174106 (2023)
https://doi.org/10.1063/5.0148175
Published: May 2023
View article titled, Nuclear magnetic resonance signal decay in the presence of a background gradient: Normal and anomalous diffusion
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Journal Articles

Leveraging uncertainty estimates and derivative information in Gaussian process regression for efficient collection and use of molecular simulation data

Jacob I. Monroe, William P. Krekelberg, Austin McDannald, Vincent K. Shen
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 164110 (2023)
https://doi.org/10.1063/5.0148488
Published: April 2023
View article titled, Leveraging uncertainty estimates and derivative information in Gaussian process regression for efficient collection and use of molecular simulation data
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Includes: Supplementary data
Journal Articles

A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles

Jan Kloppenburg, Livia B. Pártay, Hannes Jónsson, Miguel A. Caro
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 134704 (2023)
https://doi.org/10.1063/5.0143891
Published: April 2023
View article titled, A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles
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Journal Articles

SAMPL9 blind predictions for toluene/water partition coefficients using nonequilibrium alchemical approaches

Piero Procacci, Guido Guarnieri
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 124117 (2023)
https://doi.org/10.1063/5.0143824
Published: March 2023
View article titled, SAMPL9 blind predictions for toluene/water partition coefficients using nonequilibrium alchemical approaches
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Includes: Supplementary data
Journal Articles

Efficient Langevin and Monte Carlo sampling algorithms: The case of field-theoretic simulations

Bart Vorselaars
Journal: The Journal of Chemical Physics
J. Chem. Phys. 158, 114117 (2023)
https://doi.org/10.1063/5.0131183
Published: March 2023
View article titled, Efficient Langevin and Monte Carlo sampling algorithms: The case of field-theoretic simulations
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