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Richard A Messerly

Showing results (1-10 of 11) with videos related to

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The Journal of Chemical Physics|May 22, 2017
Uncertainty quantification and propagation of errors of the Lennard-Jones 12-6 parameters for n-alkanesRichard A Messerly, Thomas A Knotts, W Vincent Wilding
The Journal of Chemical Physics|September 24, 2018
Uncertainty quantification confirms unreliable extrapolation toward high pressures for united-atom Mie <i>λ</i>-6 force fieldRichard A Messerly, Michael R Shirts, Andrei F Kazakov
Journal of Chemical Theory and Computation|May 5, 2018
Configuration-Sampling-Based Surrogate Models for Rapid Parameterization of Non-Bonded InteractionsRichard A Messerly, S Mostafa Razavi, Michael R Shirts
Journal of Chemical Information and Modeling|April 29, 2025
Including Physics-Informed Atomization Constraints in Neural Networks for Reactive ChemistryShuhao Zhang, Michael Chigaev, Olexandr Isayev, et al.
The Journal of Chemical Physics|September 17, 2015
An improved statistical analysis for predicting the critical temperature and critical density with Gibbs ensemble Monte Carlo simulationRichard A Messerly, Richard L Rowley, Thomas A Knotts, et al.
Journal of Chemical and Engineering Data|October 12, 2020
Molecular Calculation of the Critical Parameters of Classical HeliumRichard A Messerly, Navneeth Gokul, Andrew J Schultz, et al.
Journal of Chemical Information and Modeling|February 7, 2022
Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid ModelsOwen C Madin, Simon Boothroyd, Richard A Messerly, et al.
Chemical Science|August 27, 2021
Predicting phosphorescence energies and inferring wavefunction localization with machine learningAndrew E Sifain, Levi Lystrom, Richard A Messerly, et al.
Journal of Chemical Information and Modeling|January 28, 2025
Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data AugmentationLuan G F Dos Santos, Benjamin T Nebgen, Alice E A Allen, et al.
Nature Chemistry|March 7, 2024
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potentialShuhao Zhang, Małgorzata Z Makoś, Ryan B Jadrich, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
The Journal of Chemical Physics|May 22, 2017
Uncertainty quantification and propagation of errors of the Lennard-Jones 12-6 parameters for n-alkanesRichard A Messerly, Thomas A Knotts, W Vincent Wilding
The Journal of Chemical Physics|September 24, 2018
Uncertainty quantification confirms unreliable extrapolation toward high pressures for united-atom Mie <i>λ</i>-6 force fieldRichard A Messerly, Michael R Shirts, Andrei F Kazakov
Journal of Chemical Theory and Computation|May 5, 2018
Configuration-Sampling-Based Surrogate Models for Rapid Parameterization of Non-Bonded InteractionsRichard A Messerly, S Mostafa Razavi, Michael R Shirts
Journal of Chemical Information and Modeling|April 29, 2025
Including Physics-Informed Atomization Constraints in Neural Networks for Reactive ChemistryShuhao Zhang, Michael Chigaev, Olexandr Isayev, et al.
The Journal of Chemical Physics|September 17, 2015
An improved statistical analysis for predicting the critical temperature and critical density with Gibbs ensemble Monte Carlo simulationRichard A Messerly, Richard L Rowley, Thomas A Knotts, et al.
Journal of Chemical and Engineering Data|October 12, 2020
Molecular Calculation of the Critical Parameters of Classical HeliumRichard A Messerly, Navneeth Gokul, Andrew J Schultz, et al.
Journal of Chemical Information and Modeling|February 7, 2022
Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid ModelsOwen C Madin, Simon Boothroyd, Richard A Messerly, et al.
Chemical Science|August 27, 2021
Predicting phosphorescence energies and inferring wavefunction localization with machine learningAndrew E Sifain, Levi Lystrom, Richard A Messerly, et al.
Journal of Chemical Information and Modeling|January 28, 2025
Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data AugmentationLuan G F Dos Santos, Benjamin T Nebgen, Alice E A Allen, et al.
Nature Chemistry|March 7, 2024
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potentialShuhao Zhang, Małgorzata Z Makoś, Ryan B Jadrich, et al.
Pageof 2