Parallel Implementation of Nonadditive Gaussian Process Potentials for Monte Carlo Simulations
暂无分享,去创建一个
[1] J. Bowman,et al. Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods. , 2021, The Journal of chemical physics.
[2] Yaolong Zhang,et al. REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems. , 2021, The Journal of chemical physics.
[3] Richard S. Graham,et al. Gaussian process models of potential energy surfaces with boundary optimization. , 2021, The Journal of chemical physics.
[4] R. Sadus,et al. Interatomic Interactions Responsible for the Solid-Liquid and Vapor-Liquid Phase Equilibria of Neon. , 2021, The journal of physical chemistry. B.
[5] Klaus-Robert Müller,et al. SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects , 2021, Nature Communications.
[6] Ulf R. Pedersen,et al. Solid-liquid coexistence of neon, argon, krypton, and xenon studied by simulations. , 2020, The Journal of chemical physics.
[7] Gábor Csányi,et al. Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3. , 2020, The Journal of chemical physics.
[8] R. Krems,et al. Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer. , 2020, The Journal of chemical physics.
[9] M. Sansom,et al. Water in Nanopores and Biological Channels: A Molecular Simulation Perspective , 2020, Chemical reviews.
[10] Mohamed Ali Boussaidi,et al. Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for Multivariate Function Representation: Application to Molecular Potential Energy Surfaces. , 2020, The journal of physical chemistry. A.
[11] Ove Christiansen,et al. A Gaussian process regression adaptive density guided approach for potential energy surface construction. , 2020, The Journal of chemical physics.
[12] Gábor Csányi,et al. Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide , 2020, npj Computational Materials.
[13] Matthew J Burn,et al. Creating Gaussian process regression models for molecular simulations using adaptive sampling. , 2020, The Journal of chemical physics.
[14] Tim Mueller,et al. Machine learning for interatomic potential models. , 2020, The Journal of chemical physics.
[15] R. Krems,et al. Interpolation and extrapolation of global potential energy surfaces for polyatomic systems by Gaussian processes with composite kernels. , 2019, Journal of chemical theory and computation.
[16] R. Sadus,et al. Fully a priori prediction of the vapor-liquid equilibria of Ar, Kr, and Xe from ab initio two-body plus three-body interatomic potentials. , 2019, The Journal of chemical physics.
[17] J. Behler,et al. A Performance and Cost Assessment of Machine Learning Interatomic Potentials. , 2019, The journal of physical chemistry. A.
[18] Elena Uteva,et al. Active learning in Gaussian process interpolation of potential energy surfaces. , 2018, The Journal of chemical physics.
[19] Aditya Kamath,et al. Neural networks vs Gaussian process regression for representing potential energy surfaces: A comparative study of fit quality and vibrational spectrum accuracy. , 2018, The Journal of chemical physics.
[20] Mark S. Gordon,et al. Perspective: Ab initio force field methods derived from quantum mechanics , 2018 .
[21] Nicola Gaston,et al. Building machine learning force fields for nanoclusters. , 2018, The Journal of chemical physics.
[22] Richard D Wilkinson,et al. Interpolation of intermolecular potentials using Gaussian processes. , 2017, The Journal of chemical physics.
[23] J. Behler. Perspective: Machine learning potentials for atomistic simulations. , 2016, The Journal of chemical physics.
[24] T. Ilyina. Climate science: Hidden trends in the ocean carbon sink , 2016, Nature.
[25] Jae-Young Choi,et al. Selective Gas Transport Through Few-Layered Graphene and Graphene Oxide Membranes , 2013, Science.
[26] Joshua D. Knowles,et al. Accuracy and tractability of a kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine , 2013, J. Comput. Chem..
[27] Paul L. A. Popelier,et al. Polarisable multipolar electrostatics from the machine learning method Kriging: an application to alanine , 2012, Theoretical Chemistry Accounts.
[28] P. Popelier,et al. Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging , 2011 .
[29] Douglas B Kell,et al. Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning. , 2009, Physical chemistry chemical physics : PCCP.
[30] Richard J. Beckman,et al. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.
[31] Wei-Liem Loh. On Latin hypercube sampling , 1996 .
[32] M. Stein. Large sample properties of simulations using latin hypercube sampling , 1987 .
[33] C. Brooks. Computer simulation of liquids , 1989 .