Efficient force field and energy emulation through partition of permutationally equivalent atoms

Efficient selection of linearly independent atomic features for accurate machine learning potentials

(2021) Jun-fan Xia et al.   CHINESE JOURNAL OF CHEMICAL PHYSICS

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects

(2021) Oliver T. Unke et al.   Nature Communications

FCHL revisited: Faster and more accurate quantum machine learning

(2020) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

Permutationally Invariant, Reproducing Kernel-Based Potential Energy Surfaces for Polyatomic Molecules: From Formaldehyde to Acetone

(2020) Debasish Koner et al.   Journal of Chemical Theory and Computation

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy

(2020) Denghui Lu et al.   COMPUTER PHYSICS COMMUNICATIONS

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

(2019) Yaolong Zhang et al.   Journal of Physical Chemistry Letters

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Machine Learning a General-Purpose Interatomic Potential for Silicon

(2018) Albert P. Bartók et al.   Physical Review X

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Parallel partial Gaussian process emulation for computer models with massive output

(2016) Mengyang Gu et al.   Annals of Applied Statistics

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

(2016) Abhirup Datta et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Taking the Human Out of the Loop: A Review of Bayesian Optimization

(2016) Bobak Shahriari et al.   PROCEEDINGS OF THE IEEE

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Local Gaussian Process Approximation for Large Computer Experiments

(2015) Robert B. Gramacy et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

DiceKriging,DiceOptim: TwoRPackages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

(2015) Olivier Roustant et al.   Journal of Statistical Software

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions

(2014) Bin Jiang et al.   JOURNAL OF CHEMICAL PHYSICS

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

(2011) Finn Lindgren et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Fixed rank kriging for very large spatial data sets

(2010) Noel Cressie et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Permutationally Invariant Polynomial Basis for Molecular Energy Surface Fitting via Monomial Symmetrization

(2009) Zhen Xie et al.   Journal of Chemical Theory and Computation

Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets

(2009) Cari G. Kaufman et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION