Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation

Discovery and Optimization of Materials Using Evolutionary Approaches

(2016) Tu C. Le et al.   CHEMICAL REVIEWS

Efficient and accurate evaluation of potential energy matrix elements for quantum dynamics using Gaussian process regression

(2016) Jonathan P. Alborzpour et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning

(2016) Timothy L. Fletcher et al.   Journal of Chemical Theory and Computation

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression

(2016) Letif Mones et al.   Journal of Chemical Theory and Computation

Efficient non-parametric fitting of potential energy surfaces for polyatomic molecules with Gaussian processes

(2016) Jie Cui et al.   JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS

Machine learning for quantum mechanics in a nutshell

(2015) Matthias Rupp   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

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

Gaussian process model for extrapolation of scattering observables for complex molecules: From benzene to benzonitrile

(2015) Jie Cui et al.   JOURNAL OF CHEMICAL PHYSICS

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Gaussian Process Model for Collision Dynamics of Complex Molecules

(2015) Jie Cui et al.   PHYSICAL REVIEW LETTERS

Neural network-based approaches for building high dimensional and quantum dynamics-friendly potential energy surfaces

(2014) Sergei Manzhos et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Structure and properties of bimetallic titanium and vanadium oxide clusters

(2014) Benjamin Helmich et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Communication: An accurate global potential energy surface for the OH + CO → H + CO2 reaction using neural networks

(2013) Jun Chen et al.   JOURNAL OF CHEMICAL PHYSICS

Accuracy and tractability of a kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine

(2013) Shaun M. Kandathil et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

On representing chemical environments

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

New developments in evolutionary structure prediction algorithm USPEX

(2012) Andriy O. Lyakhov et al.   COMPUTER PHYSICS COMMUNICATIONS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Polarisable multipolar electrostatics from the machine learning method Kriging: an application to alanine

(2012) Matthew J. L. Mills et al.   THEORETICAL CHEMISTRY ACCOUNTS

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

How to predict very large and complex crystal structures

(2010) Andriy O. Lyakhov et al.   COMPUTER PHYSICS COMMUNICATIONS

A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

(2010) Stefan Grimme et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Synergy between theory and experiment in structure resolution of low-dimensional oxides

(2010) Marek Sierka   PROGRESS IN SURFACE SCIENCE

Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning

(2009) Chris M. Handley et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Choosing the Sample Size of a Computer Experiment: A Practical Guide

(2009) Jason L. Loeppky et al.   TECHNOMETRICS

Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface

(2008) Sergei Manzhos et al.   JOURNAL OF CHEMICAL PHYSICS