Machine learning for potential energy surfaces: An extensive database and assessment of methods

Approximate high mode coupling potentials using Gaussian process regression and adaptive density guided sampling

(2019) Gunnar Schmitz et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian process regression for geometry optimization

(2018) Alexander Denzel et al.   JOURNAL OF CHEMICAL PHYSICS

Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation

(2018) Gunnar Schmitz et al.   JOURNAL OF CHEMICAL PHYSICS

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen et al.   JOURNAL OF CHEMICAL PHYSICS

Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

(2018) Matthias Rupp et al.   JOURNAL OF CHEMICAL PHYSICS

MCTDH on-the-fly: Efficient grid-based quantum dynamics without pre-computed potential energy surfaces

(2018) Gareth W. Richings et al.   JOURNAL OF CHEMICAL PHYSICS

Atomic Energies from a Convolutional Neural Network

(2018) Xin Chen et al.   Journal of Chemical Theory and Computation

Force Field for Water Based on Neural Network

(2018) Hao Wang et al.   Journal of Physical Chemistry Letters

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

Machine learning enhanced global optimization by clustering local environments to enable bundled atomic energies

(2018) Søren A. Meldgaard et al.   JOURNAL OF CHEMICAL PHYSICS

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Discovery and Optimization of Materials Using Evolutionary Approaches

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

Potential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach

(2016) Bin Jiang et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

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

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

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

Permutationally Invariant Fitting of Many-Body, Non-covalent Interactions with Application to Three-Body Methane–Water–Water

(2015) Riccardo Conte et al.   Journal of Chemical Theory and Computation

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

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

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

An adaptive potential energy surface generation method using curvilinear valence coordinates

(2012) F. Richter et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

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

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

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

An adaptive density-guided approach for the generation of potential energy surfaces of polyatomic molecules

(2009) Manuel Sparta et al.   THEORETICAL CHEMISTRY ACCOUNTS

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

Systematically convergent basis sets for explicitly correlated wavefunctions: The atoms H, He, B–Ne, and Al–Ar

(2008) Kirk A. Peterson et al.   JOURNAL OF CHEMICAL PHYSICS