wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster

(2017) Shweta Jindal et al.   JOURNAL OF CHEMICAL PHYSICS

The many-body expansion combined with neural networks

(2017) Kun Yao et al.   JOURNAL OF CHEMICAL PHYSICS

Learning molecular energies using localized graph kernels

(2017) Grégoire Ferré 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

Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

(2017) Jon Paul Janet et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

(2017) Nicholas J. Browning et al.   Journal of Physical Chemistry Letters

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Neural network potential for Al-Mg-Si alloys

(2017) Ryo Kobayashi et al.   PHYSICAL REVIEW MATERIALS

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

Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes

(2016) Michael Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

(2016) Bing Huang et al.   JOURNAL OF CHEMICAL PHYSICS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

(2016) Rafael Gómez-Bombarelli et al.   NATURE MATERIALS

Machine learning exciton dynamics

(2016) Florian Häse et al.   Chemical Science

Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery

(2015) Edward O. Pyzer-Knapp et al.   ADVANCED FUNCTIONAL MATERIALS

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

(2015) O. Anatole von Lilienfeld et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

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

High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm

(2015) Michael Gastegger et al.   Journal of Chemical Theory and Computation

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Metrics for measuring distances in configuration spaces

(2013) Ali Sadeghi et al.   JOURNAL OF CHEMICAL PHYSICS

Permutation invariant polynomial neural network approach to fitting potential energy surfaces

(2013) Bin Jiang 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

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

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