Fast, accurate, and transferable many-body interatomic potentials by symbolic regression

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

(2019) Henry Chan et al.   Journal of Physical Chemistry C

Machine-learned multi-system surrogate models for materials prediction

(2019) Chandramouli Nyshadham et al.   npj Computational Materials

Extending the accuracy of the SNAP interatomic potential form

(2018) Mitchell A. Wood 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

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

(2018) Kun Yao et al.   Chemical Science

The Use of Cluster Expansions to Predict the Structures and Properties of Surfaces and Nanostructured Materials

(2018) Liang Cao et al.   Journal of Chemical Information and Modeling

Machine Learning Force Field Parameters from Ab Initio Data

(2017) Ying Li et al.   Journal of Chemical Theory and Computation

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

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

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

A universal strategy for the creation of machine learning-based atomistic force fields

(2017) Tran Doan Huan et al.   npj Computational Materials

Accurate force field for molybdenum by machine learning large materials data

(2017) Chi Chen et al.   PHYSICAL REVIEW MATERIALS

Identifying models of dielectric breakdown strength from high-throughput data via genetic programming

(2017) Fenglin Yuan et al.   Scientific Reports

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Ab Initio-Based Bond Order Potential to Investigate Low Thermal Conductivity of Stanene Nanostructures

(2016) Mathew J. Cherukara et al.   Journal of Physical Chemistry Letters

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

First-principles interatomic potentials for ten elemental metals via compressed sensing

(2015) Atsuto Seko et al.   PHYSICAL REVIEW B

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Towards accurate prediction of catalytic activity in IrO2 nanoclusters via first principles-based variable charge force field

(2015) F. G. Sen et al.   Journal of Materials Chemistry A

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Origins of hole traps in hydrogenated nanocrystalline and amorphous silicon revealed through machine learning

(2014) Tim Mueller et al.   PHYSICAL REVIEW B

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta et al.   PHYSICAL REVIEW B

Considerations for choosing and using force fields and interatomic potentials in materials science and engineering

(2013) Chandler A. Becker et al.   CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE

Global optimization of parameters in the reactive force field ReaxFF for SiOH

(2013) Henrik R. Larsson et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Concepts for simulating and understanding materials at the atomic scale

(2012) M.W. Finnis   MRS BULLETIN

Three decades of many-body potentials in materials research

(2012) Susan B. Sinnott et al.   MRS BULLETIN

Computational aspects of many-body potentials

(2012) Steven J. Plimpton et al.   MRS BULLETIN

The interactions of self-interstitials with twin boundaries

(2012) M.I. Mendelev et al.   PHILOSOPHICAL MAGAZINE

High-dimensional neural network potentials for metal surfaces: A prototype study for copper

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Determination of best-fit potential parameters for a reactive force field using a genetic algorithm

(2011) Poonam Pahari et al.   JOURNAL OF MOLECULAR MODELING

Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

(2011) Roman M. Balabin et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Efficient hybrid evolutionary optimization of interatomic potential models

(2010) W. Michael Brown 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

Bayesian approach to cluster expansions

(2009) Tim Mueller et al.   PHYSICAL REVIEW B

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

Analysis of semi-empirical interatomic potentials appropriate for simulation of crystalline and liquid Al and Cu

(2008) M.I. Mendelev et al.   PHILOSOPHICAL MAGAZINE