Machine learning for interatomic potential models

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

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

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Atom-density representations for machine learning

(2019) Michael J. Willatt et al.   JOURNAL OF CHEMICAL PHYSICS

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

Genetic algorithms for computational materials discovery accelerated by machine learning

(2019) Paul C. Jennings et al.   npj Computational Materials

Machine-learned multi-system surrogate models for materials prediction

(2019) Chandramouli Nyshadham et al.   npj Computational Materials

Thermal conductivity of single-layer MoS2(1−x)Se2x alloys from molecular dynamics simulations with a machine-learning-based interatomic potential

(2019) Xiaokun Gu et al.   COMPUTATIONAL MATERIALS SCIENCE

Moment tensor potentials as a promising tool to study diffusion processes

(2019) I.I. Novoselov et al.   COMPUTATIONAL MATERIALS SCIENCE

General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods

(2019) Rohit Batra et al.   Journal of Physical Chemistry C

Multi-objective optimization of interatomic potentials with application to MgO

(2019) Eugene Joseph Ragasa et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

(2019) Justin S. Smith et al.   Nature Communications

An electrostatic spectral neighbor analysis potential for lithium nitride

(2019) Zhi Deng et al.   npj Computational Materials

Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys

(2019) Mehdi Jafary-Zadeh et al.   JOURNAL OF ALLOYS AND COMPOUNDS

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

De novo exploration and self-guided learning of potential-energy surfaces

(2019) Noam Bernstein et al.   npj Computational Materials

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

(2019) Alberto Hernandez et al.   npj Computational Materials

An atomistic fingerprint algorithm for learning ab initio molecular force fields

(2018) Yu-Hang Tang et al.   JOURNAL OF CHEMICAL PHYSICS

SchNet – A deep learning architecture for molecules and materials

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

Linearized machine-learning interatomic potentials for non-magnetic elemental metals: Limitation of pairwise descriptors and trend of predictive power

(2018) Akira Takahashi et al.   JOURNAL OF CHEMICAL PHYSICS

Extending the accuracy of the SNAP interatomic potential form

(2018) Mitchell A. Wood et al.   JOURNAL OF CHEMICAL PHYSICS

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

(2018) Giulio Imbalzano et al.   JOURNAL OF CHEMICAL PHYSICS

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

(2018) Volker L. Deringer et al.   Journal of Physical Chemistry Letters

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

(2018) T. L. Jacobsen 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

Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

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

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes

(2018) Gregor N. Simm et al.   Journal of Chemical Theory and Computation

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential

(2018) Felix C. Mocanu et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Toward Reliable and Transferable Machine Learning Potentials: Uniform Training by Overcoming Sampling Bias

(2018) Wonseok Jeong et al.   Journal of Physical Chemistry C

Machine learning material properties from the periodic table using convolutional neural networks

(2018) Xiaolong Zheng et al.   Chemical Science

Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape

(2018) Kamal Choudhary et al.   PHYSICAL REVIEW MATERIALS

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

(2018) Konstantin Gubaev et al.   COMPUTATIONAL MATERIALS SCIENCE

Machine Learning a General-Purpose Interatomic Potential for Silicon

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

Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

(2017) Wenwen Li et al.   JOURNAL OF CHEMICAL PHYSICS

Investigation of a Quantum Monte Carlo Protocol To Achieve High Accuracy and High-Throughput Materials Formation Energies

(2017) Kayahan Saritas et al.   Journal of Chemical Theory and Computation

Machine Learning Force Field Parameters from Ab Initio Data

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

Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions

(2017) Tomáš Trnka et al.   Journal of Chemical Theory and Computation

Accurate Neural Network Description of Surface Phonons in Reactive Gas–Surface Dynamics: N2 + Ru(0001)

(2017) Khosrow Shakouri et al.   Journal of Physical Chemistry Letters

Addressing uncertainty in atomistic machine learning

(2017) Andrew A. Peterson et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

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

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

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

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

Neural network potential for Al-Mg-Si alloys

(2017) Ryo Kobayashi et al.   PHYSICAL REVIEW MATERIALS

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2

(2016) Nongnuch Artrith et al.   COMPUTATIONAL MATERIALS SCIENCE

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

Perspective: Machine learning potentials for atomistic simulations

(2016) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Molecular graph convolutions: moving beyond fingerprints

(2016) Steven Kearnes et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Machine Learning Force Fields: Construction, Validation, and Outlook

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

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

Limitations of reactive atomistic potentials in describing defect structures in oxides

(2016) Teemu Hynninen et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

(2016) Felix A. Faber et al.   PHYSICAL REVIEW LETTERS

Challenges in large scale quantum mechanical calculations

(2016) Laura E. Ratcliff et al.   Wiley Interdisciplinary Reviews-Computational Molecular Science

The ReaxFF reactive force-field: development, applications and future directions

(2016) Thomas P Senftle et al.   npj Computational Materials

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

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

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

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

Accurate and efficient linear scaling DFT calculations with universal applicability

(2015) Stephan Mohr et al.   PHYSICAL CHEMISTRY CHEMICAL 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

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

Uncertainty Quantification in Multiscale Simulation of Materials: A Prospective

(2013) Aleksandr Chernatynskiy et al.   Annual Review of Materials Research

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

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

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

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

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

Parameterization of a reactive force field using a Monte Carlo algorithm

(2013) E. Iype et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Classical atomistic simulations of surfaces and heterogeneous interfaces with the charge-optimized many body (COMB) potentials

(2013) Tao Liang et al.   MATERIALS SCIENCE & ENGINEERING R-REPORTS

Cluster expansion made easy with Bayesian compressive sensing

(2013) Lance J. Nelson et al.   PHYSICAL REVIEW B

Compressive sensing as a paradigm for building physics models

(2013) Lance J. Nelson et al.   PHYSICAL REVIEW B

On representing chemical environments

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

Computational aspects of many-body potentials

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

Ab initiodetermination of structure-property relationships in alloy nanoparticles

(2012) Tim Mueller   PHYSICAL REVIEW B

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

Statistical approaches to forcefield calibration and prediction uncertainty in molecular simulation

(2011) Fabien Cailliez et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Efficient hybrid evolutionary optimization of interatomic potential models

(2010) W. Michael Brown et al.   JOURNAL OF CHEMICAL PHYSICS

Second-generation charge-optimized many-body potential forSi/SiO2and amorphous silica

(2010) Tzu-Ray Shan et al.   PHYSICAL REVIEW B

Exact expressions for structure selection in cluster expansions

(2010) Tim Mueller et al.   PHYSICAL REVIEW B

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

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

Molecular dissociation of hydrogen peroxide (HOOH) on a neural network ab initio potential surface with a new configuration sampling method involving gradient fitting

(2009) Hung M. Le et al.   JOURNAL OF CHEMICAL PHYSICS

Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

(2009) A. Pukrittayakamee et al.   JOURNAL OF CHEMICAL PHYSICS

How to quantify energy landscapes of solids

(2009) Artem R. Oganov et al.   JOURNAL OF CHEMICAL PHYSICS

Continuum variational and diffusion quantum Monte Carlo calculations

(2009) R J Needs et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Bayesian approach to cluster expansions

(2009) Tim Mueller et al.   PHYSICAL REVIEW B

Cluster expansion method for multicomponent systems based on optimal selection of structures for density-functional theory calculations

(2009) Atsuto Seko et al.   PHYSICAL REVIEW B

A divide-and-conquer linear scaling three-dimensional fragment method for large scale electronic structure calculations

(2008) Zhengji Zhao et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

The SIESTA method; developments and applicability

(2008) Emilio Artacho et al.   JOURNAL OF PHYSICS-CONDENSED MATTER