Machine Learning a General-Purpose Interatomic Potential for Silicon

Thermodynamic Limit of Crystal Defects with Finite Temperature Tight Binding

(2018) Huajie Chen et al.   ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS

Convolutional neural networks for atomistic systems

(2018) Kevin Ryczko et al.   COMPUTATIONAL MATERIALS SCIENCE

Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations

(2018) Patrick Bleiziffer et al.   Journal of Chemical Information and Modeling

Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

(2018) Tristan Bereau et al.   JOURNAL OF CHEMICAL PHYSICS

Hierarchical modeling of molecular energies using a deep neural network

(2018) Nicholas Lubbers et al.   JOURNAL OF CHEMICAL PHYSICS

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

(2018) Nongnuch Artrith 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

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning of molecular properties: Locality and active learning

(2018) Konstantin Gubaev et al.   JOURNAL OF CHEMICAL PHYSICS

A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information

(2018) Oliver T. Unke 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

Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks

(2018) Lin Shen et al.   Journal of Chemical Theory and Computation

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

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

Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

(2018) Miguel A. Caro et al.   PHYSICAL REVIEW LETTERS

Data-Driven Learning of Total and Local Energies in Elemental Boron

(2018) Volker L. Deringer et al.   PHYSICAL REVIEW LETTERS

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi 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

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

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW MATERIALS

Toolkit for the Construction of Reproducing Kernel-Based Representations of Data: Application to Multidimensional Potential Energy Surfaces

(2017) Oliver T. Unke et al.   Journal of Chemical Information and Modeling

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

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

Learning molecular energies using localized graph kernels

(2017) Grégoire Ferré et al.   JOURNAL OF CHEMICAL PHYSICS

Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations

(2017) Jingheng Wu et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

Neural network based coupled diabatic potential energy surfaces for reactive scattering

(2017) Tim Lenzen et al.   JOURNAL OF CHEMICAL PHYSICS

Representations in neural network based empirical potentials

(2017) Ekin D. Cubuk et al.   JOURNAL OF CHEMICAL PHYSICS

Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels

(2017) Pavlo O. Dral et al.   JOURNAL OF CHEMICAL PHYSICS

Force Field Parametrization of Metal Ions from Statistical Learning Techniques

(2017) Francesco Fracchia et al.   Journal of Chemical Theory and Computation

Machine Learning of Dynamic Electron Correlation Energies from Topological Atoms

(2017) James L. McDonagh et al.   Journal of Chemical Theory and Computation

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

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

Modeling Segregation on AuPd(111) Surfaces with Density Functional Theory and Monte Carlo Simulations

(2017) Jacob R. Boes et al.   Journal of Physical Chemistry C

Self-Diffusion of Surface Defects at Copper–Water Interfaces

(2017) Suresh Kondati Natarajan et al.   Journal of Physical Chemistry C

High-Dimensional Atomistic Neural Network Potentials for Molecule–Surface Interactions: HCl Scattering from Au(111)

(2017) Brian Kolb et al.   Journal of Physical Chemistry Letters

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

Proton-Transfer Mechanisms at the Water–ZnO Interface: The Role of Presolvation

(2017) Vanessa Quaranta et al.   Journal of Physical Chemistry Letters

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Cu Diffusion in Amorphous Ta2O5 Studied with a Simplified Neural Network Potential

(2017) Wenwen Li et al.   JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN

Wavelet Scattering Regression of Quantum Chemical Energies

(2017) Matthew Hirn et al.   MULTISCALE MODELING & SIMULATION

Machine learning reveals orbital interaction in materials

(2017) Tien Lam Pham et al.   SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS

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 in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Interatomic potential construction with self-learning and adaptive database

(2017) Kazutoshi Miwa et al.   PHYSICAL REVIEW MATERIALS

Neural network potential for Al-Mg-Si alloys

(2017) Ryo Kobayashi et al.   PHYSICAL REVIEW MATERIALS

Accurate force field for molybdenum by machine learning large materials data

(2017) Chi Chen et al.   PHYSICAL REVIEW MATERIALS

Energy-free machine learning force field for aluminum

(2017) Ivan Kruglov et al.   Scientific Reports

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

(2016) Nongnuch Artrith et al.   COMPUTATIONAL MATERIALS SCIENCE

A universal preconditioner for simulating condensed phase materials

(2016) David Packwood 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

Machine Learning Force Fields: Construction, Validation, and Outlook

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

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Comparing molecules and solids across structural and alchemical space

(2016) Sandip De et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

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

Reproducibility in density functional theory calculations of solids

(2016) K. Lejaeghere et al.   SCIENCE

Machine learning scheme for fast extraction of chemically interpretable interatomic potentials

(2016) Pavel E. Dolgirev et al.   AIP Advances

Atomistic aspects of fracture

(2015) Erik Bitzek et al.   INTERNATIONAL JOURNAL OF FRACTURE

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

Accuracy of buffered-force QM/MM simulations of silica

(2015) Anke Peguiron et al.   JOURNAL OF CHEMICAL PHYSICS

Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

(2015) Pavlo O. Dral 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

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

(2015) Tristan Bereau et al.   Journal of Chemical Theory and Computation

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

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

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

Three-center tight-binding potential model for C and Si

(2015) Wen-Cai Lu et al.   PHYSICAL REVIEW B

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu et al.   PHYSICAL REVIEW B

Reduced tight-binding models for elemental Si and N, and ordered binary Si-N systems

(2015) J. Gehrmann et al.   PHYSICAL REVIEW B

Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network

(2015) S. Alireza Ghasemi et al.   PHYSICAL REVIEW B

Low Speed Crack Propagation via Kink Formation and Advance on the Silicon (110) Cleavage Plane

(2015) James R. Kermode et al.   PHYSICAL REVIEW LETTERS

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

First principles phonon calculations in materials science

(2015) Atsushi Togo et al.   SCRIPTA MATERIALIA

Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging

(2015) Timothy J. Hughes et al.   SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

Adaptive machine learning framework to accelerateab initiomolecular dynamics

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

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

Development of a “First-Principles” Water Potential with Flexible Monomers. III. Liquid Phase Properties

(2014) Gregory R. Medders et al.   Journal of Chemical Theory and Computation

Development of a “First Principles” Water Potential with Flexible Monomers. II: Trimer Potential Energy Surface, Third Virial Coefficient, and Small Clusters

(2014) Volodymyr Babin et al.   Journal of Chemical Theory and Computation

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

Accuracy of generalized gradient approximation functionals for density-functional perturbation theory calculations

(2014) Lianhua He et al.   PHYSICAL REVIEW B

Dissociative Chemisorption ofO2Inducing Stress Corrosion Cracking in Silicon Crystals

(2014) Anna Gleizer et al.   PHYSICAL REVIEW LETTERS

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

First-principles energetics of water clusters and ice: A many-body analysis

(2013) M. J. Gillan et al.   JOURNAL OF 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

Development of a “First Principles” Water Potential with Flexible Monomers: Dimer Potential Energy Surface, VRT Spectrum, and Second Virial Coefficient

(2013) Volodymyr Babin et al.   Journal of Chemical Theory and Computation

Machine learning of molecular electronic properties in chemical compound space

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

Screened empirical bond-order potentials for Si-C

(2013) Lars Pastewka et al.   PHYSICAL REVIEW B

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

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

On representing chemical environments

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

Thermal transport in phase-change materials from atomistic simulations

(2012) Gabriele C. Sosso et al.   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

Ab initiorandom structure searching

(2011) Chris J Pickard et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

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

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging

(2011) Matthew J.L. Mills et al.   Computational and Theoretical Chemistry

A water potential based on multipole moments trained by machine learning — Reducing maximum energy errors

(2010) Glenn I. Hawe et al.   CANADIAN JOURNAL OF CHEMISTRY

Energy landscape of silicon systems and its description by force fields, tight binding schemes, density functional methods, and quantum Monte Carlo methods

(2010) S. Alireza Ghasemi et al.   PHYSICAL REVIEW B

Graphite-diamond phase coexistence study employing a neural-network mapping of theab initiopotential energy surface

(2010) Rustam Z. Khaliullin 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

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Improved modified embedded-atom method potentials for gold and silicon

(2009) Seunghwa Ryu et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Liquid-Liquid Transition in Supercooled Silicon Determined by First-Principles Simulation

(2009) P. Ganesh et al.   PHYSICAL REVIEW LETTERS

CUR matrix decompositions for improved data analysis

(2009) Michael W. Mahoney et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Hybrid atomistic simulation methods for materials systems

(2009) N Bernstein et al.   REPORTS ON PROGRESS IN PHYSICS

Silicon potentials investigated using density functional theory fitted neural networks

(2008) E Sanville et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Low-speed fracture instabilities in a brittle crystal

(2008) J. R. Kermode et al.   NATURE

Correlation between self-diffusion in Si and the migration mechanisms of vacancies and self-interstitials: An atomistic study

(2008) M. Posselt et al.   PHYSICAL REVIEW B

Describing bond-breaking processes by reactive potentials: Importance of an environment-dependent interaction range

(2008) Lars Pastewka et al.   PHYSICAL REVIEW B

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS