Machine-learning interatomic potentials for materials science

Machine-learned interatomic potentials for alloys and alloy phase diagrams

(2021) Conrad W. Rosenbrock et al.   npj Computational Materials

Machine learning for interatomic potential models

(2020) Tim Mueller et al.   JOURNAL OF CHEMICAL PHYSICS

Performance and Cost Assessment of Machine Learning Interatomic Potentials

(2020) Yunxing Zuo et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Efficient Global Structure Optimization with a Machine-Learned Surrogate Model

(2020) Malthe K. Bisbo et al.   PHYSICAL REVIEW LETTERS

Continuous and optimally complete description of chemical environments using Spherical Bessel descriptors

(2020) Emir Kocer et al.   AIP Advances

Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches

(2020) James E. Saal et al.   Annual Review of Materials Research

Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy

(2020) Christopher M. Andolina et al.   JOURNAL OF CHEMICAL PHYSICS

Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems

(2020) Mary Alice Cusentino et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide

(2020) April M. Cooper et al.   npj Computational Materials

An accurate and transferable machine learning potential for carbon

(2020) Patrick Rowe et al.   JOURNAL OF CHEMICAL PHYSICS

Review of the Frenkel–Ladd technique for computing free energies of crystalline solids

(2020) Ravi Kumar Reddy Addula et al.   MOLECULAR SIMULATION

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

(2020) Ganesh Sivaraman et al.   npj Computational Materials

MAISE: Construction of neural network interatomic models and evolutionary structure optimization

(2020) Samad Hajinazar et al.   COMPUTER PHYSICS COMMUNICATIONS

Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials

(2020) Berk Onat et al.   JOURNAL OF CHEMICAL PHYSICS

Incompleteness of Atomic Structure Representations

(2020) Sergey N. Pozdnyakov et al.   PHYSICAL REVIEW LETTERS

Machine learning for metallurgy I. A neural-network potential for Al-Cu

(2020) Daniel Marchand et al.   Physical Review Materials

Machine learning for metallurgy II. A neural-network potential for magnesium

(2020) Markus Stricker et al.   Physical Review Materials

Development of a general-purpose machine-learning interatomic potential for aluminum by the physically informed neural network method

(2020) G. P. Purja Pun et al.   Physical Review Materials

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

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

Transferability of interatomic potentials for molybdenum and silicon

(2019) Yury Vyatcheslavovich Lysogorskiy et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Materials informatics: From the atomic-level to the continuum

(2019) J.M. Rickman et al.   ACTA MATERIALIA

Accelerating materials science with high-throughput computations and machine learning

(2019) Shyue Ping Ong   COMPUTATIONAL MATERIALS SCIENCE

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

High-throughput calculations in the context of alloy design

(2019) Axel van de Walle et al.   MRS BULLETIN

Structure prediction drives materials discovery

(2019) Artem R. Oganov et al.   Nature Reviews Materials

New frontiers for the materials genome initiative

(2019) Juan J. de Pablo et al.   npj Computational Materials

Machine-learned multi-system surrogate models for materials prediction

(2019) Chandramouli Nyshadham et al.   npj Computational Materials

Enabling QM-accurate simulation of dislocation motion in γ−Ni and α−Fe using a hybrid multiscale approach

(2019) F. Bianchini et al.   PHYSICAL REVIEW MATERIALS

MatCALO: Knowledge-enabled machine learning in materials science

(2019) Mareike Picklum et al.   COMPUTATIONAL MATERIALS SCIENCE

General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods

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

Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5

(2019) Konstantinos Konstantinou et al.   Nature Communications

Physically informed artificial neural networks for atomistic modeling of materials

(2019) G. P. Purja Pun et al.   Nature Communications

Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies

(2019) Hasan Babaei et al.   Physical Review Materials

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

(2019) Volker L. Deringer et al.   ADVANCED MATERIALS

DScribe: Library of descriptors for machine learning in materials science

(2019) Lauri Himanen et al.   COMPUTER PHYSICS COMMUNICATIONS

Anharmonic thermodynamics of vacancies using a neural network potential

(2019) Anton S. Bochkarev et al.   Physical Review Materials

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

(2019) Noam Bernstein et al.   npj Computational Materials

Data-driven learning and prediction of inorganic crystal structures

(2018) Volker L. Deringer et al.   FARADAY DISCUSSIONS

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

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

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

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

Evaluating variability with atomistic simulations: the effect of potential and calculation methodology on the modeling of lattice and elastic constants

(2018) Lucas M Hale et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

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

Data-driven learning and prediction of inorganic crystal structures

(2018) Volker L. Deringer et al.   FARADAY DISCUSSIONS

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

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

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

(2018) Konstantin Gubaev et al.   COMPUTATIONAL MATERIALS SCIENCE

Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics

(2018) Luigi Bonati et al.   PHYSICAL REVIEW LETTERS

Machine learning in materials design and discovery: Examples from the present and suggestions for the future

(2018) J. E. Gubernatis et al.   PHYSICAL REVIEW MATERIALS

Atomicrex—a general purpose tool for the construction of atomic interaction models

(2017) Alexander Stukowski et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational 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

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

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

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

Classical interaction potentials for diverse materials fromab initiodata: a review ofpotfit

(2015) Peter Brommer et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu 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

Adaptive machine learning framework to accelerateab initiomolecular dynamics

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

An angular embedded atom method interatomic potential for the aluminum–silicon system

(2014) P Saidi et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

NSF cyberinfrastructures: A new paradigm for advancing materials simulation

(2013) Ellad B. Tadmor et al.   CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE

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

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

On representing chemical environments

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

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

(2012) Stefano Curtarolo et al.   COMPUTATIONAL MATERIALS SCIENCE

Variable Charge Reactive Potential for Hydrocarbons to Simulate Organic-Copper Interactions

(2012) Tao Liang et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Erratum: High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide [Phys. Rev. B83, 153101 (2011)]

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Neural network interatomic potential for the phase change material GeTe

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

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

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

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Potential Energy Surfaces Fitted by Artificial Neural Networks

(2010) Chris M. Handley et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Molecular-dynamics study of solid–liquid interface migration in fcc metals

(2010) M I Mendelev et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Ab initioquality neural-network potential for sodium

(2010) Hagai Eshet 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

Interatomic potentials for atomic scale modeling of metal–matrix ceramic particle reinforced nanocomposites

(2009) A.M. Dongare et al.   COMPOSITES PART B-ENGINEERING

Angular-dependent embedded atom method potential for atomistic simulations of metal-covalent systems

(2009) Avinash M. Dongare et al.   PHYSICAL REVIEW B

Parametrization of analytic interatomic potential functions using neural networks

(2008) M. Malshe et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS