Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Ab initio thermodynamics of liquid and solid water

(2019) Bingqing Cheng et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Designing crystallization in phase-change materials for universal memory and neuro-inspired computing

(2019) Wei Zhang et al.   Nature Reviews Materials

How to explore chemical space using algorithms and automation

(2019) Piotr S. Gromski et al.   Nature Reviews Chemistry

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon

(2019) Noam Bernstein et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Thermoelectric GeTe with Diverse Degrees of Freedom Having Secured Superhigh Performance

(2019) Min Hong et al.   ADVANCED MATERIALS

Unsupervised machine learning in atomistic simulations, between predictions and understanding

(2019) Michele Ceriotti   JOURNAL OF CHEMICAL PHYSICS

Atom-density representations for machine learning

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

Structure prediction drives materials discovery

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

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

(2019) Ryosuke Jinnouchi et al.   PHYSICAL REVIEW LETTERS

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

(2019) Konstantinos Konstantinou et al.   Nature Communications

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

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

Towards an atomistic understanding of disordered carbon electrode materials

(2018) Volker L. Deringer et al.   CHEMICAL COMMUNICATIONS

Genesis and Effects of Swapping Bilayers in Hexagonal GeSb2Te4

(2018) Jiang-Jing Wang et al.   CHEMISTRY OF MATERIALS

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

Data-driven learning and prediction of inorganic crystal structures

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

Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface

(2018) Qunchao Tong et al.   FARADAY DISCUSSIONS

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

Extending the accuracy of the SNAP interatomic potential form

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

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures

(2018) So Fujikake 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

Machine learning directed search for ultraincompressible, superhard materials

(2018) Aria Mansouri Tehrani et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

(2018) Gabriele C. Sosso et al.   MOLECULAR SIMULATION

Monatomic phase change memory

(2018) Martin Salinga et al.   NATURE MATERIALS

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

Graphitization of Glassy Carbon after Compression at Room Temperature

(2018) T. B. Shiell 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

Data-driven learning and prediction of inorganic crystal structures

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

Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface

(2018) Qunchao Tong et al.   FARADAY DISCUSSIONS

Crystal growth of Ge2Sb2Te5 at high temperatures

(2018) I. Ronneberger et al.   MRS Communications

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Thermodynamic limit for synthesis of metastable inorganic materials

(2018) Muratahan Aykol et al.   Science Advances

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

(2018) Volker L. Deringer et al.   CHEMISTRY OF MATERIALS

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

(2018) Miguel A. Caro et al.   CHEMISTRY OF MATERIALS

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

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

Progress in nanoinformatics and informational materials science

(2018) Atsuto Seko et al.   MRS BULLETIN

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Niobium tungsten oxides for high-rate lithium-ion energy storage

(2018) Kent J. Griffith et al.   NATURE

Rational Design of Carbon-Rich Materials for Energy Storage and Conversion

(2018) Debin Kong et al.   ADVANCED MATERIALS

Melting Si: Beyond Density Functional Theory

(2018) Florian Dorner et al.   PHYSICAL REVIEW LETTERS

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

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

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

Atomistic Simulations of the Crystallization and Aging of GeTe Nanowires

(2017) S. Gabardi et al.   Journal of Physical Chemistry C

Phase-change materials for non-volatile photonic applications

(2017) M. Wuttig et al.   Nature Photonics

Hybrid carbon based nanomaterials for electrochemical detection of biomolecules

(2017) Tomi Laurila et al.   PROGRESS IN MATERIALS SCIENCE

Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing

(2017) Feng Rao et al.   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

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

Simultaneous Optimization of Carrier Concentration and Alloy Scattering for Ultrahigh Performance GeTe Thermoelectrics

(2017) Juan Li et al.   Advanced Science

Wave Functions, Density Functionals, and Artificial Intelligence for Materials and Energy Research: Future Prospects and Challenges

(2017) Martín A. Mosquera et al.   ACS Energy Letters

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

Surface reconstruction of fluorites in vacuum and aqueous environment

(2017) Giuseppe Fisicaro et al.   PHYSICAL REVIEW MATERIALS

Homo Citansand Carbon Allotropes: For an Ethics of Citation

(2016) Roald Hoffmann et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Graphitization of amorphous carbons: A comparative study of interatomic potentials

(2016) Carla de Tomas et al.   CARBON

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

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

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

Origin of low sodium capacity in graphite and generally weak substrate binding of Na and Mg among alkali and alkaline earth metals

(2016) Yuanyue Liu et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Reproducibility in density functional theory calculations of solids

(2016) K. Lejaeghere et al.   SCIENCE

Discovery of earth-abundant nitride semiconductors by computational screening and high-pressure synthesis

(2016) Yoyo Hinuma et al.   Nature Communications

Elucidating the Nature of the Active Phase in Copper/Ceria Catalysts for CO Oxidation

(2016) Joseph S. Elias et al.   ACS Catalysis

Machine learning scheme for fast extraction of chemically interpretable interatomic potentials

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

Computational predictions of energy materials using density functional theory

(2016) Anubhav Jain et al.   Nature Reviews Materials

Microscopic Complexity in Phase-Change Materials and its Role for Applications

(2015) Volker L. Deringer et al.   ADVANCED FUNCTIONAL MATERIALS

Conceptual Inorganic Materials Discovery - A Road Map

(2015) Martin Jansen   ADVANCED MATERIALS

New Insights into the Structure of Nanoporous Carbons from NMR, Raman, and Pair Distribution Function Analysis

(2015) Alexander C. Forse et al.   CHEMISTRY OF MATERIALS

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

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

Density-functional theory guided advances in phase-change materials and memories

(2015) Wei Zhang et al.   MRS BULLETIN

Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds

(2015) Romain Gautier et al.   Nature Chemistry

Learning scheme to predict atomic forces and accelerate materials simulations

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

Microscopic origin of resistance drift in the amorphous state of the phase-change compound GeTe

(2015) S. Gabardi 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

Aging mechanisms in amorphous phase-change materials

(2015) Jean Yves Raty et al.   Nature Communications

Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials

(2014) Nongnuch Artrith et al.   NANO LETTERS

An optoelectronic framework enabled by low-dimensional phase-change films

(2014) Peiman Hosseini et al.   NATURE

Simulation of crystallization inGe2Sb2Te5: A memory effect in the canonical phase-change material

(2014) J. Kalikka et al.   PHYSICAL REVIEW B

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations

(2013) Gabriele C. Sosso et al.   Journal of Physical Chemistry Letters

Theoretical Prediction and Experimental Realization of New Stable Inorganic Materials Using the Inverse Design Approach

(2013) Andriy Zakutayev et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Using Low-Loss Phase-Change Materials for Mid-Infrared Antenna Resonance Tuning

(2013) Ann-Katrin U. Michel et al.   NANO LETTERS

On representing chemical environments

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

Hyperuniformity in amorphous silicon based on the measurement of the infinite-wavelength limit of the structure factor

(2013) R. Xie et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

First-Principles Calculation of NMR Parameters Using the Gauge Including Projector Augmented Wave Method: A Chemist’s Point of View

(2012) Christian Bonhomme et al.   CHEMICAL REVIEWS

Perspective on density functional theory

(2012) Kieron Burke   JOURNAL OF CHEMICAL PHYSICS

Amorphous structures of Ge/Sb/Te alloys: Density functional simulations

(2012) J. Akola et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide

(2012) Nongnuch Artrith et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

Thermal transport in phase-change materials from atomistic simulations

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

Neural network interatomic potential for the phase change material GeTe

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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Breaking the Speed Limits of Phase-Change Memory

(2012) D. Loke et al.   SCIENCE

Arithmetic and Biologically-Inspired Computing Using Phase-Change Materials

(2011) C. David Wright et al.   ADVANCED MATERIALS

Uncovering Molecular Processes in Crystal Nucleation and Growth by Using Molecular Simulation

(2011) Jamshed Anwar et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Investigating the structure of non-graphitising carbons using electron energy loss spectroscopy in the transmission electron microscope

(2011) Zhi-li Zhang et al.   CARBON

Real Structure and Thermoelectric Properties of GeTe-Rich Germanium Antimony Tellurides

(2011) Tobias Rosenthal et al.   CHEMISTRY OF MATERIALS

VESTA 3for three-dimensional visualization of crystal, volumetric and morphology data

(2011) Koichi Momma et al.   JOURNAL OF APPLIED CRYSTALLOGRAPHY

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Nucleation mechanism for the direct graphite-to-diamond phase transition

(2011) Rustam Z. Khaliullin et al.   NATURE MATERIALS

Interfacial phase-change memory

(2011) R. E. Simpson et al.   Nature Nanotechnology

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Denser than diamond:Ab initiosearch for superdense carbon allotropes

(2011) Qiang Zhu et al.   PHYSICAL REVIEW B

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

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Computer-simulation design of new phase-change memory materials

(2010) J. Hegedus et al.   PHYSICA STATUS SOLIDI A-APPLICATIONS AND MATERIALS SCIENCE

Ab initioquality neural-network potential for sodium

(2010) Hagai Eshet 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

Modelling of nanoparticles: approaches to morphology and evolution

(2010) A S Barnard   REPORTS ON PROGRESS IN PHYSICS

From phase-change materials to thermoelectrics?

(2010) Matthias N. Schneider et al.   ZEITSCHRIFT FUR KRISTALLOGRAPHIE

Thin-Film Transistors

(2009) Robert A. Street   ADVANCED MATERIALS

Self-assembly ofsp2-bonded carbon nanostructures from amorphous precursors

(2009) R. C. Powles et al.   PHYSICAL REVIEW B

Beyond Point Charges: Dynamic Polarization from Neural Net Predicted Multipole Moments

(2008) Michael G. Darley et al.   Journal of Chemical Theory and Computation

Crystalline-Amorphous Core−Shell Silicon Nanowires for High Capacity and High Current Battery Electrodes

(2008) Li-Feng Cui et al.   NANO LETTERS

Low-speed fracture instabilities in a brittle crystal

(2008) J. R. Kermode et al.   NATURE

Pressure-induced phase transitions in silicon studied by neural network-based metadynamics simulations

(2008) Jörg Behler et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

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