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

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

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

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

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

Similarity Between Amorphous and Crystalline Phases: The Case of TiO2

(2018) Juraj Mavračić 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

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Mapping uncharted territory in ice from zeolite networks to ice structures

(2018) Edgar A. Engel et al.   Nature 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

Thermodynamic limit for synthesis of metastable inorganic materials

(2018) Muratahan Aykol et al.   Science Advances

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

(2018) Daniele Dragoni et al.   PHYSICAL REVIEW 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

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

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

(2018) Konstantin Gubaev et al.   COMPUTATIONAL MATERIALS SCIENCE

Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

(2018) Francesco Maresca et al.   npj Computational Materials

Geometries of edge and mixed dislocations in bcc Fe from first-principles calculations

(2018) Michael R. Fellinger et al.   PHYSICAL REVIEW MATERIALS

Investigating Sodium Storage Mechanisms in Tin Anodes: A Combined Pair Distribution Function Analysis, Density Functional Theory, and Solid-State NMR Approach

(2017) Joshua M. Stratford et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

Ab initio random structure searching of organic molecular solids: assessment and validation against experimental data

(2017) Miri Zilka 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

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

Revealing and exploiting hierarchical material structure through complex atomic networks

(2017) Sebastian E. Ahnert 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

Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: Application to elemental titanium

(2017) Akira Takahashi 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

A universal preconditioner for simulating condensed phase materials

(2016) David Packwood et al.   JOURNAL OF CHEMICAL PHYSICS

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression

(2016) Letif Mones et al.   Journal of Chemical Theory and Computation

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

Reproducibility in density functional theory calculations of solids

(2016) K. Lejaeghere et al.   SCIENCE

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Global minimization of gold clusters by combining neural network potentials and the basin-hopping method

(2015) Runhai Ouyang et al.   Nanoscale

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Synthesis of borophenes: Anisotropic, two-dimensional boron polymorphs

(2015) A. J. Mannix et al.   SCIENCE

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

Synthesis of an open-framework allotrope of silicon

(2014) Duck Young Kim et al.   NATURE MATERIALS

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

On representing chemical environments

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

Two-Dimensional Boron Monolayer Sheets

(2012) Xiaojun Wu et al.   ACS Nano

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

(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

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

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

Ab initiorandom structure searching

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

Van der Waals density functionals applied to solids

(2011) Jiří Klimeš et al.   PHYSICAL REVIEW B

Crystal Structures of Dense Lithium: A Metal-Semiconductor-Metal Transition

(2011) M. Marqués et al.   PHYSICAL REVIEW LETTERS

Ab initioquality neural-network potential for sodium

(2010) Hagai Eshet et al.   PHYSICAL REVIEW B

Hypothetical low-energy chiral framework structure of group 14 elements

(2010) Chris J. Pickard 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

Boron: Elementary Challenge for Experimenters and Theoreticians

(2009) Barbara Albert et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Ionic high-pressure form of elemental boron

(2009) Artem R. Oganov et al.   NATURE

Efficient Implementation of a van der Waals Density Functional: Application to Double-Wall Carbon Nanotubes

(2009) Guillermo Román-Pérez 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

Covalent radii revisited

(2008) Beatriz Cordero et al.   DALTON TRANSACTIONS

Highly compressed ammonia forms an ionic crystal

(2008) Chris J. Pickard et al.   NATURE MATERIALS

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

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

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS

Restoring the Density-Gradient Expansion for Exchange in Solids and Surfaces

(2008) John P. Perdew et al.   PHYSICAL REVIEW LETTERS