Neural network potential from bispectrum components: A case study on crystalline silicon

Performance and Cost Assessment of Machine Learning Interatomic Potentials

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

SciPy 1.0: fundamental algorithms for scientific computing in Python

(2020) Pauli Virtanen et al.   NATURE METHODS

Ab initio phase diagram and nucleation of gallium

(2020) Haiyang Niu et al.   Nature Communications

Structure prediction drives materials discovery

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

Improve the performance of machine-learning potentials by optimizing descriptors

(2019) Hao Gao et al.   JOURNAL OF CHEMICAL PHYSICS

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

Atomic energy mapping of neural network potential

(2019) Dongsun Yoo et al.   Physical Review Materials

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

(2018) M. Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

Metadynamics for training neural network model chemistries: A competitive assessment

(2018) John E. Herr et al.   JOURNAL OF CHEMICAL PHYSICS

Building machine learning force fields for nanoclusters

(2018) Claudio Zeni 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

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

(2018) Volker L. Deringer 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

Atomic structure of boron resolved using machine learning and global sampling

(2018) Si-Da Huang et al.   Chemical Science

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics

(2018) Luigi Bonati et al.   PHYSICAL REVIEW LETTERS

Machine Learning a General-Purpose Interatomic Potential for Silicon

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

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

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

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

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

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Reproducibility in density functional theory calculations of solids

(2016) K. Lejaeghere et al.   SCIENCE

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   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

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

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

Angular velocity of gravitational radiation from precessing binaries and the corotating frame

(2013) Michael Boyle   PHYSICAL REVIEW D

New developments in evolutionary structure prediction algorithm USPEX

(2012) Andriy O. Lyakhov et al.   COMPUTER PHYSICS COMMUNICATIONS

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

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

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

(2011) Nongnuch Artrith 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

Elastic constants of silicon materials calculated as a function of temperature using a parametrization of the second-generation reactive empirical bond-order potential

(2008) J. David Schall 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