Gaussian approximation potentials: Theory, software implementation and application examples

A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles

(2023) Jan Kloppenburg et al.   JOURNAL OF CHEMICAL PHYSICS

Modeling high-entropy transition metal alloys with alchemical compression

(2023) Nataliya Lopanitsyna et al.   Physical Review Materials

Massively parallel fitting of Gaussian approximation potentials

(2023) Sascha Klawohn et al.   Machine Learning Science and Technology

Machine-learned acceleration for molecular dynamics in CASTEP

(2023) Tamás K. Stenczel et al.   JOURNAL OF CHEMICAL PHYSICS

Tensor-Reduced Atomic Density Representations

(2023) James P. Darby et al.   PHYSICAL REVIEW LETTERS

Efficient parametrization of the atomic cluster expansion

(2022) Anton Bochkarev et al.   Physical Review Materials

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

(2022) Simon Batzner et al.   Nature Communications

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

(2022) Dorothea Golze et al.   CHEMISTRY OF MATERIALS

Compressing local atomic neighbourhood descriptors

(2022) James P. Darby et al.   npj Computational Materials

Origins of structural and electronic transitions in disordered silicon

(2021) Volker L. Deringer et al.   NATURE

Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide

(2021) Ganesh Sivaraman et al.   PHYSICAL REVIEW LETTERS

Physics-Inspired Structural Representations for Molecules and Materials

(2021) Felix Musil et al.   CHEMICAL REVIEWS

Gaussian Process Regression for Materials and Molecules

(2021) Volker L. Deringer et al.   CHEMICAL REVIEWS

Optimal radial basis for density-based atomic representations

(2021) Alexander Goscinski et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3

(2020) Yuan-Bin Liu et al.   JOURNAL OF CHEMICAL PHYSICS

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

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning of molecular properties: Locality and active learning

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

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

(2018) Linfeng Zhang 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

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

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

(2018) Konstantin Gubaev et al.   COMPUTATIONAL MATERIALS SCIENCE

Chemical shifts in molecular solids by machine learning

(2018) Federico M. Paruzzo et al.   Nature Communications

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

Comparing molecules and solids across structural and alchemical space

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

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

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

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók 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