Efficient implementation of atom-density representations

Performance and Cost Assessment of Machine Learning Interatomic Potentials

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

PANNA: Properties from Artificial Neural Network Architectures

(2020) Ruggero Lot et al.   COMPUTER PHYSICS COMMUNICATIONS

Partitioning of sulfur between solid and liquid iron under Earth’s core conditions: Constraints from atomistic simulations with machine learning potentials

(2020) Zhigang Zhang et al.   GEOCHIMICA ET COSMOCHIMICA ACTA

An accurate and transferable machine learning potential for carbon

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

Recursive evaluation and iterative contraction of N-body equivariant features

(2020) Jigyasa Nigam et al.   JOURNAL OF CHEMICAL PHYSICS

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

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

(2019) Félix Musil et al.   Journal of Chemical Theory and Computation

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

(2019) Max Veit et al.   Journal of Chemical Theory and Computation

Operators in quantum machine learning: Response properties in chemical space

(2019) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

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

DScribe: Library of descriptors for machine learning in materials science

(2019) Lauri Himanen et al.   COMPUTER PHYSICS COMMUNICATIONS

Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations

(2019) Jutta Rogal et al.   PHYSICAL REVIEW LETTERS

Simple data and workflow management with the signac framework

(2018) Carl S. Adorf et al.   COMPUTATIONAL MATERIALS SCIENCE

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

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

(2018) Thuong T. Nguyen 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

Recognizing Local and Global Structural Motifs at the Atomic Scale

(2018) Piero Gasparotto et al.   Journal of Chemical Theory and Computation

Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

(2018) Miguel A. Caro et al.   PHYSICAL REVIEW LETTERS

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

(2018) Andrea Grisafi et al.   PHYSICAL REVIEW LETTERS

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

Machine Learning a General-Purpose Interatomic Potential for Silicon

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

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

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

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

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

Accelerated materials property predictions and design using motif-based fingerprints

(2015) Tran Doan Huan et al.   PHYSICAL REVIEW B

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

Identification of slow molecular order parameters for Markov model construction

(2013) Guillermo Pérez-Hernández et al.   JOURNAL OF CHEMICAL PHYSICS

Demonstrating the Transferability and the Descriptive Power of Sketch-Map

(2013) Michele Ceriotti et al.   Journal of Chemical Theory and Computation

On representing chemical environments

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

Determination of reaction coordinates via locally scaled diffusion map

(2011) Mary A. Rohrdanz et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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