976 citations · 5.6k across the 39 of their papers we have counts for
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Accelerating fourth-generation machine learning potentials by quasi-linear scaling particle mesh charge equilibration
Moritz Gubler, Jonas A. Finkler, Moritz R. Schäfer +2
Machine learning potentials (MLP) have revolutionized the field of atomistic simulations by describing the atomic interactions with the accuracy of electronic structure methods at…
High-Dimensional Neural Network Potentials for Magnetic Systems Using Spin-Dependent Atom-Centered Symmetry Functions
Marco Eckhoff, Jörg Behler
Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first principles-quality simulations. Still, most machine learning potentials ca…
A Bin and Hash Method for Analyzing Reference Data and Descriptors in Machine Learning Potentials
Martín Leandro Paleico, Jörg Behler
In recent years the development of machine learning (ML) potentials (MLP) has become a very active field of research. Numerous approaches have been proposed, which allow to perform…
An experimentally validated neural-network potential energy surface for H atoms on free-standing graphene in full dimensionality
Sebastian Wille, Hongyan Jiang, Oliver Bünermann +3
We present a first principles-quality potential energy surface (PES) describing the inter-atomic forces for hydrogen atoms interacting with free-standing graphene. The PES is a hig…
A Performance and Cost Assessment of Machine Learning Interatomic Potentials
Yunxing Zuo, Chi Chen, Xiangguo Li +8
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the d…
Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
Giulio Imbalzano, Andrea Anelli, Daniele Giofr é +3
Machine learning of atomic-scale properties is revolutionizing molecular modelling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a frac…