976 citations · 5.6k across the 39 of their papers we have counts for
40 papers
A high-dimensional neural network potential for finite-temperature phenomena in NiTi martensite
Petr Jaroš, Petr Sedlák, Petr Šesták +3
We present a high-dimensional neural network potential (HDNNP) for the martensitic phase of the NiTi shape-memory alloy trained to density functional theory (DFT) data. A central a…
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…
Hydrogen Atom Scattering at the AlO(0001) Surface: A Combined Experimental and Theoretical Study
Martin Liebetrau, Yvonne Dorenkamp, Oliver Bünermann +1
Investigating atom-surface interactions is the key to an in-depth understanding of chemical processes at interfaces, which are of central importance in many fields -- from heteroge…
Tutorial: How to Train a Neural Network Potential
Alea Miako Tokita, Jörg Behler
The introduction of modern Machine Learning Potentials (MLP) has led to a paradigm change in the development of potential energy surfaces for atomistic simulations. By providing ef…
Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker +1
In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemist…
High-dimensional neural network potentials for accurate vibrational frequencies: The formic acid dimer benchmark
Dilshana Shanavas Rasheeda, Alberto Martín Santa Daría, Benjamin Schröder +2
In recent years, machine learning potentials (MLP) for atomistic simulations have attracted a lot of attention in chemistry and materials science. Many new approaches have been dev…