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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…
A Hessian-Based Assessment of Atomic Forces for Training Machine Learning Interatomic Potentials
Marius Herbold, Jörg Behler
In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PES) with close to f…
Insights into lithium manganese oxide-water interfaces using machine learning potentials
Marco Eckhoff, Jörg Behler
Unraveling the atomistic and the electronic structure of solid-liquid interfaces is the key to the design of new materials for many important applications, from heterogeneous catal…
Properties of α-Brass Nanoparticles II: Structure and Composition
Jan Weinreich, Martín Leandro Paleico, Jörg Behler
Nanoparticles have become increasingly interesting for a wide range of applications, because in principle it is possible to tailor their properties by controlling size, shape and c…
A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker +1
Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the establish…
A criticial view on e occupancy as a descriptor for oxygen evolution catalytic activity in LiMnO nanoparticles
Florian Schönewald, Marco Eckhoff, Max Baumung +4
We investigate the effect of the surface electronic structure and composition of LiMnO nanoparticles on the electrocatalytic oxygen evolution reaction (OER). Scanning trans…