Donnerstag, 16.04.2026
Atomistic Simulations of Complex Systems with High-Dimensional Neural Network Potentials
17:15 - 18:45 Uhr
Campus Technik, HS C, Technikerstraße 25
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Eintritt / Kosten: keine
Vortragende/r
Prof. Dr. Jörg Behler
Jörg Behler studied chemistry at the University of Dortmund from 1995 to 2000, completing his studies with an external diploma thesis at the University of Reading in Great Britain and habilitated in 2014. In 2022 he was appointed to the newly established Chair of Theorectical Chemistry II at the Faculty of Chemistry and Biochemistry of the Ruhr University and a research professorship at the Research Center "Chemical Sciences and Sustainability" of the Research Alliance Ruhr.
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Abstract
Machine Learning Potentials (MLPs) have revolutionized the field of atomistic simulations, since they allow to combine the accuracy of electronic structure calculations with the efficiency of empirical potentials thus enabling the investigation of large systems on long time scales. When selecting a particular type of MLP, the physical interactions in the system of interest have to be taken into account to ensure a reliable representation of the multidimensional potential energy surface. In this talk, an overview of four generations of high-dimensional neural network potentials will be given, from efficient local potentials to MLPs explicitly considering long-range interactions and non-local charge transfer. Typical applications for systems of increasing complexity will be discussed with a focus on simulations of solid-liquid interfaces.
Veranstalter
FSP Scientific Computing
Exchange in Scientific Computing (ESC)