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MM: Fachverband Metall- und Materialphysik

MM 62: Developement of Calculation Methods III

Thursday, March 21, 2024, 15:45–18:00, C 264

15:45 MM 62.1 Phase transitions in radial distribution biased Molecular Dynamics simulations — •Lars Dammann, Patrick Huber, and Robert H. Meißner
16:00 MM 62.2 Finite temperature electronic structure calculations for heavy element tetrahedral semiconductors using a dynamic tight-binding model — •Shaoming Zhang, Martin Schwade, and David A. Egger
16:15 MM 62.3 Machine Learning Potentials for Multi-State Systems: Predicting Photoluminescence Spectra from Molecular DynamicsChristopher Linderälv, •Nicklas Österbacka, Julia Wiktor, and Paul Erhart
16:30 MM 62.4 Dynasor 2.0: From simulation to experiment through correlation functions — •Esmée Berger, Erik Fransson, Fredrik Eriksson, Eric Lindgren, and Paul Erhart
16:45 MM 62.5 Machine-learned interatomic potential for microstructure formation in Ni-rich NiAl systems — •Adam Fisher, Julie B. Staunton, Huan Wu, and Peter Brommer
17:00 MM 62.6 Cross-Platform Hyperparameter Optimizer for Machine-Learning Potential Fitting — •Daniel F. Thomas du Toit, Yuxing Zhou, and Volker L. Deringer
17:15 MM 62.7 Warm Dense Hydrogen as a Benchmark for Machine-Learning PotentialsBastian Jäckl, Thomas Bischoff, and •Matthias Rupp
17:30 MM 62.8 Ab initio Raman spectroscopy including temperature: Theory and application for GaN and BaZrS3 — •Florian Knoop, Nimrod Benshalom, Matan Menahem, Omer Yaffe, and Olle Hellman
17:45 MM 62.9 An efficient method for estimating the dynamics of full polarizability tensor in ab initio molecular dynamics simulations — •Pouya Partovi-Azar
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