SKM 2023 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 36: Data Driven Materials Science: Big Data and Work Flows – Microstructure-Property-Relationships (joint session MM/CPP)

Donnerstag, 30. März 2023, 10:15–13:15, SCH A 251

10:15 MM 36.1 Orisodata: A methodology for grain segementation in atomistic simulations using orientation based iterative self-organizing data analysis — •Arun Prakash
10:30 MM 36.2 Comparison of atomic environment descriptors with domain knowledge of the interatomic bond — •Mariano Forti, Ralf Drautz, and Thomas Hammerschmidt
10:45 MM 36.3 A Machine-Learning Framework to Identify Equivalent Atoms at Real Crystalline Surfaces — •King Chun Lai, Sebastian Matera, Christoph Scheurer, and Karsten Reuter
11:00 MM 36.4 Identifying ordered domains in atom probe tomography using machine learning — •Alaukik Saxena, Navyanth Kusampudi, Shyam Katnagallu, Baptiste Gault, Dierk Raabe, and Christoph Freysoldt
11:15 MM 36.5 Atomic cluster expansion: training a transferable water interatomic potential from the local atomic environments of ice — •Eslam Ibrahim, Yury Lysogorskiy, and Ralf Drautz
  11:30 15 min. break
11:45 MM 36.6 Enhancing molecular dynamics simulations of water in comparison to neutron scattering data with algorithms — •Veronika Reich, Luis Carlos Pardo, Martin Müller, and Sebastian Busch
12:00 MM 36.7 Stress and Heat Flux via Automatic Differentiation — •Marcel F. Langer, Florian Knoop, J. Thorben Frank, Christian Carbogno, Matthias Scheffler, and Matthias Rupp
12:15 MM 36.8 Accurate thermodynamic properties of bcc refractories through Direct Upsamling — •Axel Forslund, Jong Hyun Jung, Prashanth Srinivasan, and Blazej Grabowski
12:30 MM 36.9 Efficient workflow for treating thermal and zero-point contributions to the formation enthalpies of ionic materials — •Rico Friedrich, Marco Esters, Corey Oses, Stuart Ki, Maxwell J. Brenner, David Hicks, Michael J. Mehl, Cormac Toher, and Stefano Curtarolo
12:45 MM 36.10 Microstructure-Property Linkages for Effective Elasticity Tensors by Deep Learning — •Bernhard Eidel
  13:00 MM 36.11 The contribution has been withdrawn.
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