SKM 2023 –
wissenschaftliches Programm
CPP 48: 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
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10:15 |
CPP 48.1 |
Orisodata: A methodology for grain segementation in atomistic simulations using orientation based iterative self-organizing data analysis — •Arun Prakash
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10:30 |
CPP 48.2 |
Comparison of atomic environment descriptors with domain knowledge of the interatomic bond — •Mariano Forti, Ralf Drautz, and Thomas Hammerschmidt
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10:45 |
CPP 48.3 |
A Machine-Learning Framework to Identify Equivalent Atoms at Real Crystalline Surfaces — •King Chun Lai, Sebastian Matera, Christoph Scheurer, and Karsten Reuter
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11:00 |
CPP 48.4 |
Identifying ordered domains in atom probe tomography using machine learning — •Alaukik Saxena, Navyanth Kusampudi, Shyam Katnagallu, Baptiste Gault, Dierk Raabe, and Christoph Freysoldt
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11:15 |
CPP 48.5 |
Atomic cluster expansion: training a transferable water interatomic potential from the local atomic environments of ice — •Eslam Ibrahim, Yury Lysogorskiy, and Ralf Drautz
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11:30 |
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15 min. break
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11:45 |
CPP 48.6 |
Enhancing molecular dynamics simulations of water in comparison to neutron scattering data with algorithms — •Veronika Reich, Luis Carlos Pardo, Martin Müller, and Sebastian Busch
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12:00 |
CPP 48.7 |
Stress and Heat Flux via Automatic Differentiation — •Marcel F. Langer, Florian Knoop, J. Thorben Frank, Christian Carbogno, Matthias Scheffler, and Matthias Rupp
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12:15 |
CPP 48.8 |
Accurate thermodynamic properties of bcc refractories through Direct Upsamling — •Axel Forslund, Jong Hyun Jung, Prashanth Srinivasan, and Blazej Grabowski
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12:30 |
CPP 48.9 |
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
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12:45 |
CPP 48.10 |
Microstructure-Property Linkages for Effective Elasticity Tensors by Deep Learning — •Bernhard Eidel
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13:00 |
CPP 48.11 |
The contribution has been withdrawn.
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