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cond-mat.mtrl-sci2023
Towards active learning: A stopping criterion for the sequential sampling of grain boundary degrees of freedom
Timo Schmalofski, Martin Kroll, Holger Dette +1
Many materials processes and properties depend on the anisotropy of the energy of grain boundaries, i.e.~on the fact that this energy is a function of the five geometric degrees of…
cond-mat.mtrl-sci2021★ 1 cited
Efficient prediction of grain boundary energies from atomistic simulations via sequential design
Martin Kroll, Timo Schmalofski, Holger Dette +1
Data based materials science is the new promise to accelerate materials design. Especially in computational materials science, data generation can easily be automatized. Usually, t…