activity
20142023
most citedEfficient prediction of grain boundary energies from atomistic simulations via sequential design

1 citations · 3 across the 9 of their papers we have counts for

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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…