Efficient prediction of grain boundary energies from atomistic simulations via sequential design
Martin Kroll,
Timo Schmalofski,
Holger Dette
et al.
Abstract:Data based materials science is the new promise to accelerate materials design. Especially in computational materials science, data generation can easily be automatized. Usually, the focus is on processing and evaluating the data to derive rules or to discover new materials, while less attention is being paid on the strategy to generate the data. In this work, we show that by a sequential design of experiment scheme, the process of generating and learning from the data can be combined to discover the relevant … Show more
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