Efficient sampling in materials simulation - exploring the parameter space of grain boundaries
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Date
2016
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Abstract
In the framework of materials design there is the demand for extensive databases of specific materials
properties. In this work we suggest an improved strategy for creating future databases, especially for
extrinsic properties that depend on several material parameters. As an example we choose the energy of
grain boundaries as a function of their geometric degrees of freedom. The construction of existing databases
of grain boundary energies in face-centred and body centred cubic metals relied on the a-priori knowledge of
the location of important cusps and maxima in the five-dimensional energy landscape, and on an as-densely-
as-possible sampling strategy. We introduce two methods to improve the current state of the art. The
location and number of the energy minima along which the hierarchical sampling takes place is predicted
from existing data points without any a-priori knowledge, using a predictor function. Furthermore we
show that it is more efficient to use a sequential sampling in a \design of experiment" scheme, rather than
sampling all observations homogeneously in one batch. This sequential design exhibits a smaller error than
the simultaneous one, and thus can provide the same accuracy with fewer data points. The new strategy
should be particularly beneficial in the exploration of grain boundary energies in new alloys and/or non-cubic
structures.
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Keywords
sequential sampling, high-throughput methods, atomistic simulations, grain boundary energy, design of experiment