Crystal structure prediction with machine learning-based element substitution. (arXiv:2201.11188v1 [cond-mat.mtrl-sci])

The prediction of energetically stable crystal structures formed by a given
chemical composition is a central problem in solid-state physics. In principle,
the crystalline state of assembled atoms can be determined by optimizing the
energy surface, which in turn can be evaluated using first-principles
calculations. However, performing the iterative gradient descent on the
potential energy surface using first-principles calculations is prohibitively
expensive for complex systems, such as those with many atoms per unit cell.
Here, we present a unique methodology …

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