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Data-driven learning and prediction of inorganic crystal structures
2018
Faraday discussions
Machine learning-based interatomic potentials, fitting energy landscapes "on the fly", are emerging and promising tools for crystal structure prediction.
doi:10.1039/c8fd00034d
pmid:30043006
fatcat:6i45xmi2njg3zascfbx3etnl54