A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
Data-driven learning and prediction of inorganic crystal structures
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