Predicting emergence of crystals from amorphous matter with deep
  learning
Muratahan Aykol Amil Merchant Simon Batzner Jennifer N. Wei Ekin Dogus Cubuk
Crystallization of the amorphous phases into metastable crystals plays a
fundamental role in the formation of new matter, from geological to biological
processes in nature to synthesis and development of new materials in the
laboratory. Predicting the outcome of such phase transitions reliably would
enable new research directions in these areas, but has remained beyond reach
with molecular modeling or ab-initio methods. Here, we show that
crystallization products of amorphous phases can be predicted in any inorganic
chemistry by sampling the crystallization pathways of their local structural
motifs at the atomistic level using universal deep learning potentials. We show
that this approach identifies the crystal structures of polymorphs that
initially nucleate from amorphous precursors with high accuracy across a
diverse set of material systems, including polymorphic oxides, nitrides,
carbides, fluorides, chlorides, chalcogenides, and metal alloys. Our results
demonstrate that Ostwald's rule of stages can be exploited mechanistically at
the molecular level to predictably access new metastable crystals from the
amorphous phase in material synthesis.