Predicting emergence of crystals from amorphous matter with deep learning
This enables new research in material synthesis by predicting metastable crystals, though it is incremental as it builds on existing concepts like Ostwald's rule.
The paper tackles the problem of predicting crystallization outcomes from amorphous phases, which is challenging for molecular modeling, by using deep learning potentials to sample crystallization pathways, achieving high accuracy across diverse inorganic materials.
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.