تقرير
Predicting emergence of crystals from amorphous matter with deep learning
| العنوان: | Predicting emergence of crystals from amorphous matter with deep learning |
|---|---|
| المؤلفون: | Aykol, Muratahan, Merchant, Amil, Batzner, Simon, Wei, Jennifer N., Cubuk, Ekin Dogus |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Condensed Matter - Materials Science, Condensed Matter - Disordered Systems and Neural Networks, Computer Science - Machine Learning, Physics - Computational Physics |
| الوصف: | 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. Comment: 5 main figures, 4 supplementary figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.01117 |
| رقم الأكسشن: | edsarx.2310.01117 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |