Machine learning potentials for multicomponent systems: The Ti-Al binary system
| Autor: | Atsuto Seko |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Polynomial
Computer science Computer Science::Neural and Evolutionary Computation FOS: Physical sciences 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Set (abstract data type) Physics - Chemical Physics 0103 physical sciences Binary system 010306 general physics Atomic density Chemical Physics (physics.chem-ph) Condensed Matter - Materials Science business.industry Materials Science (cond-mat.mtrl-sci) Computational Physics (physics.comp-ph) 021001 nanoscience & nanotechnology Extended formulation Multicomponent systems Artificial intelligence Variety (universal algebra) 0210 nano-technology business Physics - Computational Physics computer Energy (signal processing) |
| Zdroj: | Physical Review B. 102 |
| ISSN: | 2469-9969 2469-9950 |
| Popis: | Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs employs a systematic set of polynomial invariants including high-order ones to represent the neighboring atomic density. In this study, a formulation of the polynomial invariants is extended to the case of multicomponent systems. The extended formulation is more complex than the formulation for elemental systems. This study also shows its application to Ti-Al binary system. As a result, an MLP with the lowest error and MLPs with high computational cost performance are selected from the many MLPs developed systematically. The predictive powers of the developed MLPs for many properties, such as the formation energy, elastic constants, thermodynamic properties, and mechanical properties, are examined. The MLPs exhibit high predictive power for the properties in a wide variety of ordered structures. The present scheme should be systematically applicable to other multicomponent systems. Comment: 13 pages, 6 figures |
| Databáze: | OpenAIRE |
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