A neural-network machine learning interatomic potential is used to estimate eutectic points in high-melting alloys by operating directly in the liquid phase without solid-structure input, demonstrated on the Ti-B-C system.
Alou An- gulo, Mathieu Bertin, Jean-Hugues Fillion, Alejandro Rivero Santamar´ ıa, and Maurice Monnerville
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Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics
A neural-network machine learning interatomic potential is used to estimate eutectic points in high-melting alloys by operating directly in the liquid phase without solid-structure input, demonstrated on the Ti-B-C system.