3 papers
cond-mat.mtrl-sci2026
Machine Learning Compatible CALPHAD-type Optimization from Phase Equilibria by Auto-differentiation
Wenhao Zhang, Jean-Claude Crivello, Yusuke Matsuoka +2
To accurately determine phase boundaries and phase transitions, thermodynamic models that describe free energies of phases often have to be optimized based on experimentally observ…
cond-mat.mtrl-sci2026
LLM-guided phase diagram construction through high-throughput experimentation
Ryo Tamura, Haruhiko Morito, Yuna Oikawa +7
Constructing phase diagrams for multicomponent alloys requires extensive experimental measurements and is a time-consuming task. Here we investigate whether large language models (…
cond-mat.mtrl-sci2025
aLLoyM: A large language model for alloy phase diagram prediction
Yuna Oikawa, Guillaume Deffrennes, Taichi Abe +2
Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifica…