Machine Learning Potential for Ga–In Alloy Melting
TL;DR
Radar‐metric analysis shows that PBE + D3‐based models provide the most balanced overall performance for density, liquid structure, and diffusion, whereas LDA‐based models give the best predictions for Ga and EGaIn.
Abstract
Low‐melting liquid metals, especially Ga–In alloys, are essential for flexible electronics, soft robotics, and adaptive thermal interfaces. Predictive atomistic modeling of their melting behavior is challenging because experiments generally provide limited microscopic insight, whereas first‐principles simulations are computationally expensive. In this work, Neuroevolution Potential (NEP) potentials for the Ga–In system are developed using density functional theory (DFT) datasets of 200–700 atom supercells generated with the local density approximation (LDA), Perdew‐Burke‐Ernzerhof (PBE), and PBE + D3 functionals. Eighteen independent NEP models with different energy/force/virial weights were trained and benchmarked against density, radial distribution function (RDF), self‐diffusion coefficient (SDC), and melting temperature ( T m ). Radar‐metric analysis shows that PBE + D3‐based models provide the most balanced overall performance for density, liquid structure, and diffusion, whereas LDA‐based models give the best T m predictions for Ga and EGaIn. We further design a local Lindemann parameter for solid–liquid identification in disordered alloys, extending the two‐phase method to compositionally complex liquid‐metal systems. Together, these results provide a benchmarked NEP model set and a scalable framework for phase‐transition and transport simulations of Ga–In liquid metals.