摘要
Atomistic simulations are crucial for predicting material properties and understanding phase stability,essential for materials selection and development.However,the high computational cost of density functional theory calculations challenges the design of materials with complex structures and composition.This study introduces new data acquisition strategies using Bayesian-Gaussian optimization that efficiently integrate the geometry of the convex hull to optimize the yield of batch experiments.We developed uncertainty-based acquisition functions to prioritize the computation tasks of configurations of multi-component alloys,enhancing our ability to identify the ground-state line.
基金
supported by the National Science Foundation under Grant No.(CAREER DMR-1848128)
This research was supported in part through computational resources provided by Information Technology at Purdue,West Lafayette,Indiana^(40).D.W.would like to thank the University of Liverpool Library for Open Access Funds。
