摘要
Wepresent an algorithm for the high-throughput computational discovery of intermetallic compounds in systems with a large number of components.It is particularly important for high entropy alloys(HEAs),where multiple principal elements can form numerous potential intermetallic compounds during the condensation process,making it challenging to predict the dominant phase.Our algorithm is based on a brute-force evaluation of candidate structures with a fixed underlying lattice(FCC or BCC)accelerated by machine-learning interatomic potentials.The algorithm takes a set of chemical elements and a crystal lattice type as inputs and produces structures on and near the convex hull of thermodynamically stable structures.
基金
supported by the Russian Science Foundation(grant number 23-13-00332,https://rscf.ru/project/23-13-00332/).
