Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams.

ABSTRACT: In the search for novel intermetallic ternary alloys, much of the effort goes into performing a large number of ab initio calculations covering a wide range of compositions and structures. These are essential to building a reliable convex hull diagram. While density functional theory (DFT) provides accurate predictions for many systems, its computational overheads set a throughput limit on the number of hypothetical phases that can be probed. Here, we demonstrate how an ensemble of machine-learning (ML) spectral neighbor-analysis potentials (SNAPs) can be integrated into a workflow for the construction of accurate ternary convex hull diagrams, highlighting regions that are fertile for materials discovery. Our workflow relies on using available binary-alloy data both to train the SNA

SUBMITTER: Rossignol H 

PROVIDER: S-EPMC10966649 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature