首页|Trinity College Dublin Reports Findings in Machine Learning (Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams)
Trinity College Dublin Reports Findings in Machine Learning (Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams)
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New research on Machine Learning is the subject of a report. According to news reporting out of Dublin, Ireland, by NewsRx editors, research stated, "In the search for novel intermetallic ternary alloys, much of the effort goes into performing a large number of calculations covering a wide range of compositions and structures. These are essential to building a reliable convex hull diagram." Our news journalists obtained a quote from the research from Trinity College Dublin, "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 SNAP models and to create prototypes for ternary phases. From the prototype structures, all unique ternary decorations are created and used to form a pool of candidate compounds. The SNAPs ensemble is then used to prerelax the structures and screen the most favorable prototypes before using DFT to build the final phase diagram. As constructed, the proposed workflow relies on no extra first-principles data to train the ML surrogate model and yields a DFT-level accurate convex hull."
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