Evolutionary design of machine-learning-predicted bulk metallic glasses.

ABSTRACT: The size of composition space means even coarse grid-based searches for interesting alloys are infeasible unless heavily constrained, which requires prior knowledge and reduces the possibility of making novel discoveries. Genetic algorithms provide a practical alternative to brute-force searching, by rapidly homing in on fruitful regions and discarding others. Here, we apply the genetic operators of competition, recombination, and mutation to a population of trial alloy compositions, with the goal of evolving towards candidates with excellent glass-forming ability, as predicted by an ensemble neural-network model. Optimization focuses on the maximum casting diameter of a fully glassy rod, D _max, the width of the supercooled region, ΔT _x

SUBMITTER: Forrest RM 

PROVIDER: S-EPMC9923804 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature