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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, and the price-per-kilogramme, to identify commercially viable novel glass-formers. The genetic algorithm is also applied with specific constraints, to identify novel aluminium-based and copper-zirconium-based glass-forming alloys, and to optimize existing zirconium-based alloys.

SUBMITTER: Forrest RM

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

REPOSITORIES: biostudies-literature

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

Forrest Robert M RM Greer A Lindsay AL

Digital discovery 20230104 1

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 comp ...[more]

PMID: 36798881

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