TY - JOUR
AU1 - Forrest, Robert M.
AU2 - Greer, A. Lindsay
AB - 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, Dmax, the width of the supercooled region, ΔTx, 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.
TI - Evolutionary design of machine-learning-predicted bulk metallic glasses
JF - Digital Discovery
DO - 10.1039/d2dd00078d
DA - 2023-02-13
UR - https://www.deepdyve.com/lp/royal-society-of-chemistry/evolutionary-design-of-machine-learning-predicted-bulk-metallic-qA0sVBSdeF
SP - 202
EP - 218
VL - 2
IS - 1
DP - DeepDyve
ER -