Two-Shot Optimization of Compositionally Complex Refractory Alloys

TL;DR

This study presents a rapid, iterative alloy discovery process combining computational thermodynamics, experimental synthesis, and Bayesian optimization to efficiently identify refractory alloys with enhanced specific hardness and elastic modulus.

Contribution

It introduces a two-shot optimization framework that significantly improves alloy properties without prior physical models, demonstrating the effectiveness of batch Bayesian optimization in materials design.

Findings

54% increase in Pareto front hypervolume after two iterations

10 alloys in second batch outperformed all first batch alloys

Efficient alloy discovery within a complex composition space

Abstract

In this paper, a synergistic computational/experimental approach is presented for the rapid discovery and characterization of novel alloys within the compositionally complex (i.e., "medium/high entropy") refractory alloy space of Ti-V-Nb-Mo-Hf-Ta-W. This was demonstrated via a material design cycle aimed at simultaneously maximizing the objective properties of high specific hardness (hardness normalized by density) and high specific elastic modulus (elastic modulus normalized by density). This framework utilizes high-throughput computational thermodynamics and intelligent filtering to first reduce the untenably large alloy space to a feasible size, followed by an iterative design cycle comprised of high-throughput synthesis, processing, and characterization in batch sizes of 24 alloys. After the first iteration, Bayesian optimization was utilized to inform selection of the next batch of 24 alloys. This paper demonstrates the benefit of using batch Bayesian optimization (BBO) in material design, as significant gains in the objective properties were observed after only two iterations or "shots" of the design cycle without using any prior knowledge or physical models of how the objective properties relate to the design inputs (i.e., composition). Specifically, the hypervolume of the Pareto front increased by 54% between the first and second iterations. Furthermore, 10 of the 24 alloys in the second iteration dominated all alloys from the first iteration.