Good Enough is Better: Feasibility vs. Pareto-Optimality in Alloy Design

TL;DR

This paper compares Pareto-optimality and constraint satisfaction approaches in alloy design, showing that focusing on feasibility often yields more practical and earlier solutions in complex, constrained scenarios.

Contribution

It demonstrates that constraint satisfaction methods outperform optimization in finding viable alloys in realistic, multi-objective, constrained alloy design problems.

Findings

Constraint satisfaction yields higher likelihood of viable alloys.

Constraint methods find solutions earlier than optimization.

Feasibility focus leads to more actionable alloy discovery.

Abstract

In alloy design, the search for candidate materials is often framed as an optimization problem, with the goal of identifying Pareto-optimal solutions across multiple objectives. However, Pareto-optimal solutions do not necessarily satisfy all minimum performance thresholds required for practical deployment. An alternative approach is to treat alloy design as a constraint satisfaction problem, in which the goal is to identify any solution that meets all bare minimum requirements across multiple quantities of interest. These approaches have yet to be benchmarked against each other in the context of realistic alloy design problems. In this work, we demonstrate that, in realistic alloy design campaigns involving multiple objectives and constraints, the constraint satisfaction framework yields a higher likelihood of finding viable alloys than optimization-based approaches. Furthermore, constraint-satisfaction approaches find the first viable alloy solutions earlier than optimization. Our results suggest that focusing on feasibility rather than optimality can lead to more actionable outcomes in materials discovery, particularly in highly constrained applications.