Towards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development

TL;DR

This paper presents a Bayesian optimization framework that autonomously explores problem formulations in materials design, enabling efficient alloy development by balancing multiple objectives without predefined problem parameters.

Contribution

It introduces a novel Bayesian optimization approach over problem formulation space, allowing autonomous discovery of optimal alloy designs aligned with multi-attribute preferences.

Findings

Framework effectively identifies optimal alloy formulations.

Converges on solutions satisfying multiple performance thresholds.

Demonstrates potential to accelerate materials discovery processes.

Abstract

Accelerated discovery in materials science demands autonomous systems capable of dynamically formulating and solving design problems. In this work, we introduce a novel framework that leverages Bayesian optimization over a problem formulation space to identify optimal design formulations in line with decision-maker preferences. By mapping various design scenarios to a multi attribute utility function, our approach enables the system to balance conflicting objectives such as ductility, yield strength, density, and solidification range without requiring an exact problem definition at the outset. We demonstrate the efficacy of our method through an in silico case study on a Mo-Nb-Ti-V-W alloy system targeted for gas turbine engine blade applications. The framework converges on a sweet spot that satisfies critical performance thresholds, illustrating that integrating problem formulation discovery into the autonomous design loop can significantly streamline the experimental process. Future work will incorporate human feedback to further enhance the adaptability of the system in real-world experimental settings.