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

2025-02-09

AI Summary

Multi-objective optimization of Mo–Nb–Ti–V–W refractory high-entropy alloys faces conflicting objectives—including ductility, yield strength, density, and solidification range—compounded by ill-defined problem formulation. Method: This work introduces a novel Bayesian optimization paradigm operating in the “problem-formulation space,” for the first time integrating formulation discovery into autonomous materials design. It dynamically constructs multi-objective optimization problems aligned with user preferences via a multi-attribute utility function—without requiring predefined objective weights or functional forms. Coupling Thermo-Calc thermodynamic modeling with high-throughput simulation, the method rapidly converges to a Pareto-optimal “sweet spot” satisfying multiple engineering constraints in a gas turbine blade use case. Contribution/Results: The framework unifies autonomous, adaptive problem discovery and solution search, significantly reducing traditional trial-and-error cycles while enabling interpretable, preference-driven alloy design.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Autonomous system for alloy development
• Bayesian optimization over formulation space
• Balancing conflicting material properties

Innovation

• Bayesian optimization over formulation space
• Multi-attribute utility function mapping
• Autonomous design loop integration