Problem-fluent models for complex decision-making in autonomous materials research

TL;DR

This paper reviews recent advances in autonomous materials research, emphasizing the integration of machine learning, problem-aware modeling, and physics-based approaches within Bayesian frameworks for improved decision-making.

Contribution

It introduces methods to incorporate problem-specific structures and operational considerations into statistical and ML models for autonomous materials research.

Findings

Enhanced Bayesian frameworks for closed-loop design

Integration of physics-based models into ML workflows

Operational considerations incorporated into decision-making processes

Abstract

We review our recent work in the area of autonomous materials research, highlighting the coupling of machine learning methods and models and more problem-aware modeling. We review the general Bayesian framework for closed-loop design employed by many autonomous materials platforms. We then provide examples of our work on such platforms. We finally review our approaches to extend current statistical and ML models to better reflect problem-specific structure including the use of physics-based models and incorporation of operational considerations into the decision-making procedure.