Problem-fluent models for complex decision-making in autonomous materials research
This work addresses the challenge of complex decision-making in autonomous materials research, but it is incremental as it reviews and extends existing methods.
The paper reviews a Bayesian framework for autonomous materials research, integrating machine learning with problem-aware modeling to enhance decision-making, but does not provide concrete numerical results.
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.