  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.
