Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design. (arXiv:2207.04994v3 [stat.ML] UPDATED)

Data-driven design shows the promise of accelerating materials discovery but
is challenging due to the prohibitive cost of searching the vast design space
of chemistry, structure, and synthesis methods. Bayesian Optimization (BO)
employs uncertainty-aware machine learning models to select promising designs
to evaluate, hence reducing the cost. However, BO with mixed numerical and
categorical variables, which is of particular interest in materials design, has
not been well studied. In this work, we survey frequentist and Bayesian
approaches to uncertainty quantification …

arxiv design machine machine learning materials mixed uncertainty