  One compelling vision of the future of materials discovery and design
involves the use of machine learning (ML) models to predict materials
properties and then rapidly find materials tailored for specific applications.
However, realizing this vision requires both providing detailed uncertainty
quantification (model prediction errors and domain of applicability) and making
models readily usable. At present, it is common practice in the community to
assess ML model performance only in terms of prediction accuracy (e.g., mean
absolute error), while neglecting detailed uncertainty quantification and
robust model accessibility and usability. Here, we demonstrate a practical
method for realizing both uncertainty and accessibility features with a large
set of models. We develop random forest ML models for 33 materials properties
spanning an array of data sources (computational and experimental) and property
types (electrical, mechanical, thermodynamic, etc.). All models have calibrated
ensemble error bars to quantify prediction uncertainty and domain of
applicability guidance enabled by kernel-density-estimate-based feature
distance measures. All data and models are publicly hosted on the Garden-AI
infrastructure, which provides an easy-to-use, persistent interface for model
dissemination that permits models to be invoked with only a few lines of Python
code. We demonstrate the power of this approach by using our models to conduct
a fully ML-based materials discovery exercise to search for new stable, highly
active perovskite oxide catalyst materials.
