Materials property prediction with uncertainty quantification: A benchmark study

Uncertainty quantification (UQ) has increasing importance in the building of robust high-performance and generalizable materials property prediction models. It can also be used active learning to train better models by focusing on gathering new training data from uncertain regions. There are several categories UQ methods, each considering different types uncertainty sources. Here, we conduct a comprehensive evaluation methods for graph neural network-based evaluate how they truly reflect that want error bound estimation or learning. Our experimental results over four crystal datasets (including formation energy, adsorption total bandgap properties) show popular ensemble NOT always best choice prediction. For convenience community, all source code accessed freely at https://github.com/usccolumbia/materialsUQ.