Approaches for Uncertainty Quantification of AI-predicted Material
  Properties: A Comparison
Francesca Tavazza Kamal Choudhary Brian DeCost
The development of large databases of material properties, together with the
availability of powerful computers, has allowed machine learning (ML) modeling
to become a widely used tool for predicting material performances. While
confidence intervals are commonly reported for such ML models, prediction
intervals, i.e., the uncertainty on each prediction, are not as frequently
available. Here, we investigate three easy-to-implement approaches to determine
such individual uncertainty, comparing them across ten ML quantities spanning
energetics, mechanical, electronic, optical, and spectral properties.
Specifically, we focused on the Quantile approach, the direct machine learning
of the prediction intervals and Ensemble methods.