Uncertainty Prediction for Machine Learning Models of Material Properties.

ABSTRACT: Uncertainty quantification in artificial intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in materials science. While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i.e., the evaluation of the uncertainty on each prediction, are not as frequently available. In this work, we compare three different approaches to obtain such individual uncertainty, testing them on 12 ML-physical properties. Specifically, we investigated using the quantile loss function, machine learning the prediction intervals directly, and using Gaussian processes. We identify each approach's advantages and disadvantages and end up slightly favoring the modeling of the individual uncerta

SUBMITTER: Tavazza F 

PROVIDER: S-EPMC8655759 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature