Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison

• URL: http://arxiv.org/abs/2310.13136v1
• Date: Thu, 19 Oct 2023 20:20:39 GMT
• Title: Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison
• Authors: Francesca Tavazza and Kamal Choudhary and Brian DeCost
• Abstract summary: Three easy-to-implement approaches to determine individual uncertainty are presented. We focus on the direct machine learning of the prediction intervals and Ensemble methods.
• Score: 0.4037357056611557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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