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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
Presentation Title Uncertainty Prediction for a Variety of Material Properties Modelled via Machine Learning
Author(s) Francesca M. Tavazza, Kamal Choudhary, Brian DeCost
On-Site Speaker (Planned) Francesca M. Tavazza
Abstract Scope Uncertainty quantification in AI-based predictions of material properties is of immense importance for the success and reliability of AI applications in material 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 seldom available. In this work we compare 3 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 compare their results. All data for training and testing were taken from the publicly available JARVIS-DFT database, and the codes developed for computing the prediction intervals are available through JARVIS-tools
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, Other


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