Conference Tools for 2021 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	Model Comparison and Uncertainty Prediction for ML Models of Crystalline Solids Material Properties
Author(s)	Francesca M. Tavazza, Kamal Choudhary, Brian De Cost
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 sustainability of AI in material science. An easy way to compare AI predictions, algorithms and descriptors effectiveness is testing them on systematically computed density functional theory (DFT) databases of material properties. JARVIS-DFT is a NIST-developed such database (available online at https://www.ctcms.nist.gov/~knc6/JVASP.html), with properties like formation energy, bandgaps, elastic tensor, electronic density of states, dielectric function, effective carrier masses, and Seebeck coefficients, computed for about 40 000 materials. In this talk the ML models (JARVIS-ML) developed using such a database are discussed, as well as how their uncertainty (MAE and prediction intervals) depends on the ML algorithm, descriptors, and training procedures employed in developing them. JARVIS-ML is available online at https://www.ctcms.nist.gov/jarvisml/
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation

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