| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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| Presentation Title |
Closed-loop Discovery of Materials with Simultaneous Electronic and Mechanical Property Targets |
| Author(s) |
Christopher D. Stiles, Elizabeth Pogue, Alexander New, Brandon Wilfong, Gregory Bassen, Izze Hedrick, Edwin B. Gienger, Christine D. Piatko, Janna Domenico, Kyle McElroy, Timothy Montalbano, Michael Pekala, Nam Q. Le, Christopher Ratto, Andrew Lennon, Tyrel M. McQueen |
| On-Site Speaker (Planned) |
Christopher D. Stiles |
| Abstract Scope |
Machine learning (ML) techniques present tremendous opportunities to accelerate materials design and discovery. However, significant developments are required to adapt approaches from other domains. For example, ML models for materials must generally contend with sparser and more inhomogeneous data. These challenges compound for practical tasks that require simultaneous optimization of multiple properties. We present results from a “closed-loop” approach integrating ML model predictions with experimental synthesis and characterization that provide new data to update our models. We first demonstrated success in discovery of superconducting compounds using information from the Materials Project and Supercon databases, together with data from in-house synthesis and characterization of crystal structure and critical temperature, to train our ML models. Building on this framework, we have expanded our approach to include prediction of mechanical properties, characterized experimentally using high-throughput nanoindentation. Our work demonstrates the promise of ML techniques applied toward materials discovery while optimizing multiple properties simultaneously. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Mechanical Properties, Electronic Materials |