|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Closed-loop Discovery of the Composition-structure-properties Relationships of Superconductors
||Christopher Stiles, Nam Q. Le, Ian McCue, Alexander New, Christine Piatko, Janna Domenico, Eddie Gienger, Kyle McElroy, Ivelisse Cabrera, Daniel Rose, Timothy Montalbano, Michael Pekala, Christine Chung, Tyrel McQueen, Elizabeth Pogue, Christopher Ratto, Andrew Lennon
|On-Site Speaker (Planned)
Materials discovery is currently an expensive and slow process, driven by trial and error. Recent research has focused on applying machine learning (ML) to existing datasets to efficiently discover materials with desired properties. However, these studies treat materials databases as fixed snapshots, rather than expanding knowledge stores. They also do not intrinsically incorporate physics and domain knowledge into their models. Here, we provide a case-study in developing a closed-loop system - ML, synthesis, characterization - to map and explore the application-rich space of superconducting compounds. We focused on superconductivity given its incomplete theory and unambiguous property measurement that does not require statistical sampling. By combining materials-related descriptors with a novel ML approach (e.g., RooSt), we screened datasets such as Materials Project to predict new compounds, which were then fabricated and characterized. We carried out several closed-loop iterations, updating our models with experimental results, which enabled discovery of novel superconducting compounds.
||Machine Learning, Computational Materials Science & Engineering, Characterization