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	Machine Learning Prediction of Defect Formation Energies
Author(s)	Vinit Sharma, Pankaj Kumar, Pratibha Dev, Ghanshyam Pilania
On-Site Speaker (Planned)	Vinit Sharma
Abstract Scope	The feasibility and the stability of a defect in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. Here, using ML techniques we identify the factors that influence defect formation energy in two material classes namely perovskites and MXenes. Using elemental properties as features and random forest regression, we demonstrate a systematic approach to down select the important features, establishing a framework for accurate predictions of the defect formation energy. Our work reveals previously unknown Hume-Rothery-like rules for complex material systems, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection. The framework itself is general and can be applied to other material classes.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, Computational Materials Science & Engineering,

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