|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||NOW ON-DEMAND ONLY – Physics Based Analytical Models for the Design of New Metastable Rare-earth Compounds
||Prashant Singh, T. Del Rose, Guillermo Vazquez, Raymundo Arroyave, Yaroslav Mudryk
|On-Site Speaker (Planned)
Rare earths find uses in many applications due to their vast span of distinctive physical and chemical properties such as permanent magnets and magnetocaloric materials. The application of machine-learning approaches in rare-earth intermetallic design has been sparse, however, due lack of reliable databases. We utilized `in-house’ rare-earth database to train a SISSO (sure independence screening and sparsifying operator) based machine-learning model and developed physically interpretable analytical models to assess the thermodynamic stability of rare-earth compounds. The analytical models were used for extensive exploration of the alloying effect on thermodynamics stability of Ce based cubic Laves phases with MgCu2 crystal structure. Our predictions are confirmed by experiments and provide quantitative guidance for compositional considerations within machine-learning models for discovering new metastable materials. A detailed electronic-structure analysis of Ce-Fe-Cu systems was also discussed to understand the electronic origin of phase (in)stability.
||Machine Learning, Magnetic Materials, Other