|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Application of Compositionally-restricted Attention-based Network (CrabNet) for Screening Candidate Dispersed Phases for Designing High Strength Alloys
||Trupti Mohanty, Fan Zhang, K. S. Ravi Chandran, Taylor D. Sparks
|On-Site Speaker (Planned)
Dispersion of hard phase particles in the parent phase matrix has been one of the effective methods in designing high strength alloys for numerous advanced applications. The screening of potential dispersed phases with desired hardness from huge data space has been a challenging task. Machine learning tools can be quite useful for rapid screening of suitable dispersed phases by predicting their hardness values, and thereby enabling accelerated development of high strength alloys. The present work focuses on applicability of compositionally restricted attention-based network (CrabNet) for screening some of the potential alloy phases based on predicted hardness values leveraging their elastic moduli, which can be utilized as dispersed phases in designing of alloys. Equilibrium phase composition was derived using CALPHAD and subsequently hardness values of the phases was predicted invoking the trained CrabNet. The training of CrabNet for prediction of elastic moduli on benchmark datasets and its performance have been discussed.