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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Quantifying the Contributions of Strengthening Mechanisms in Refractory Complex Concentrated Alloys through Physics-Informed Statistical Learning.

Author(s)

Jierui Zhao, Yang Chen, Liang Qi

On-Site Speaker (Planned)

Jierui Zhao

Abstract Scope

We aim to understand the relative importance of mechanisms governing the strength of refractory complex concentrated alloys. Several possible mechanisms are considered: Peierls resistance, where unstable stacking fault energy and dislocation line tension set the stress required for screw dislocation glide; elastic mismatch, where spatial variations in shear modulus change the elastic interaction between dislocations and the surrounding lattice; lattice distortion, where atomic size mismatch produces fluctuating local strain fields that interact with the dislocation strain field; chemical ordering, where changes in local chemical environments reshape the glide energy landscape; and interstitial oxygen, where oxygen-rich clusters generate strong localized obstacles to dislocation motion. For each mechanism, physically informed descriptors are constructed and combined with approximately 280 experimentally reported alloy compositions to fit a strength prediction model based on Bayesian hierarchical regression. Interpretable statistical analyses are then employed to quantify the relative importance of these competing mechanisms and guide alloy design.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation

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