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About this Abstract
Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
Presentation Title	Mechanism-Based Data-Driven Exploration of Complex Concentrated Alloys with Enhanced Mechanical Performance
Author(s)	Yi Yao, Jonathan Cappola, Zhengyu Zhang, Wenjun Cai, Lin Li
On-Site Speaker (Planned)	Lin Li
Abstract Scope	Complex Concentrated Alloys (CCAs), particularly body-centered cubic refractory high-entropy alloys (RHEAs), exhibit remarkable mechanical properties under extreme conditions, but their vast compositional range makes identifying optimal properties challenging. This study utilizes advanced computational technologies, including machine learning potentials and graph neural networks, to expedite the development of CCAs. We investigate the dislocation behaviors of model alloys through large-scale atomistic simulations, focusing on how chemical composition and local ordering affect the mobility of edge and screw dislocations, as well as the impact of lattice distortion and diffuse anti-phase boundary energy (DAPBE) on dislocation behaviors. The identified mechanisms allow us to fine-tune compositions for dislocation motion, and balance lattice distortion and DAPBE, aiming to uncover promising candidates quickly. Our molecular dynamics simulations, enhanced by a GNN model that integrates local atomic environment data, provide a promising approach for optimizing alloy compositions and processing methods, ultimately enhancing performance in aggressive environments.
Proceedings Inclusion?	Planned:
Keywords	Computational Materials Science & Engineering, Machine Learning, High-Entropy Alloys

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