Postdoctoral researcher Yixi Shen and graduate student Ryan Leroux represented the Beyerlein group at the NSF ACCESS Regional AI Workshop – SoCal Edition, which brought together researchers, educators, and students from across Southern California to explore applications of AI and advanced computing in their work.

Yixi presented a poster titled "Data-Driven Mobility Laws for Σ3{112} Incoherent Twin Boundaries in Ni–Cr Alloys", showcasing the group's work on understanding thermal stability in nanotwinned materials. In this work, we use molecular dynamics (MD) to quantify incoherent twin boundary (ITB) migration in Ni–Cr alloys, resolving how composition, interatomic potential, and local Cr distributions jointly control ITB energy and kinetics. Building on these MD results, we train a machine-learning surrogate that predicts log⁡10(vITB)log10(vITB) from temperature, composition, and local chemical descriptors, achieving ~0.30 RMSE in out-of-fold testing across distinct chemical configurations. This ML-accelerated mobility model enables rapid screening of ITB kinetics and provides efficient inputs for multiscale phase-field simulations aimed at predicting NT thermal stability in chemically complex Ni-based alloys.