Anirudh Raju Natarajan

Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials

Abstract Multicomponent transition metal carbides are promising for extreme-environment applications, but identifying compositions that are both synthesizable and hard remains challenging. We fine-tune the MACE machine-learned interatomic potential on approximately 28,000 density...

Machine learning interatomic potentials for solid-state precipitation

Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and data-generation schemes designed to streamline...