3 papers
physics.comp-ph2026
Scalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials
Tina Torabi, Matthias Militzer, Michael P. Friedlander +1
Machine learning interatomic potentials (MLIPs) provide an effective approach for accurately and efficiently modeling atomic interactions, expanding the capabilities of atomistic s…
cond-mat.mtrl-sci2025
Modeling solute-grain boundary interactions in a bcc Ti-Mo alloy using density functional theory
Hariharan Umashankar, Daniel Scheiber, Vsevolod I. Razumovskiy +1
Solute segregation in alloys is a key phenomenon which affects various material characteristics such as embrittlement, grain growth and precipitation kinetics. In this work, the se…
cond-mat.mtrl-sci2025
Atomistically informed phase field study of austenite grain growth
Ayush Suhane, Daniel Scheiber, Vsevolod I. Razumovskiy +1
Atomistically-informed phase field simulations have been performed to investigate the effect of five common alloying elements (Nb, Ti, Mo, V, Mn) on austenite grain growth. The ani…