23 citations · 48 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
A three-dimensional atomistic kinetic Monte Carlo study of dynamic solute-interface interaction
Aulia Tegar Wicaksono, Chad W. Sinclair, Matthias Militzer
A three-dimensional atomistic Kinetic Monte Carlo model was developed and used to study the interaction between mobile solutes and a migrating interface. While the model was develo…