48 citations · 159 across the 7 of their papers we have counts for
12 papers
Machine-learning interatomic potentials for materials science
Y. Mishin
Large-scale atomistic computer simulations of materials rely on interatomic potentials providing computationally efficient predictions of energy and Newtonian forces. Traditional p…
Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network method
G. P. Purja Pun, V. Yamakov, J. Hickman +2
Abstract Interatomic potentials constitute the key component of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network (PINN) meth…
Atomistic study of grain-boundary segregation and grain-boundary diffusion in Al-Mg alloys
R. K. Koju, Y. Mishin
Mg grain boundary (GB) segregation and GB diffusion can impact the processing and properties of Al-Mg alloys. Yet, Mg GB diffusion in Al has not been measured experimentally or pre…
Relationship between grain boundary segregation and grain boundary diffusion in Cu-Ag alloys
R. K. Koju, Y. Mishin
While it is known that alloy components can segregate to grain boundaries (GBs), and that the atomic mobility in GBs greatly exceeds the atomic mobility in the lattice, little is k…
Direct atomistic modeling of solute drag by moving grain boundaries
R. K. Koju, Y. Mishin
We show that molecular dynamics (MD) simulations are capable of reproducing the drag of solute segregation atmospheres by moving grain boundaries (GBs). Although lattice diffusion…
Solute drag and dynamic phase transformations in moving grain boundaries
Y. Mishin
A discrete model and the regular solution approximation are applied to describe the effect of grain boundary motion on grain boundary phase transformations in a binary alloy. The m…