activity
20192026
most citedData-driven Material Models for Atomistic Simulation

70 citations · 164 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.mtrl-sci2026

Thermal Transport in SiC with Intrinsic Defects and Mg Transmutation Products

Chen Shen, Yang Su, Maciej P. Polak +5

Silicon carbide is a leading candidate material for advanced nuclear energy systems, but irradiation-induced defects and transmutation products can severely degrade its thermal con…

cond-mat.mtrl-sci2023

Dynamic Formation of Preferentially Lattice Oriented, Self Trapped Hydrogen Clusters

M. A. Cusentino, E. L. Sikorski, M. J. McCarthy +2

A series of MD and DFT simulations were performed to investigate hydrogen self-clustering and retention in tungsten. Using a newly develop machine learned interatomic potential, sp…

cond-mat.mtrl-sci2022★ 24 cited

Machine Learned Interatomic Potential for Dispersion Strengthened Plasma Facing Components

E. L. Sikorski, M. A. Cusentino, M. J. McCarthy +3

Tungsten (W) is a material of choice for the divertor material due to its high melting temperature, thermal conductivity, and sputtering threshold. However, W has a very high britt…

physics.chem-ph2020★ 70 cited

Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems

Mary Alice Cusentino, Mitchell A. Wood, Aidan P. Thompson

A natural extension of the descriptors used in the Spectral Neighbor Analysis Potential (SNAP) method is derived to treat atomic interactions in chemically complex systems. Atomic…

physics.comp-ph2019★ 70 cited

Data-driven Material Models for Atomistic Simulation

Mitchell A. Wood, Mary Alice Cusentino, Brian D. Wirth +1

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and inge…