| FitSNAP: Atomistic machine learning with LAMMPS A Rohskopf, C Sievers, N Lubbers, M Cusentino, J Goff, J Janssen, ... Journal of Open Source Software 8 (84), 5118, 2023 | 27 | 2023 |
| Emergence of near-boundary segregation zones in face-centered cubic multiprincipal element alloys MJ McCarthy, H Zheng, D Apelian, WJ Bowman, H Hahn, J Luo, SP Ong, ... Physical Review Materials 5 (11), 113601, 2021 | 16 | 2021 |
| Shuffling mode competition leads to directionally anisotropic mobility of faceted Σ11 boundaries in fcc metals MJ McCarthy, TJ Rupert Physical Review Materials 4 (11), 113402, 2020 | 14 | 2020 |
| Machine learned interatomic potential for dispersion strengthened plasma facing components EL Sikorski, MA Cusentino, MJ McCarthy, J Tranchida, MA Wood, ... The Journal of Chemical Physics 158 (11), 2023 | 12 | 2023 |
| Chemical order transitions within extended interfacial segregation zones in NbMoTaW D Aksoy, MJ McCarthy, I Geiger, D Apelian, H Hahn, EJ Lavernia, J Luo, ... Journal of Applied Physics 132 (23), 2022 | 12 | 2022 |
| Emergence of directionally-anisotropic mobility in a faceted Ʃ11⟨ 110⟩ tilt grain boundary in Cu MJ McCarthy, TJ Rupert Modelling and Simulation in Materials Science and Engineering 28 (5), 055008, 2020 | 5 | 2020 |
| Alloying induces directionally-dependent mobility and alters migration mechanisms of faceted grain boundaries MJ McCarthy, TJ Rupert Scripta Materialia 194, 113643, 2021 | 4 | 2021 |
| Atomic Representations of Local and Global Chemistry in Complex Alloys MJ McCarthy, J Startt, R Dingreville, AP Thompson, MA Wood arXiv preprint arXiv:2303.04311, 2023 | 2 | 2023 |
| Bayesian blacksmithing: discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys J Startt, MJ McCarthy, MA Wood, S Donegan, R Dingreville npj Computational Materials 10 (1), 164, 2024 | 1 | 2024 |
| Dynamic formation of preferentially lattice oriented, self trapped hydrogen clusters MA Cusentino, EL Sikorski, MJ McCarthy, AP Thompson, MA Wood Materials Research Express 10 (10), 106513, 2023 | 1 | 2023 |
| Developing machine learned potentials for high temperature applications ES Salas, MA Cusentino, MJ McCarthy, MA Wood, AP Thompson Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2023 | | 2023 |
| Recent Developments in Machine Learning Interatomic Potentials for Molecular Dynamics ES Salas, AD Rohskopf, JM Goff, MJ McCarthy, MA Cusentino, MA Wood, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2023 | | 2023 |
| Atomistic modeling of plasma material interactions using SNAP machine learned interatomic potentials MA Cusentino, ES Salas, MJ McCarthy, JM Goff, AD Rohskopf, MA Wood, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2023 | | 2023 |
| Development of Machine Learned Interatomic Potentials for Modeling the Effect of Mixed Material Layers on Hydrogen Retention MA Cusentino, MJ McCarthy, ES Salas, MA Wood, AP Thompson Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2023 | | 2023 |
| Machine-learned Interatomic Potential Development for H Trapping in ZrC Strengthened W ES Salas, MA Cusentino, MJ McCarthy, J Tranchida, MA Wood, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2023 | | 2023 |
| Investigating the influence of local composition on properties in complex alloys using machine learned interatomic potentials M McCarthy, J Startt, R Dingreville, A Thompson, M Wood APS March Meeting Abstracts 2023, Q53. 009, 2023 | | 2023 |
| Exploring refractory complex concentrated alloy behavior in the fusion reactor environment with a machine-learned interatomic potential. M McCarthy, J Startt, R Dingreville, A Thompson, M Wood Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Machine Learned Interatomic Potential Development of W-ZrC for Fusion Divertor Microstructure and Thermomechanical Properties. E Sikorski, MA Cusentino, M McCarthy, J Tranchida, M Wood, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| Molecular Dynamics Modeling of Hydrogen and Nitrogen Implantation in Tungsten Using Machine Learned Interatomic Potentials. MA Cusentino, M McCarthy, E Sikorski, M Wood, A Thompson Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
| FitSNAP: Machine Learned Potentials for LAMMPS. A Rohskopf, C Sievers, M McCarthy, J Goff, E Sikorski, A Thompson, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | | 2022 |
