| Data-driven material models for atomistic simulation MA Wood, MA Cusentino, BD Wirth, AP Thompson Physical Review B 99 (18), 184305, 2019 | 90 | 2019 |
| Explicit multielement extension of the spectral neighbor analysis potential for chemically complex systems MA Cusentino, MA Wood, AP Thompson The Journal of Physical Chemistry A 124 (26), 5456-5464, 2020 | 69 | 2020 |
| FitSNAP: Atomistic machine learning with LAMMPS A Rohskopf, C Sievers, N Lubbers, MA Cusentino, J Goff, J Janssen, ... Journal of Open Source Software 8 (84), 5118, 2023 | 56 | 2023 |
| A molecular dynamics study of subsurface hydrogen-helium bubbles in tungsten ZJ Bergstrom, MA Cusentino, BD Wirth Fusion Science and Technology 71 (1), 122-135, 2017 | 50 | 2017 |
| Compositional and structural origins of radiation damage mitigation in high-entropy alloys MA Cusentino, MA Wood, R Dingreville Journal of Applied Physics 128 (12), 2020 | 45 | 2020 |
| Molecular statics calculations of the biases and point defect capture volumes of small cavities AA Kohnert, MA Cusentino, BD Wirth Journal of Nuclear Materials 499, 480-489, 2018 | 42 | 2018 |
| A comparison of interatomic potentials for modeling tungsten–hydrogen–helium plasma–surface interactions MA Cusentino, KD Hammond, F Sefta, N Juslin, BD Wirth Journal of Nuclear Materials 463, 347-350, 2015 | 33 | 2015 |
| 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 | 21 | 2023 |
| Suppression of helium bubble nucleation in beryllium exposed tungsten surfaces MA Cusentino, MA Wood, AP Thompson Nuclear Fusion 60 (12), 126018, 2020 | 11 | 2020 |
| Helium diffusion and bubble evolution in tungsten nanotendrils MA Cusentino, BD Wirth Computational Materials Science 183, 109875, 2020 | 9 | 2020 |
| Beryllium-driven structural evolution at the divertor surface MA Cusentino, MA Wood, AP Thompson Nuclear fusion 61 (4), 046049, 2021 | 8 | 2021 |
| Machine learned interatomic potentials for gas-metal interactions MA Cusentino, MA Wood, AP Thompson Modelling and Simulation in Materials Science and Engineering 33 (1), 015007, 2024 | 5 | 2024 |
| Development of multi-scale computational frameworks to solve fusion materials science challenges A Lasa, S Blondel, MA Cusentino, D Dasgupta, P Hatton, J Marian, ... Journal of Nuclear Materials 594, 155011, 2024 | 4 | 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 | 3 | 2023 |
| Discovering key unknowns for tungsten-hydrogen-helium plasma material interactions using molecular dynamics MA Cusentino | 3 | 2018 |
| Assessment of the literature about Be-W mixed material layer formation in the fusion reactor environment A Lasa, D Dasgupta, MJ Baldwin, MA Cusentino, P Hatton, D Perez, ... Materials Research Express 11 (3), 032002, 2024 | 2 | 2024 |
| Molecular dynamics of high pressure tin phases: Empirical and machine learned interatomic potentials MA Cusentino, B Nebgen, KM Barros, JS Smith, JD Shimanek, A Allen, ... AIP conference proceedings 2844 (1), 320002, 2023 | 2 | 2023 |
| The effect of composition on helium bubble nucleation in complex concentrated alloys MJ McCarthy, KM Karl, MA Cusentino Journal of Applied Physics 137 (17), 2025 | 1 | 2025 |
| Overview of advanced plasma-facing materials testing for Fusion Pilot Plants at DIII-D J Coburn, F Effenberg, MA Cusentino, C Hargrove, M Ialovega, M Morbey, ... Nuclear Materials and Energy, 102064, 2026 | | 2026 |
| A Machine Learning Approach for Atomistic-Informed Modeling of Particle Exchange in a Plasma-Surface Interface GM Gorman, TJ Hardin, MA Cusentino, MM Hopkins GEC 2025, 2025 | | 2025 |
