| Machine learning models for predictive materials science from fundamental physics: An application to titanium and zirconium MS Nitol, DE Dickel, CD Barrett Acta Materialia 224, 117347, 2022 | 23 | 2022 |
| Solid solution softening in dislocation-starved Mg–Al alloys MS Nitol, S Adibi, CD Barrett, JW Wilkerson Mechanics of Materials 150, 103588, 2020 | 22 | 2020 |
| LAMMPS implementation of rapid artificial neural network derived interatomic potentials D Dickel, M Nitol, CD Barrett Computational Materials Science 196, 110481, 2021 | 19 | 2021 |
| Artificial neural network potential for pure zinc MS Nitol, DE Dickel, CD Barrett Computational Materials Science 188, 110207, 2021 | 19 | 2021 |
| Unraveling Mg 〈c + a〉 slip using neural network potential MS Nitol, S Mun, DE Dickel, CD Barrett Philosophical Magazine 102 (8), 651-673, 2022 | 9 | 2022 |
| Faceting and Twin–Twin Interactions in {1121} and {1122} Twins in Titanium C Barrett, J Martinez, M Nitol Metals 12 (6), 895, 2022 | 6 | 2022 |
| Hybrid interatomic potential for Sn MS Nitol, K Dang, SJ Fensin, MI Baskes, DE Dickel, CD Barrett Physical Review Materials 7 (4), 043601, 2023 | 5 | 2023 |
| Machine Learning Based Approach to Predict Ductile Damage Model Parameters for Polycrystalline Metals DN Blaschke, T Nguyen, M Nitol, D O'Malley, S Fensin Computational Materials Science 229, 112382, 2023 | 2 | 2023 |
| Unraveling Mg< c+ a> Slip Using Neural Network Potentials C Barrett, M Nitol, D Dickel Magnesium Technology 2022, 273-279, 2022 | 1 | 2022 |
| New modified embedded-atom method interatomic potential to understand deformation behavior in VNbTaTiZr refractory high entropy alloy MS Nitol, MJ Echeverria, K Dang, MI Baskes, SJ Fensin Computational Materials Science 237, 112886, 2024 | | 2024 |
| LEAD-LEArning Damage D Blaschke, M Nitol Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2023 | | 2023 |
| Faceting and Twin–Twin Interactions in {1121} and {1122} Twins in Titanium. Metals 2022, 12, 895 C Barrett, J Martinez, M Nitol s Note: MDPI stays neutral with regard to jurisdictional claims in published …, 2022 | | 2022 |
| Predictive Computational Materials Modeling with Machine Learning: Creating the Next Generation of Atomistic Potential Using Neural Networks MS Nitol Mississippi State University, 2021 | | 2021 |
Christopher BarrettDepartment of Mechanical Engineering, Mississippi State UniversityVerified email at me.msstate.edu
