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Mashroor S. Nitol
标题
引用次数
引用次数
年份
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
272022
Solid solution softening in dislocation-starved Mg–Al alloys
MS Nitol, S Adibi, CD Barrett, JW Wilkerson
Mechanics of Materials 150, 103588, 2020
222020
Artificial neural network potential for pure zinc
MS Nitol, DE Dickel, CD Barrett
Computational Materials Science 188, 110207, 2021
212021
LAMMPS implementation of rapid artificial neural network derived interatomic potentials
D Dickel, M Nitol, CD Barrett
Computational Materials Science 196, 110481, 2021
202021
Unraveling Mg 〈c + a〉 slip using neural network potential
MS Nitol, S Mun, DE Dickel, CD Barrett
Philosophical Magazine 102 (8), 651-673, 2022
102022
Hybrid interatomic potential for Sn
MS Nitol, K Dang, SJ Fensin, MI Baskes, DE Dickel, CD Barrett
Physical Review Materials 7 (4), 043601, 2023
82023
Faceting and Twin–Twin Interactions in {1121} and {1122} Twins in Titanium
C Barrett, J Martinez, M Nitol
Metals 12 (6), 895, 2022
72022
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
32023
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
22024
Unraveling Mg< c+ a> Slip Using Neural Network Potentials
C Barrett, M Nitol, D Dickel
Magnesium Technology 2022, 273-279, 2022
12022
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
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