Mashroor Nitol
Staff Scientist @Los Alamos National Laboratory
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WORK HISTORY
Staff Scientist @Los Alamos National Laboratory
Los Alamos, NM, US
As a Computational Materials Scientist, I specialize in atomistic-scale modeling and characterization of materials using Density Functional Theory (DFT) and Molecular Dynamics (MD). My work bridges fundamental physics and advanced materials design, with a strong focus on machine learning-based interatomic potentials.Key Responsibilities:Develop machine learning interatomic potentials from high-fidelity DFT databases (e.g, Moment Tensor Potentials, Rapid Artificial Neural Network Potentials).Conduct large-scale atomistic simulations to investigate phase stability, defect energetics, and interface behavior.Automate and optimize simulation workflows for MD and DFT using Python and high-performance computing environments.Major Achievements:Successfully modeled and characterized the phase behavior of multiphase Sn.Developed the first interatomic potential for the Ti–Al–V alloy system and conducted detailed interface studies.Predicted a phase diagram for the Cu–Ag system with experimental-level accuracy.Characterized structural and energetic properties of high-entropy alloys (HEAs), including CoCrFeNi and HfNbTaTiZr.Led the development of the first magnetic interatomic potential for Fe capturing both spin and lattice dynamics.Contributed to the first molecular dynamics-compatible interatomic potential for Pu.Authored and deployed numerous automated tools to accelerate materials modeling workflows.
EDUCATION
The University of Texas at San Antonio
Master of Science - MS, Mechanical Engineering
Khulna University of Engineering and Technology
Bachelor of Science (B.Sc.), Mechanical Engineering
Mississippi State University
Doctor of Philosophy - PhD, Mechanical Engineering
SKILLS
ABOUT MASHROOR NITOL
I’m an applied computational materials scientist with a broad interdisciplinary background in materials modeling, data science, and high-performance computing. My expertise spans numerical analysis, finite element modeling, and the development of machine learning-based interatomic potentials to solve complex problems in materials design and characterization.With hands-on experience in both research and application, I bring a deep understanding of manufacturing processes and the ability to model systems within realistic engineering constraints. My skill set bridges simulation, data, and software development to generate meaningful insights across atomic to macro scales.I’m passionate about turning scientific questions into actionable solutions through scalable computation and collaborative research. Always open to new challenges and conversations in materials science, machine learning, and data-driven innovation.
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