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Computational Materials Scientist | Periodic DFT, AIMD/CPMD, ML Interatomic Potentials, Materials Informatics
ABOUT ME
I am a PhD candidate in Metallurgical and Materials Engineering at IIT Kharagpur, working in computational materials science using periodic density functional theory, ab initio molecular dynamics, and machine-learned interatomic potentials. My research focuses on ion transport, phase stability, mechanical behaviour, and data-driven materials discovery in crystalline functional materials. I am particularly interested in connecting first-principles accuracy with larger time and length scales through automated workflows, statistical analysis of disordered systems, and interpretable machine learning.
Selected Publications
Beyond LGPS: Superionic Conduction Meets Chemomechanical Anisotropy in Li₁₁AlP₂S₁₂ Solid-State Li-Ion Batteries
Acta Materialia (2026)
Cooperative Transport of Lithium in Disordered Li₁₀MP₂S₁₂ (M = Sn, Si) Electrolytes for Li-Ion Batteries
Chemistry of Materials (2024)
Interfacial Shear Strength of MXene Interfaces
Cell Reports Physical Science (2025)
Machine Learning Predicted Inelasticity in Defective Two-Dimensional Transition Metal Dichalcogenides Using SHAP Analysis
Physical Chemistry Chemical Physics (2024)
Research Themes
My current work spans periodic DFT and finite-temperature atomistic modelling, ML interatomic potentials for extended-scale molecular dynamics, phase stability and mechanical characterisation, and interpretable machine learning for materials discovery.
Opportunities
I am currently interested in postdoctoral opportunities in computational materials science, theoretical chemistry, and atomistic modelling, especially projects involving periodic DFT, molecular dynamics, ML interatomic potentials, materials informatics, and functional materials design.
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