Academic Background

PhD, Metallurgical and Materials Engineering
Indian Institute of Technology Kharagpur
2021 to Present
Advisor: Prof. Sankha Mukherjee

My doctoral research focuses on atomistic modelling of functional materials using periodic DFT, ab initio molecular dynamics, and machine-learned interatomic potentials. I study how structural disorder, finite-temperature dynamics, phase stability, and mechanical behaviour influence materials performance, with a particular focus on solid-state ion conductors and related crystalline systems.

Selected outputs from my PhD work include first-author papers in Chemistry of Materials and Acta Materialia, collaborative publications in Cell Reports Physical Science and Physical Chemistry Chemical Physics, and ongoing work on ML interatomic potentials and anharmonic transport in halide solid electrolytes.

MTech, Molecular Engineering
Indian Institute of Technology Delhi
2019 to 2021
CGPA: 9.478/10
Ranked 1st in the programme

My MTech work involved applying machine-learning methods to materials discovery problems, including unsupervised learning strategies for identifying candidate solid electrolytes for Li-ion batteries. This work developed my interest in combining atomistic modelling with data-driven analysis.

MSc, Chemistry

H.N.B. Garhwal University

2016 to 2018

CGPA: 7.71/10 

BSc, Physics-Chemistry-Mathematics

H.N.B. Garhwal University

2013 to 2016

Percentage: 74.5% 

AiMat Summer School, Machine Learning for Materials
Karlsruhe Institute of Technology, Germany (2025)

CAMD Summer School on Electronic Structure Theory and Materials Design
Technical University of Denmark, Denmark (2024)

International Conference on 60 Years of Density Functional Theory
IIT Mandi, India (2024)