Research & Projects
Research & Projects
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Project 1: Atomistic Simulations of Multi-Elemental Alloys for Extreme Environments
Project 1: Atomistic Simulations of Multi-Elemental Alloys for Extreme Environments
This project formed the core of my Ph.D. research at the Indian Institute of Technology (IIT) Roorkee (2017–2022), supervised by Prof. Avinash Parashar. Collaborating with researchers from Queen’s University (Canada) and the Chinese Academy of Sciences, I aimed to decode the mechanical behavior of high-entropy alloys (HEAs) under extreme conditions using molecular dynamics (MD) and density functional theory (DFT). The work was driven by the need for materials with superior shock resistance in aerospace and defense applications.
Objectives
Objectives
Investigate the role of lattice distortion and nanovoids on the shock compression behavior of Co-Cr-Cu-Fe-Ni HEAs.
Quantify crack propagation dynamics under Mode-I/II loading using atomistic simulations.
Establish design principles for alloys with enhanced fracture toughness and thermal stability.
Results
Results
Published 8+ papers in journals like Computational Materials Science and Engineering Fracture Mechanics.
Discovered that nanovoids and grain boundaries act as stress concentrators, but lattice distortion improves shock resistance.
Secured a DST-funded collaborative project to extend findings into dynamic loading scenarios.
Findings are now guiding alloy design for Indian defense agencies and international aerospace partners
Project 2
Project 2
High-Throughput Screening of MOFs for CO2 Capture Using Machine Learning
High-Throughput Screening of MOFs for CO2 Capture Using Machine Learning
As a specially appointed researcher at the University of Tokyo (2024–present), I work in a team to design metal-organic frameworks (MOFs) for carbon sequestration. This project combines Monte Carlo simulations, DFT, Molecular dynamics and machine learning to address climate change challenges.
Objectives
Objectives
Screen verious MOF/Zeolites candidates for CO2 adsorption capacity and selectivity using ASE and GAUSSIAN.
Develop a Python-based workflow (ASE libraries) to predict adsorption isotherms and pore geometry effects.
Validate results with experimental partners at The University of Tokyo, Japan.
Results
Results
Identified MOFs with >30% higher CO2 selectivity than benchmark materials.
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