Curriculum Vitae

Doctoral candidate in Computational Chemistry at IIT Delhi with expertise in molecular dynamics simulations, enhanced sampling techniques, and machine learning applications. PMRF scholar with 9.5/10 CGPA and 10+ publications in high-impact journals.

Education

Feb 2021 – Present

Ph.D. Computational Chemistry

Indian Institute of Technology Delhi

Thesis Advisor: Dr. Tarak Karmakar

CGPA: 9.50/10.0

"Computational Modelling of Monolayer-Protected Metal Nanoclusters: Formation, Self-Assembly, and Catalytic Mechanisms"

Monolayer-protected metal nanoclusters (MPCs) are a new class of materials with atomically precise structures, enabling tuneable physicochemical properties and a broad range of applications in catalysis, biomedicine, and materials science. This thesis employs a multiscale computational approach to understand key aspects of MPC behaviour, including their formation, self-assembly, interactions with biomolecules, and catalytic mechanisms.

2018 – 2020

M.Sc. Chemistry

Indian Institute of Technology Delhi

Thesis Advisor: Prof. Anil J. Elias

CGPA: 9.33/10.0

"Highly E-Selective Olefination of Methyl Substituted N-Heteroarenes with Benzyl Amines using NaCl as Catalyst and Water as Solvent"

Developed a sustainable, metal-free method for highly E-selective olefination using NaCl catalyst in aqueous medium. Demonstrated broad substrate scope and green synthesis potential through in-situ aldehyde formation.

2015 – 2018

B.Sc. Chemistry

B. K. Birla College of Commerce, Science and Arts, Kalyan, Mumbai

CGPA: 6.87/7.00

2013 – 2015

Higher Secondary Education

Tarapur Vidya Mandir, Boisar

Subjects: Physics, Chemistry, Maths, Biology

Scholarships and Awards

2024 Asian Young NanoScientist Presentation Award
2021 Prime Minister Research Fellowship
2020 CSIR-NET Junior Research Fellowship Award
2020 Graduate Aptitude Test in Engineering (GATE)
2018 Joint Admission Test for Masters (JAM)

Research Skills

  • Classical molecular dynamics simulations (Atomistic and coarse grained)
  • Ab initio molecular dynamics simulations
  • Enhanced sampling methods (Metadynamics, OPES, Umbrella sampling)
  • Machine-learning potential-based simulations
  • QM/MM molecular dynamics simulations
  • Mechanistic investigation using static DFT calculations

Technical Skills

  • OS: Linux, Windows, macOS
  • Software: GROMACS, Gaussian, VMD, OVITO, PLUMED, CP2K, DeepMD-kit, LAMMPS
  • Programming: Python (NumPy, Pandas, Matplotlib, ASE)
  • ML Frameworks: PyTorch, LibTorch
  • Visualization: Matplotlib, VMD, OVITO
  • Tools: GitHub, LaTeX, Jupyter Notebooks