Graduate student in Chemistry (IISER Pune), working on molecular simulation and computational modeling of materials. Interested in combining classical/ab-initio simulation with machine learning to understand and predict molecular and material properties.

Currently seeking PhD opportunities in materials modeling and molecular simulation.

📧 sharmanitin12333@gmail.com · 🔗 LinkedIn

Computational chemistry and molecular simulation, with a growing focus on machine-learning-based modeling of molecular and material properties. My work so far has centered on classical molecular dynamics, structural and dynamical property analysis, and I'm building toward ML-driven property prediction.

water-md-simulation Classical MD study comparing four water models (SPC, SPC/E, TIP3P, TIP4P) using GROMACS across multiple temperatures — density, RDF, dielectric constant, hydrogen bonding, and self-diffusion, benchmarked against experiment. Conducted under Prof. Arun Venkatnathan, IISER Pune.

solubility-prediction Machine learning model for aqueous solubility prediction using the Delaney (ESOL) dataset, with RDKit-based molecular featurization.

• M.Sc. Chemistry — IISER Pune (2025–2027, ongoing)
• B.Sc. Chemistry (Hons.) — University of Delhi (2022–2025)
• IIT JAM Qualifier

Python · C/C++ · GROMACS · VMD · XMgrace · scikit-learn · Linux · LaTeX · HPC cluster computing (Taurus, Parambrahma)

Open to research collaborations and PhD discussions in molecular simulation, materials modeling, and ML for chemistry.