Research Overview
My research focuses on understanding molecular behavior in complex chemical systems through computational methods, with a long-term vision of developing sustainable chemical processes that address global energy and environmental challenges.
Research Philosophy: Combine rigorous molecular-level understanding with chemical engineering principles. Use computational methods not as an end in themselves, but as a bridge between quantum mechanics and macroscopic phenomena.
Sustainability Mission: Chemistry and chemical engineering are essential for addressing climate change, energy transition, and circular economy. I want to develop computational tools that accelerate the design of green processes and catalysts.
Research Areas
Computational Molecular Engineering
Research Focus: Molecular Engineering
My research focuses on using molecular dynamics simulations (GROMACS, LAMMPS) to understand how small metal clusters (palladium, platinum) behave in different chemical environments. I develop and validate force fields, analyze solvation effects, and track cluster aggregation dynamics. This work forms the foundation for rational catalyst design and nanomaterial engineering.
Key Tools & Software:
GROMACS
LAMMPS
MDAnalysis
VMD
Python
Technical Skills:
Molecular Dynamics
Force Field Development
Nanoclusters
Solvation Thermodynamics
Trajectory Analysis
Related Research Interests:
MD Simulation
Force Fields
Nanoclusters
Solvation
GROMACS
LAMMPS
Future Research Directions: Advanced sampling techniques (enhanced MD), reactive molecular dynamics for catalysis, machine learning-based force field development
Sustainable Process Engineering
Research Focus: Sustainability
I am motivated by the challenge of developing sustainable chemical processes that address global energy and environmental needs. My industrial experience in hydrogen production at ONGC’s pilot plant showed me that computational methods can dramatically accelerate the design of efficient, green chemical processes. My research interests include hydrogen technologies, biomass valorization, catalytic process design, and digital tools for process optimization.
Key Tools & Software:
Aspen Plus
COMSOL Multiphysics
Python
Process Simulation Software
Technical Skills:
Process Modeling
Thermodynamic Analysis
Green Hydrogen
Catalysis
Process Intensification
Related Research Interests:
Green Hydrogen
Catalysis
Biomass Conversion
Process Intensification
Sustainability
Future Research Directions: Integration of MD simulations with process models, development of sustainable catalysts, machine learning for process optimization
Scientific AI & Data Science
Research Focus: Scientific AI
Machine learning and AI are transforming chemical engineering by enabling faster discovery, automated data analysis, and predictive modeling. I apply these techniques to extract insights from molecular simulations, develop predictive models for molecular properties, and automate complex workflows. My goal is to make scientific computing more accessible and efficient.
Key Tools & Software:
TensorFlow
Scikit-learn
Keras
PyTorch
Python
Pandas
Technical Skills:
Machine Learning
Deep Learning
Data Science
Predictive Modeling
Scientific Automation
Related Research Interests:
Machine Learning
Data Science
AI for Chemistry
Automation
Predictive Models
Future Research Directions: ML for force field development, graph neural networks for molecular systems, automated experimental design, high-throughput screening
Scientific Computing
Research Focus: Computing
High-performance computing is essential for simulating large molecular systems and processing massive datasets. I develop efficient scientific software in Python, optimize code for HPC systems, and work with Linux environments. I believe that good scientific computing practices—clean code, reproducibility, version control, documentation—are fundamental to quality research.
Key Tools & Software:
Python
Linux / Bash
Git / GitHub
HPC Systems (SLURM, PBS)
Jupyter Notebooks
Docker
Technical Skills:
Python Programming
Linux Administration
HPC Job Scheduling
Code Optimization
Software Development
Related Research Interests:
HPC
Python
Linux
Scientific Software
Reproducibility
Future Research Directions: GPU-accelerated computing, containerized research workflows, open-source scientific software, reproducible computational workflows
Current Research Focus (2024-2026)
Primary Project: Comprehensive molecular dynamics study of palladium nanoclusters (Pd₃ to Pd₅₅) in multiple solvent environments
Key Findings:
- Solvation effects dramatically influence cluster stability
- Different aggregation pathways in various solvents
- Water’s hydrogen bonding network plays a stabilizing role
- Aggregation dynamics can be tracked using graph algorithms
Methodology:
- 90+ independent MD trajectories (100 ns each)
- Custom force field development and validation
- Advanced trajectory analysis using Python + MDAnalysis
- Machine learning for property prediction
Publication Status: Manuscript in preparation for Journal of Chemical Physics
Research Interests
Beyond my current projects, I am interested in:
- Advanced Sampling Techniques: Enhanced MD, rare event sampling, metadynamics
- Reactive Molecular Dynamics: For studying catalytic reactions at the atomic level
- Machine Learning for Molecular Systems: Graph neural networks, transferable force fields, property prediction
- Multiscale Modeling: Connecting quantum mechanics → molecular scale → continuum scale
- Green Hydrogen Technologies: Catalyst design, process optimization, efficiency improvement
- Biomass Valorization: Converting biomass to chemicals and fuels
- Process Intensification: Novel reactor designs, efficient synthesis routes
- High-Performance Computing: GPU acceleration, scalability, exascale simulations
- Open Source Scientific Software: Tools for reproducible research
Collaboration & Mentorship
I am interested in collaborating on projects involving:
- Computational chemistry and molecular dynamics
- Machine learning applications to chemistry
- Green chemistry and sustainable processes
- Scientific software development
For collaboration inquiries, please contact me.
Publications
See my publications page for papers and manuscripts.