Vikas Tiwari
Curriculum Vitae
I am a computational chemist exploring how molecular simulations, machine learning, and AI can be combined to uncover chemical mechanisms and accelerate discovery. My interests span machine-learned interatomic potentials, enhanced sampling, multiscale modeling, and data-driven drug discovery, with a focus on building predictive models for complex molecular and chemical systems.
Education
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.
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.
B.Sc. Chemistry
B. K. Birla College of Commerce, Science and Arts, Kalyan, Mumbai
CGPA: 6.87/7.00
Scholarships and Awards
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, Quantum ESPRESSO
- Programming: Python (NumPy, Pandas, Matplotlib, ASE)
- ML Frameworks: PyTorch, LibTorch
- Visualization: Matplotlib, VMD, OVITO
- Tools: GitHub, LaTeX, Jupyter Notebooks