🎓 Postdoctoral Scholar @ UC Berkeley & Lawrence Berkeley National Lab with Teresa Head-Gordon || Ph.D. @ Indiana University Bloomington with Krishnan Raghavachari
🧪 Computational Chemist | ML Scientist | Quantum Modeler
📍 Based in Berkeley, CA | Open to Industry Roles (ML × Chemistry × AI4Science)
I build machine learning models that match quantum chemistry accuracy — for real-world molecular systems, catalysis, interfacial environments, and biomolecules.
- 🔍 Developed hybrid ML–DFT model for water-ion and water–metal oxide systems
- 🧠 Designed delta-learning force field for many-body PES of water clusters
- ⚛️ Led NMR chemical shift prediction project for Proteins and Nucleic Acids combining fragment based quantum chemistry and machine learning (PCCP 2020, JCTC 2023 Jan, JCTC 2023 May – MIM-ML)
- 📈 Published in JACS, Inorg Chem, JCTC; CO₂RR and NRR mechanisms
- 🧪 Simulations: AIMD (CP2K), DFT (Q-Chem, Gaussian, ORCA), force fields (OpenMM/AMOEBA)
- Languages: Python, Fortran, Bash
- Libraries: PyTorch, scikit-learn, NumPy, pandas
- Chemistry: CP2K, ORCA, Q-Chem, Gaussian, xTB, CREST, OpenMM, AMBER
- ML Chemistry: Delta learning, Deep Learning, many-body energy decomposition
- Extras: Git, Jupyter, GitHub Actions
🔗 MIM-ML
Predict NMR shifts of nucleic acids using fragment-based quantum descriptors + random forest; experimental accuracy at GFN2-xTB cost
🔗 Delta-learning-force
Learn energy corrections for 2-body water-ion clusters via feedforward NN (in progress)
🔗 SCALE
AI-assisted agentic molecular optimization workflow integrating chemical reasoning and data-driven scoring functions
🔗 Or56a Analysis
Mechanistic model of odorant antagonism via AlphaFold + docking + pocket detection