eSupervisor Detail

About me

Dr. Rahul Kumar is a computational chemist with 4.5+ years postdoctoral experience at IIT Delhi and IIT Bombay, specializing in DFT+U modeling of Na-ion battery cathodes, ML-driven band gap predictions (XGBoost, R²=0.92), and π-conjugated helicenes for optoelectronics. With a PhD from IIT Delhi (2020) and 13+ peer-reviewed publications (J. Mater. Chem. A, Int. J. Quantum Chem.), he excels in VASP/Quantum ESPRESSO workflows, NEB diffusion barriers and SHAP-interpretable ML for materials discovery

Interests: Research InterestsNa-ion Battery Cathodes: Atomistic DFT+U/D3 simulations of layered oxides (O3/P2/P3 phases), moisture-driven degradation, Na+ diffusion barriers via NEB, and voltage/capacity fade mechanisms, validated against GITT/experiments. �ML for Materials Discovery: Ensemble models (XGBoost/Random Forest) with SHAP interpretability for rapid band gap/HOMO-LUMO predictions across crystal systems, emphasizing database-native features like Wyckoff positions.Optoelectronic π-Conjugated Systems: Design and electronic structure of helicenes/furan-based molecules for NIR absorbers and organic electronics, using long-range corrected DFT and TD-DFT for spectral properties. �Electrolyte and Interface Modeling: Ab initio MD of ionic liquids/electrolytes, radical anions of chalcogenides, and TM oxide surfaces for energy storage stability. �Beyond Batteries: Metal halide perovskites photophysics, gas-capture nanoclusters, and antioxidant dichalcogenides via hybrid DFT/MM approaches.