Active Research Interests

• First principles electronic structure modeling of organic, inorganic and hybrid materials
• Automated knowledge discovery: High throughput data generation, descriptor selection, and rule-mining (extracting insights from data)
• Machine learning techniques such as support vector regression/classification, kernel ridge regression, random forests, gradient boosting, artificial neural networks etc.
• Adaptive design: Model uncertainty quantification, regression and selection
• Modeling of interfaces: Metal-metal, metal-ceramic and ceramic-ceramic interfaces (dislocation structures, charge transfer effects, etc.) using atomistic potentials
• Modeling of functional materials: Electronic, dielectric, ferroelectric, magnetic and mechanical properties of bulk, interfaces and nanostructures
• Beyond density functional theory (DFT) methods (such as HSE, GW computations) to facilitate computational design of materials with strong electronic correlations
• First principles thermodynamics: Combining DFT with thermodynamics and statistical mechanics to access finite temperature behavior
• Scale bridging methods: Kinetic Monte Carlo simulations, cluster expansion and effective Hamiltonian based techniques
• Catalysis: Prediction of reaction mechanisms and conversion efficiencies by combining DFT with transition state theory
• Optimization and search: Applying genetic algorithms to predict optimized crystal structures, Pareto front modeling

Some Recent highlights