Vaibhav’s research advances electronic structure theory by improving density functional theory (DFT), a widely used framework for modeling molecules and materials. While DFT is valued for its balance of accuracy and efficiency, its reliability depends on approximations that describe electron–electron interactions. To address these limitations, Vaibhav combines highly accurate quantum-mechanical (wavefunction) data with machine learning techniques to develop next-generation functionals that enhance the accuracy, interpretability, and applicability of DFT in computational chemistry and beyond.
Mentor
Paul Zimmerman