Research Area
Device-scale quantum transport calculations are limited less by the non-equilibrium Green’s function formalism itself than by the cost of producing an accurate Hamiltonian for a realistic device geometry. Coupling machine-learned tight-binding Hamiltonians to NEGF removes that bottleneck, enabling transport simulations of two-dimensional material devices at realistic sizes. Related work applies the same learned-representation idea to spectroscopic response, including phonon-assisted optical spectra.
Publications in this area
Physical Review B, 111, 235146 (2025)
Mishra K., Pandey S.K., Chaudhuri S., Rawat R., Bhobe P.A.
IEEE MoSICom 2025, BITS Pilani Dubai Campus
Zou J., Zhouyin Z., Gu Q., Pandey S.K.
Physical Review B, 109, 075133 (2024)
Shee P., Yang C.-J., Pandey S.K., Nandy A.K., Kulkarni R., Thamizhavel A., Fiebig M., Pal S.
European Physical Journal B, 92, 131 (2019)
Pandey S.K., Pareek T.P.
CoRR / Nature Partner Journals, 2025
Gu Q., Pandey S.K., Zhouyin Z.
Other research areas
Exploring spin-orbit assisted Mott insulators and 3d/4d/5d transition metal compounds as QSL candidates using ab initio and renormalization group methods.
Developing efficient deep learning-based tight-binding approaches with ab initio accuracy for billion-atom-scale electronic simulations at finite temperatures.
Investigating cuprate analogues like AgF₂ using random phase approximation and fluctuation exchange to identify high-Tc unconventional superconductors.
Using dynamical mean-field theory (DMFT) to study how dynamic electronic correlation enhances orbital magnetization in layered materials.
Studying displacive topological phase transitions and hidden-order phases in Weyl semimetals via first-principles and effective theory.