The Materials Physics Group, led by Koushik Pal, works on theory and simulation of quantum and energy materials utilizing electronic structure theory, multi-scale modelling, high-performance computing and materials informatics. The overreaching aim of the group is to design highly efficient quantum and energy materials that can be used in faster electronics, quantum computing, and clean energy devices. This research in the group is multidisciplinary and sits at the intersection of solid state physics and materials science. The group interacts closely with leading experimental groups to provide insights into experimental observations, guide experiments, and to to get inspired by experiments in pursuit of solving critical problems in a timely manner and to bridge the gap between theory, simulations and experiments. Recent works from the group have lead to successful experimental realization of weak tological phase and several low-thermal conductivity materials.  

One theme of research in the group is to understand quantum phases of materials such as  topological insulating, Dirac and Weyl semimatallic phases and investigate their effects on various materials properties like electrical and thermal transport using theoretical and computaional approaches. The idea is to develop structure-property relationships that can be used as "design rules" for efficient screening and design of new materials with target properties. The group aims to exploit connections between dispareate materials classes and couple multiple order paerameters - such as topology, thermoelectricity, magnetism, electron-phonon couplimg, strongly correlated electrons, etc. - to tune properties of materials with ease. This theme aligns well with the semiconductor and quantum missions which are onging worldwide.

Another research direction in the group is to perform theory guided design of new materilas which have not been synthesized in the lab yet. This is done either using the "bottiom-up" approach i.e., atomically design new materials from scracth using quantum mechanics or using the "top-down" approach which is essentially screenign potential materials from large materials databases using physics informed ideas. Of course, both these approaches require a set of rigorous stability checks - thermodynamics, dynamic, kinertic, etc., - to be able to make robust predictions. Techniques from machine-learning and ideas of informatics can be applied to materials domain to solve this type of problems quite efficiently, which falls in the area materlals informatics. In terms of materials, the group focuses on semicondutors,  various quantum topolgical materials, thermoelectrics and photovolatics such as halide perovskites.