Oxide Thermoelectric Materials
In this project we are investigating the phonon scattering mechanisms in nanostructured ZnO. The idea is to introduce multiscale crystal defects, e.g. grain boundaries, dopants, precipitates to enhance the hierarchical phonon scattering at different length scales. Simulations will be performed to calculate the effect of nanostructuring on phonon properties and thereby, the thermal conductivity of ZnO. We are also conducting experiments to introduce nanoscale defects and characterize their influence on the thermoelectric properties of ZnO.
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Segregation of Al on (10.0) grain boundary in ZnO
Hybrid Perovskites for Solar and Battery Applications
Hybrid halide perovskites have emerged rapidly in the last decade as one of the most promising class of materials for solar energy harvesting. They have shown high efficiency and can be a cheaper alternative to current Si based solar cells. However, their stability is one of the main concerns. We are using force field atomistic simulation methods to understand the behavior of these materials under different conditions such as, temperature, humidity etc. We will also study the effect of ionic migration on structural stability of pervoskites.
Self diffusion of Iodine atoms in CH3NH3PbI3 simulated using nudged elastic band method.
Materials Informatics
We are developing novel machine learning models to address the problem of small datasets encountered frequently in the materials informatics. Building machine learning models accounting for the experimental uncertainty is another area, which we have been studying in our group. We are using generative algorithms for designing crystal structures for targeted properties.
A two-step machine learning approach to classify and predict low thermal conductivity transition metal oxides.
Funding Agencies
