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Research Interest
Organic & Perovskite Solar Cells
Machine Learning for Optoelectronic Devices
Large Language Models (LLMs) for Materials Discovery
Interfacial Energetics and Engineering
Organic Bioelectronics
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Machine Learning For Material Science.
Discovering quantitative structure-property relationship of organic solar cells using machine learning
Focusing and utilising machine learning to understand the hidden trends and redefining the fundamental losses in organic solar cells to enhance the stability and performance.
Developing a model to predict the photovoltaic performance parameters with different donor-acceptor combinations
Data-enabled machine-learning framework employed to predict the energy losses in the polymer:non-fullerene acceptor-based devices.
Photophysics of third-generation solar cell
Here at IIT Delhi, We are exploring the photo-physics of organic and perovskite solar cells using different optoelectronics characterization techniques.
We present a systematic investigation to determine the effects of energetic offset and disorder on different recombination losses in open circuit voltage (VOC) using time-resolved photoluminescence spectroscopy and voltage loss analysis of real fabricated devices.
At IIT-Delhi, We have a custom-built Transient photocurrent and photovoltage (TPC/TPV) measurement system to understand the charge carrier dynamics of solar cells.
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