About
On the record
I am actively seeking a PhD position in computational materials science, specializing in density functional theory (DFT) and machine learning. My expertise spans Quantum ESPRESSO, SCAPS, and Python-based data science, machine learning, and deep learning. With a strong foundation in first-principles calculations, I have extensively analyzed electronic, optical, mechanical, and thermoelectric properties using Quantum ESPRESSO and BoltzTraP. Additionally, I have honed my skills in SCAPS simulations to evaluate solar cell performance. To accelerate the discovery of high-performance materials, I leverage advanced machine learning techniques, incorporating customized features for targeted applications. My proficiency in web scraping further enables me to extract and curate valuable structured datasets for materials informatics. By integrating computational physics with artificial intelligence, I aim to push the boundaries of materials research, uncovering innovative materials with tailored properties for next-generation technologies.
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