I specialize in computational materials science, focusing on the structural, electronic, optical, and transport properties of nanostructured systems using density functional theory (DFT). My research also investigates chemical reactivity and bonding to explore structure-property relationships. I design novel materials for nanoelectronics and optoelectronics, with applications in UV absorption, solar cells, and photocatalysis. Additionally, I work on developing nanoscale energy storage devices, such as capacitors, battery anodes, and thermoelectrics, as well as gas sensors for detecting key biological and environmental molecules. A key area of my research is the design of electrocatalysts for CO₂ reduction into valuable chemical fuels, advancing sustainable, carbon-neutral technologies. Currently, I integrate AI/ML and large language models (LLMs) with DFT to accelerate materials screening and discovery. I also contribute to building open-access materials libraries to aid experimentalists in selecting materials for specific applications.
Relevant Skills:
Familiar with Machine Learning Algorithms and Large Language Models.
Familiar with Linux and Windows operating systems.
Experienced in FORTRAN and Python programming languages.
Experienced in GAUSSIAN, SIESTA, QUANTUM ESPRESSO, QUANTUM ATK and VASP DFT codes.
Experienced in Gollum code for thermoelectric calculation.
Experienced in cluster computing and parallel installation of DFT codes.
Experienced in visualization tools like Vesta, XCrySDen, GaussView, Virtual Nano Lab, Avogadro, Multiwfn, etc.