My research focuses on the design and characterization of low-dimensional materials using density functional theory (DFT). I investigate structure–property relationships to optimize materials for hydrogen storage, catalytic activity, and energy conversion applications, with an emphasis on tailoring properties through doping, decoration, and functionalization. I am currently extending my research to energy storage systems, particularly metal–sulfur batteries.
Embedded Files
(1) Metal-Sulfur battery: Selecting anchoring material to reduce shuttle using First principles method.
(1) Metal-Sulfur battery: Selecting anchoring material to reduce shuttle using First principles method.
(2) Material modeling: Designing of novel material using the ab initio method.
(2) Material modeling: Designing of novel material using the ab initio method.
(3) Hydrogen economy:
(3) Hydrogen economy:
(a) Hydrogen production: Searching for a suitable catalyst to produce hydrogen using lower over-potential (low energy).
(a) Hydrogen production: Searching for a suitable catalyst to produce hydrogen using lower over-potential (low energy).
(b) Hydrogen storage: Searching for a suitable 2D material with high gravimetric weight.
(b) Hydrogen storage: Searching for a suitable 2D material with high gravimetric weight.
(4) Quantum dots: Understanding the stability and electronic, optical properties of q-dots.
(4) Quantum dots: Understanding the stability and electronic, optical properties of q-dots.
(5) Thermal conductivity and thermoelectricity: Calculation of thermal conductivity by solving the phonon Boltzman equation (Phono3py, ShengBTE) along with solving the electronic Boltzman equation (Boltztrap) to determine thermoelectric properties.
(5) Thermal conductivity and thermoelectricity: Calculation of thermal conductivity by solving the phonon Boltzman equation (Phono3py, ShengBTE) along with solving the electronic Boltzman equation (Boltztrap) to determine thermoelectric properties.
Technical skills
Programming Language and scripting: Python, Jupyter Notebook, Fortran, Bash, awk, sed, C, foundation of machine learning.
Material modeling Python libraries: ase, pymatgen
DFT codes: VASP, Gaussian, Quantum Espresso,
Other utility codes: Phonopy, Phono3py, ShengBTE/almaBTE, Boltztrap2
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