Deep Arnold Gomes, College of Sciences and Mathematics Faculty and Staff

Professional Experience

Research Associate, Nanomaterials and Soft Matter Lab, University of Dhaka, Dhaka, Bangladesh. Jan. 2023–July 2026
Performed DFT+U calculations in VASP on transition-metal- and nonmetal-co-doped TiO₂ nanotubes and Mn-doped graphene, studying electronic, optical, and dielectric properties. Developed a machine-learning interatomic potential (MACE) for Mn-doped graphene. Experimental work included XRD, UV-vis spectroscopy, AFM, SEM, and magnetization measurements on doped manganites.

Founder and Director, Alpha Brilliance Institute, Dhaka, Bangladesh. Jan. 2018–July 2026
Founded and ran a coaching center for Cambridge O- and A-Level students. Designed and taught courses in physics, chemistry, computer science, and mathematics. Wrote original worksheets, exam papers, mark schemes, and revision notes in mathematics, physics, chemistry, computer science, and English.

Graduate Teaching Assistant, Department of Physics, Auburn University, Auburn, AL. Aug. 2026–present
Engineering Physics I

Innovation

Computational Study of Cu–S Co-Doped TiO₂ Nanotubes
Nanomaterials and Soft Matter Lab, University of Dhaka. Supervisor: Dr. Khandker Saadat Hossain
Used DFT+U calculations in VASP to study how Cu and S co-doping changes the electronic structure, band gap, and charge distribution of anatase TiO₂ nanotubes. Analyses included formation energies, band structures, density of states, and Bader charge analysis.

First-Principles Optical and Dielectric Properties of Anatase TiO₂
Nanomaterials and Soft Matter Lab, University of Dhaka
Built anatase TiO₂ supercells with Python/ASE and computed their optical and dielectric response using DFT+U in VASP. Linked the calculated absorption to a solar-weighted (AM1.5G) absorptance metric to compare photocatalytic performance across structures.

Machine-Learning Interatomic Potential for Mn-Doped Graphene
University of Dhaka. Supervisor: Dr. Alamgir Kabir
Developing a MACE machine-learning interatomic potential trained on DFT data for Mn-doped graphene. The aim is to enable simulations at near-DFT accuracy over larger length and time scales than DFT alone can reach. Validated the model against DFT using energy and force parity analysis.

Experimental Research, M.Sc.
University of Dhaka
Synthesized and characterized doped manganites. Experimental work included magnetization measurements, XRD, UV-vis spectroscopy, AFM, and SEM.