Saquib Ahmed, PhD

Saquib Ahmed

Founder and Director Technology Building 115
Phone: (716) 878-6002


Ph.D., Materials Science Engineering, Rutgers University (NJ)

M.S., Physics, Rutgers University (NJ)

B.S., Industrial Engineering, Economics, Columbia University (NY)

B.A., Physics, Math, Franklin & Marshall College (PA)

Research Focus

  • Clean Energy –Solar devices, Batteries, and Supercapacitors
  • Quantum Materials and Phenomena
  • Magnetic Materials
  • Nanocomposites for Sensor applications
  • Catalysis

Modes of Research

  • Computational: using Density Functional Theory (DFT), classical Molecular Dynamics (MD), Ab initio Molecular Dynamics (AIMD)
  • Accelerated Discovery: traditional Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNN), home-grown python scripts for image recognitions studies, and applications of 2D nanomaterials

Research website

CISNN-Specific Research

Dr. Ahmed focuses to intersect his research in device physics and materials phenomena with accelerated discovery, keeping a keen pulse on the economic viability modeling of the technology he is probing, and importantly invoking the ethical, legal, and social implications of the given technology and research avenue.

CISNN specific publications, white papers, grants, and other resources

  • Carbonara, Ahmed*, et al. “A Machine Learning Approach to Delineate Impact of Materials Properties on Solar Cell Device Physics”, ACS Omega, JUST ACCEPTED, May 2022. {Clean Energy: Device Physics, Computation, ML, Ethics}
  • Carbonara, Ahmed*, et al. “Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI3−xBrx)”, ACS Applied Materials and Interfaces, published December 2021. {Clean Energy: Device Physics, Computation, ML, Ethics}
  • Carbonara, Ahmed, Biswas* “Superoxide Mediated Transition Metal Oxides for Aerobic Oxidation Reactions”, DOE White Paper, 2022 (did not ultimately get invited) {Catalysis, Energy and Reaction Engineering: Experiment, Computation, ML}