Mahdi Matt Aarabi, PhD

Affiliated Programs

• Chemistry (BA/BS)

Education

PhD, Computational Chemistry (2019)
Texas Tech University

About Me

I earned my Ph.D. in Physical and Computational Chemistry from Texas Tech University, where my research focused on developing automated computational methods to study hydrogen storage materials. Before joining Widener, I taught a range of chemistry courses at Central Ohio Technical College on the Ohio State University Newark campus, as well as at Texas Tech University. 

With more than a decade of teaching experience, I emphasize active learning and a pragmatic approach in the classroom, encouraging students to connect complex concepts to real-world applications. My goal is to foster curiosity, critical thinking, and confidence in chemistry for students from diverse backgrounds.

Research Interests

• Designing high-resolution DFT calculations, generating precise potential energy surfaces (PES) using automated “On-the-Fly” Crystal code.
• Machine learning–integrated workflows for computational chemistry (DFT, MD, QM) applied to ligand binding in medicine.
• AI-based molecular simulations for drug design using tools such as RDKit, DeepChem, PyTorch, and Jupyter.

Publications

• Aarabi, M. (2024) On-the-fly Crystal: How to reliably and automatically characterize and construct potential energy surfaces. Journal of Computational Chemistry, 45, 1261-1278.
• Aarabi, M. (2023) Quantum dynamical investigation of dihydrogen-hydride exchange in a transition metal polyhydride complex. Journal of Physical Chemistry A, 127, 6385-6399.
• Aarabi, M. (2017) Adsorption of H2 on Ga24N24 cluster; A density functional theory investigation. Vacuum, 143, 209-216

Awards

• Faculty Teaching Excellence Award, COTC at Ohio State University, Newark campus (2025)
• The best upper-division teaching award (second place) in Chemistry at Texas Tech University (2021)