Dr. Prajay Patel received his undergraduate education in Chemistry and Mathematics
at the University of Texas at Arlington. While an undergraduate, he had the opportunity
to study computational chemistry, a field at the intersection of chemistry, physics,
math, and computer science. This experience along with
working as a tutor for the math and chemistry departments were the motivating factors
to pursue graduate studies after completing coursework in 2014. At the University
of North Texas, he joined Professor Angela K. Wilson’s team, and following a move
to Michigan State University in 2016, he focused on the development and application
of effective quantum chemical strategies targeting high accuracy thermochemistry and
organometallic catalysis. Upon receiving his PhD in 2019, he did his postdoctoral
appointment with Dr. Cong Liu and Dr. Massimiliano Delferro at Argonne National Laboratory
where he worked on modeling X-ray absorption spectra of surface organometallic catalysts
with data science approaches.
Dr. Patel joined the faculty in August 2022 and primarily teaches the physical chemistry sequence and the Basic Ideas in Forensic Chemistry course. Special topic courses that he would develop and teach include computational chemistry and Python for Scientific Data Visualization.
Patel Research
Research in the Patel group is in the field of computational chemistry, a field of chemistry at the intersection of chemistry, math, physics, and computer science that utilizes computers to tackle chemical problems. With the continuing evolution of computer hardware and software, computational chemistry has become vital to chemistry from the valuable insight it provides into chemical processes and structures, to the prediction of properties that are difficult to measure experimentally. One of the most beneficial aspects of this field is the ability to investigate a broad array of challenges, which includes the development and understanding of computational and cheminformatic methodologies to study transition metals, organic reactions, as well as thermodynamic and spectroscopic properties of molecules and complexes across the periodic table.
Catalysis
We primarily focus on surface organometallic chemistry (SOMC) and how catalytic design
of amorphous supports can be modulated to affect both the thermodynamics and kinetics
of catalytic reactions targeting polymer upcycling, which helps reduce plastic waste
and renew plastic products.
Cheminformatics
As artificial intelligence (AI) techniques become more prevalent in day-to-day life,
exploring the chemical space at large is a relatively new frontier, and will lead
to enhanced descriptions of chemical processes and structures. Research in our group
focuses on understanding fundamental relationships between the molecular structure
and various chemical properties such as the pKa for drug-like molecules and the peaks
and intensities for absorption spectroscopy. This paper focused on integrating methods
from quantum mechanics (QM), molecular dynamics (MD), and machine learning (ML) to
predict thermochemical properties of drug-like molecules. (Reproduced from Phys. Chem.
Chem. Phys., 2024, 26, 7907 with permission from the PCCP Owner Societies.)
Collaborative Research
The Patel group has an ongoing collaboration with staff scientists at Argonne National
Laboratory (ANL) focusing on modeling various catalytic reactions including single
atom catalysts for polymer upcycling and electrocatalysts for fuel cells.
We also collaborate with other faculty members of the Chemistry Department. This includes modeling the reaction networks of aryne-boron chemistry and dye degradation reactions on semiconductor materials.
This paper focused on using lithium-ion battery materials as tunable supports for single atom catalysts. (Reprinted with permission from ACS Catal., 2022, 12, 7233-7242. Copyright 2022 American Chemical Society)
Selected Publications
(Google Scholar: https://scholar.google.com/citations?hl=en&user=M-09UlAAAAAJ)
1. Draper, M. R.; Waterman IV, A.; Dannatt, J. E.; Patel, P.* Integrating Multiscale
and Machine Learning Approaches towards the SAMPL9 LogP Challenge, Phys. Chem. Chem.
Phys., 2024, 26, 7907-7919. https://doi.org/10.1039/D3CP04140A.
2. Patel, P.*; Pillai, N.; Toby, I. No-Boundary Thinking for Artificial Intelligence
in Bioinformatics and Education, Front. Bioinform., 2024, 3, 1332902. https://doi.org/10.3389/fbinf.2023.1332902.
3. Xu, J.; Lund, C; Kim Y.; Patel, P; Liu, C. Recent Advances on Computational Modeling
of Supported Single-Atom and Cluster Catalysts: Characterization, Catalyst-Support
Interaction, and Active Site Heterogeneity, Catalysts, 2024, 14, 224. https://doi.org/10.3390/catal14040224.
4. Patel, P.; Wells, R. H.; Kaphan, D. M.; Delferro, M.; Skodje, R. T.; Liu, C. Computational
Investigation of the Role of Active Site Heterogeneity for a Supported Organovanadium(III)
Hydrogenation Catalyst. ACS Catal., 2021, 11 (12), 7257-7269 https://doi.org/10.1021/acscatal.1c00688.
5. Patel, P. and Wilson, A. K. Domain-based Local Pair Natural Orbital Methods within
the Correlation Consistent Composite Approach. J. Comput. Chem., 2020, 41, 800–813.
https://doi.org/10.1002/jcc.26129.