(3jh) First-Principles Modeling of Catalysts and Electrolytes for a Low-Carbon Future

I earned my B.S. in Chemical Engineering from Montana State University, where I conducted research in antimicrobial food preservation and biofilm engineering. My passion for computational catalysis led me to pursue a Ph.D. at the University of Pittsburgh under the mentorship of Prof. Giannis Mpourmpakis. My doctoral work focused on leveraging density functional theory (DFT), microkinetic modeling (MKM), and kinetic Monte Carlo (kMC) simulations to elucidate complex reaction mechanisms in heterogeneous catalysis, with applications ranging from alkane dehydrogenation, photocatalytic methane conversion, stereoselective directed hydrogenation, hydrogen spillover, and electrochemical looping reactions. I collaborated with experimentalists across multiple institutions to bridge computational insights with real-world catalytic performance. Currently, I am a postdoctoral fellow at the University of California, Berkeley, working with Prof. Kristin Persson and Prof. Omar Yaghi. My research integrates quantum chemistry, chemical reaction networks (CRNs), and machine learning (ML) to understand electrolyte decomposition in metal-ion batteries and to design novel metal-organic frameworks for carbon capture and water harvesting. At the heart of my work is a drive to accelerate the discovery of materials and mechanisms that enable cleaner energy, more efficient chemical processes, and a more sustainable technological future. I am committed to building a career that bridges disciplines, connects theory with practice, and empowers the transition to a low-carbon economy.

Research Interests

I am broadly interested in using computational modeling and data science to tackle fundamental challenges in catalysis and electrochemical energy storage. My research aims to bridge the gap between computational predictions and experimental realization, leveraging fundamental thermodynamics, kinetics, and transport phenomena to guide catalyst and electrolyte design. A key question driving my work is: How can we rationally design catalysts with enhanced selectivity and activity? How can we computationally map and predict complex reaction networks in energy storage systems?

During my Ph.D., we developed descriptor-based screening methodologies to design and optimize catalysts for alkane dehydrogenation (DH), a key process in fuel and chemical production. By integrating DFT, MKM, and structure–activity relationships (SARs), we provided mechanistic insights into alkane DH on pristine and Ga-doped γ-Al₂O₃. Building on this foundation, we extended our SAR framework from metal oxides to nitrides, identifying pristine aluminum nitride (AlN) as an efficient DH catalyst. Additionally, we explored heterometal doping as a strategy to enhance DH performance, demonstrating that Zn-doped AlN exhibits significantly higher DH activity compared to pristine AlN and traditional oxide catalysts. Importantly, we elucidated complex hydrocarbon DH mechanisms on previously untested AlN catalysts, showcasing the power of computation-driven catalyst discovery. Proof-of-concept experiments further validated these predictions, confirming that Zn-doped AlN exhibits (i) a higher intrinsic reaction rate, (ii) significantly lower apparent activation energy, and (iii) slightly higher propane conversion compared to pristine AlN. These insights provide a valuable framework for designing efficient DH catalysts and bridging theoretical predictions with experimental validation.

Building on my expertise in computational catalyst design, I expanded my research to methane activation, a critical challenge in sustainable hydrocarbon utilization. Methane's inert nature makes its selective conversion under mild conditions particularly difficult, but photocatalysis presents an exciting opportunity to achieve this. Using a multiscale modeling approach, we revealed the mechanism of photocatalytic nonoxidative coupling of methane on rutile TiO₂. Our findings revealed that photogenerated holes dramatically lower the activation barrier for methane dissociation, enabling methyl radical formation and subsequent ethane production at mild temperatures (315 K). However, we identified hydrogen poisoning as a key limitation, which we addressed by introducing a dynamic catalysis strategy. By elevating temperature to only 690 K, we facilitated hydrogen evolution and catalyst regeneration, ensuring sustained activity. This work not only provides fundamental insights into methane activation but also establishes a new paradigm in dynamic catalysis, where photonic and thermal energy are synergistically leveraged to drive complex hydrocarbon transformations.

In my postdoctoral research, I use high-throughput computation, machine learning, and chemical reaction network analysis to unravel the complex chemistry of solid-electrolyte interphase (SEI) formation in lithium-ion batteries. By constructing extensive reaction networks, we identify both known and previously unrecognized SEI products, shedding light on electrolyte decomposition mechanisms. These newly identified SEI products are critical because they provide insights into the chemical interactions that govern battery performance and degradation. Importantly, some of these new molecules have been confirmed by mass spectrometry. My goal is to establish a predictive framework that links molecular-level electrolyte chemistry with macroscopic battery behavior, enabling the design of more stable, efficient, and longer-lasting energy storage systems.

Moving forward, I am driven by the goal of transforming the way we design materials to enable a sustainable energy future. By leveraging machine learning and high-throughput computations, I aim to accelerate the discovery of innovative electrochemical materials and catalytic processes that can address key global challenges, such as energy storage efficiency, carbon capture, and clean energy conversion. My objective is to create scalable solutions that accelerate the transition to a low-carbon, energy-efficient world.

Teaching Interests

Engineering is a constantly evolving field. Ask someone from 40 years ago, and they would not have relied on computers and calculators as extensively as we do today; now, these tools are indispensable. What truly matters is having a deep grasp of the fundamentals and the skills to learn and adapt. This is where my teaching approach comes in. I aim to empower students not just with knowledge, but with the ability to tackle tomorrow's challenges, helping them become innovators, problem-solvers, and future leaders shaping the world.

My teaching approach has been recognized not only through formal awards, such as the Engineering Graduate Student Organization Award for Outstanding Teaching Assistant in the Chemical Engineering Department, but also through the words of my students. Their feedback, describing me as “fantastic at explaining concepts,” “always willing to help,” and someone who “cares about our success”, reminds me why I am so passionate about education.

Looking ahead, I am excited to teach core chemical engineering courses like thermodynamics, transport phenomena, and reaction engineering while also developing advanced courses that integrate machine learning, quantum chemistry, and materials informatics. Ultimately, my mission is not just to teach concepts but to transform the way students see themselves as innovators, problem solvers, and future leaders shaping the world.

Selected Research Works

1. Abdelgaid, M.; Perera, S.; Rashad, A.; Porosoff, M. & Mpourmpakis, G. Transition metal-doped aluminum nitride catalysts for propane dehydrogenation. Chemical Engineering Journal, 510, 161774 (2025).

2. Abdelgaid, M.; Miu, E., V.; Kwon, H.; Kauppinen, M.; Grönbeck, H. & Mpourmpakis, G. Multiscale modeling reveals aluminum nitride as an efficient propane dehydrogenation catalyst. Catalysis Science & Technology, 13, 3527-3536 (2023).

3. Abdelgaid, M. & Mpourmpakis, G. Structure–Activity Relationships in Lewis Acid–Base Heterogeneous Catalysis. ACS Catalysis, 12, 4268–4289 (2022).

4. Abdelgaid, M.; Dean, J. & Mpourmpakis, G. Improving alkane dehydrogenation activity on γ-Al₂O₃ through Ga doping. Catalysis Science & Technology, 10, 21, 7194-7202 (2020).

5. Roy, J.; Abdelgaid, M.; Grönbeck, H. & Mpourmpakis, G. Dynamic Catalysis Multiscale Simulations for Nonoxidative Coupling of Methane Using Light and Heat. ACS Catalysis, 15, 1195–1205 (2025).

6. Li, H.; Abdelgaid, M.; Paudel, J.; Holzapfel, N.; Augustyn, V.; McKone, J. R.; Mpourmpakis, G. & Crumlin, E. Operando Unveiling Platinum-induced Hydrogen Interaction with Tungsten Trioxide. Journal of the American Chemical Society, Cover Feature, 147, 8, 6472–6479 (2025).