(2fu) Bridging Thermal and Electrochemical Catalysis: Rational Catalyst Design at Atomic Scales through Physical and Machine Learning Based Insights

OVERVIEW

The overarching goal of my research is to accelerate materials discovery via a wide range of computational methods. My lab will combine physics-based models, high-throughput simulations, ab initio simulation methods like density functional theory (DFT), and machine learning (ML) to discover functional materials and design catalysts for thermal and electrocatalytic reactions. We will target applications that enable use of renewable resources and sustainable processes. The same basic principles and approaches, borrowed from catalysis, will be applied to screen solid polymer electrolytes (SPE) in pursuit of safer and more durable low-cost lithium-ion batteries. These goals will be accomplished in collaboration with experimentalists and our findings will be stored in online databases (theory & experiment) for easy use or reproduction by other researchers.

RESEARCH SUMMARY

In the realm of heterogeneous catalysis, whether thermal or electrochemical, physical models supplemented by machine learning (ML) can accelerate the discovery of inexpensive and abundant catalysts with high activity, selectivity, and stability. Computationally predicting effective materials is essential because the chemical space of candidate materials can be quite large, and coupled with challenges in targeted synthesis, experimental exploration of this material space is prohibitively time-taking. Therefore, I propose physical insights complemented ML approaches for rational catalyst materials design, spanning new classes of materials like high entropy oxides and alloys, dual and single metal atom catalysts. Fundamental insights into complex material systems (through interface and defect engineering) and complex reaction chemistries will deliver sustainable solutions to the energy crisis and the use of renewable energy sources. Materials modeling will be integrated with big-data optimization approaches using collaborator’s experimental data. Bayesian optimization via experimental data reduces the high dimensionality of descriptors space for materials search. This algorithm will be employed towards designing highly conductive polymer electrolytes for the development of next-generation safe lithium-ion batteries.

EARLY CAREER RESEARCH GOALS AND PROJECT AIMS

During my postdoctoral work, I successfully developed physical insights complemented ML approaches towards swift predictions of metal ad-atom diffusion barriers and surface/segregation energies for enhanced understanding of sintering and catalyst durability. Moreover, I successfully integrated experimental and theoretical data (which will be uploaded into an online database cathub, https://www.catalysis-hub.org) for high quality predictions of material performance towards electrochemical reactions (e.g., Oxygen reduction reaction, ORR) using ML techniques. Moreover, my PhD dissertation investigated reaction chemistry (hydrodeoxygenation and CO oxidation) at oxide/metal interfaces that take advantage of the properties of the interfacial components. We used DFT calculations, microkinetic modelling and a Bayesian statistical inference approach to identify dominant reaction networks, form a descriptor-based design model, and predict optimal catalytic activity. I have also worked on a collaborative project to tune the conductivity in PEO₆ electrolyte-based Li-ion batteries where I achieved an order of magnitude increase in room temperature ionic Li⁺ conductivity via defects and interstitial migrations.

Motivated from my previous research experiences and expertise, I propose the following long-term aims with a hierarchy of short-term tasks to be completed within each aim (Figure), to enable computationally driven materials design.

i. To develop physics-based models for the design of stable mixed/high entropy oxide clusters

ii. To computationally guide the design towards high entropy alloy (HEA) catalysis,

iii. To examine dual single metal atoms and their synergy towards catalytic applications, and

iv. To design highly conductive polymer electrolytes by machine learning based screening.

Goals i-iii will enable rational design of catalytic materials, and we will pursue these goals for specific reaction chemistries including the selective degradation of aromatic plastic wastes into target arenes, selective hydrodeoxygenation of biomass-derived compounds over high-entropy alloy/oxides hetero-structure catalysts, dry reforming of methane, and co-reduction of CO₂ and N₂ based small molecules into urea. The reactions are motivated by their relevance to enabling a sustainable energy and chemical infrastructure.

On a bigger picture, ML extracted human-interpretable models along with derived physical insights can accelerate materials discovery and design as well as foster new collaborations through the sharing of ideas via easy access to online database of theory and experimental physicochemical properties.

TEACHING INTERESTS

Giving back to society is one of the humanity's most unselfish acts. Towards this goal, wisdom, in any form, is extremely valuable and should be nourished through personal experience, examined, and then passed on to future generations. These objectives would be realized through teaching courses in chemical sciences or allied fields such as chemical engineering, chemistry, or material sciences.

Through my PhD years, I've taught undergraduate and graduate students the fundamentals of theoretical computational modelling as a mentor. This allowed me to see the teacher-student relationship from the other side of the table (mentor). There were days when I would debug experiments, teach theoretical information on the whiteboard, fulfill research/grant deadlines, and provide emotional support to undergraduate and graduate students going through the ups and downs of life. Such mentoring and teaching expertise as well as teaching assistant experience (Fluid Mechanics and laboratory) prepared me to teach core chemical engineering courses at the graduate and undergraduate level, such as Physical Chemistry, Inorganic Chemistry, Statistical Thermodynamics, Fluid Mechanics and laboratory, Quantum Chemistry, Reaction Kinetics.

In my teaching, I would promote a stimulating learning environment where learners can comfortably identify and address their limitations, needs, goals and interests. I would utilize approaches that promote understanding, retention of concepts through focused and paced meaningful teaching interaction, and lastly one that encourages self-directed learning. My evaluations would include learning-oriented problem statements and open-ended questions for students to explore. My teaching expertise and knowledge enables me to meet a growing need for accessible education in Heterogeneous catalysis and characterization and Computational tools for modelling and revealing their connections and limitations with experimental systems.

SELECTED PUBLICATIONS

My publications can be found via my Google Scholar page(https://scholar.google.com/citations?hl=en&user=ZHEdfbsAAAAJ&view_op=list_works&sortby=pubdate). Below follows a selection of publications:

1. L. Wang ^#, S. Deo ^# (^#equal contribution), A. Mukhopadhyay, NA Pantelis, M. J. Janik, R. Rioux, Emergent Behavior in Oxidation Catalysis over Single-Atom Pd on a Reducible CeO₂ Support via Mixed Redox Cycles, ACS Catalysis 12 (20), 12927-12941

2. S. Deo, M. J. Janik, Predicting an optimal oxide/metal catalytic interface for hydrodeoxygenation chemistry of biomass derivatives, Catalysis Science & Technology, 2021, 11, 5606 - 5618.

3. J. Zhang, S. Deo, M.J. Janik, J. Medlin, Control of molecular bonding strength on metal catalysts with organic monolayers for CO2 reduction J. Am. Chem. Soc. 2020, 142, 11, 5184-5193

4. L. Wang, S. Deo, K. Dooley, M.J. Janik, R. Rioux, Influence of metal nuclearity and physicochemical properties of ceria on the oxidation of carbon monoxide, Chin. J. Catal., 2020, 41: 951–962

5. S. Deo, W. Medlin, E. Nikolla, M.J. Janik, Reaction paths for hydrodeoxygenation of furfuryl alcohol at TiO2/Pd interfaces, Journal of Catalysis, 377 (2019) 28-40

6. D. Gao, I.T. McCrum, S. Deo, Y.-W. Choi, F. Scholten, W. Wan, J.G. Chen, M.J. Janik, B. Roldan Cuenya, Activity and selectivity control in CO2 electroreduction to multicarbon products over CuOx catalysts via electrolyte design, ACS Catal., (2018) 10012-10020

Figure. A schematic representation of physical models supplemented materials screening towards accelerating the discovery of inexpensive and abundant catalysts across thermal and electrochemical domains.