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

The overarching goal of my research is to accelerate catalyst discovery via a wide range of computational methods. In particular, my work shows how combining physical models, high-throughput simulations, density functional theory (DFT), and machine learning (ML) can elucidate different aspects of materials discovery and catalyst design in thermal and electrocatalysis.

Research Interests

Heterogeneous Catalysis

Computational Chemistry & modelling

Electrochemical systems

Surface Chemistry

Lithium-ion batteries

Doctoral Research

Department of Chemical Engineering, Pennsylvania State University

Advisor: Dr. Michael Janik, Dr. Robert Rioux

During my research career as a PhD student at the Pennsylvania State University (Fall 2016 - Apr 2021), I worked on optimizing diverse chemical reactions including hydrodeoxygenation (HDO) of biomass derivatives (towards biofuels) and CO oxidation over the two extremes of catalytic systems - one at TiO₂ encapsulated interface on Pd nanoparticles and the other at the limit of atomic Pd supported on ceria (single atom catalyst). These were simulated through Density Functional Theory (DFT) to predict optimal catalytic systems through a descriptor-based design model consisting of electronic and structural properties. My PhD dissertation is titled ‘Tuning metal-support interactions for catalysis at multi-component interfaces using ab - initio and statistical methods guided by experiments’. These external and internal collaborative projects with universities, and members with expertise from diverse backgrounds trained me to look at research problems from a multi-disciplinary perspective and helped me solve problems in a diverse team environment.

Postdoctoral Research

Department of Chemical Engineering, Stanford University

SUNCAT@SLAC (Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory

Advisor: Dr. Frank Abild-Pedersen, Dr. Johannes Voss, Dr. Kirsten Winther

Since May 2021, I've been working as a postdoctoral researcher at the Stanford University and SLAC National Laboratory, where I learned that the chemical space of materials to investigate for any application might be painfully enormous, as well as difficult and time-consuming to test experimentally. As a result, I'm learning how to employ a high-throughput exploration (using machine learning (ML) methods) and computation of unexplored materials of high interest, and their relevant properties (for various reactions), based on a large number of bulk prototype materials including antimonates, oxides, pyrochlores, alloys, nitrides, and sulfides. In addition, I noticed that an online library for the field of heterogeneous catalysis that integrates theory with experimental aspects (of materials data) such as spectroscopic measurements, voltametric maps, diffraction patterns, catalyst characterization information, and so on is currently absent. Such an integrated database, in my opinion, will be beneficial in the search for new materials. These are in line with my future research interests, which include how physical models, high-throughput simulations, DFT, and ML methods may be used to understand many aspects of materials discovery and catalyst design in thermal and electrocatalysis (towards renewable resources and sustainable solutions).

Research highlights:

In the realm of heterogeneous catalysis, whether thermal or electrochemical, physical models supplemented by Machine learning (ML) models can accelerate the discovery of inexpensive and abundant catalysts with high activity, selectivity, and stability. These models come very handy since the chemical space of materials to explore can be quite large, as well as difficult and time-taking to be tested experimentally. Therefore, we propose physical insights complemented ML approaches for rational catalyst design for several applications.

In the near term, my lab will collaborate with experimental groups to simulate and understand experimental data on sintering and materials stability towards catalyst design under several reactions conditions of temperature, pressure and pH. These would further improve the physical models towards design of catalytic active sites. My lab would also model the effects of additive chemistries and experiemental features to design realistic and novel OxR active sites for electrocatalysis using ML approach. I will collect high-throughput experimental data to confirm the atomistic results while also developing machine learning algorithms to connect the two scales. Long-term, autonomous material design and quick hypothesis testing for various applications will be made possible by the combination of data collection and mechanistic insights gained through machine learning.

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. 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
2. 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
3. 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
4. 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
5. 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