(6bh) Molecular Modeling and Machine Learning for Catalysis and Separations

Research Interests:

As earth’s population grows and the standard of living in developing nations improves, demand for energy, water, and agricultural products will increase. These will interact in complex ways as production increases to meet demand; the NSF has even prioritized these challenges in their Innovation at the Nexus of Food, Energy, and Water Systems (INFEWS) initiatives. Human and animal wastes have been viewed as environmental and sanitary liabilities, but they could be viable renewable resources instead. Technologies that economically convert biomass in wastewater to useful fuels and chemicals could conserve energy and clean water, making fresh water available for human or animal consumption or irrigation. In addition, as new biomass pyrolysis, dehydration, and hydrodeoxygenation processes approach commercialization, new water treatment processes will need to be developed to clean up process water, which often contain compounds toxic to microbes and resistant to traditional wastewater treatment.

Chemical transformation and solute-substrate interactions are at the heart of any such future technologies, creating a significant opportunity for applying catalytic science to water treatment. Moreover, in this age of computer-aided catalyst design, progress toward optimal catalyst and reactor designs can be accelerated by utilizing modeling tools to identify and alleviate bottlenecks in reaction networks and in processes, as well as to aid in the search for high-performance materials. As vast libraries of materials are created in lab and in silico, extracting actionable information from these big data can be difficult. Techniques from machine learning and data science can be harnessed to identify critical trends and descriptors, as well as build predictive models for discovery.

Developing these technologies will require an interdisciplinary and collaborative approach, bringing together the insights and methodologies of chemical, environmental, and civil engineers. During my PhD, I developed expertise in reaction engineering, catalysis, and computational chemistry for processing biomass into fuels and chemicals, and in my current post doc, I am acquiring new skills for modeling separation processes and designing new materials in silico, as well as applying new machine learning techniques. Leveraging these techniques in collaboration with experimentalists will accelerate progress toward a more sustainable future.

Research Experience

During my Ph.D. with Prof. Dion Vlachos, I applied molecular dynamics and electronic structure calculations to investigate fundamental interactions between solvents, solutes, and catalysts in biomass catalysis. In my first project, I discovered how co-solvent interactions protect biomass-derived furans from side reactions in fructose dehydration [1] [2]. I developed structure-activity relationships for homogeneous Lewis acids to probe mechanistic details in glucose isomerization [3] [4]. I also exhaustively screened site geometries for Sn-Beta, identifying the most stable active geometry and proposing a source for its Brønsted acidity [5]. In my final project, which I continued during my post doc, I developed the reaction mechanism for fructose etherification on the hierarchical Sn-SPP zeolite [7].

In my current post doc with Prof. Ilja Siepmann, I am developing and applying Monte Carlo and molecular dynamics simulations in five projects: 1) predicting solution-phase adsorption into hierarchical zeolites under reaction conditions [8], 2) investigating adsorption of organics at the air-water surface [6], 3) calculating the viscosities of novel lubricants, 4) predicting thermophysical properties of hydrocarbons, and 5) developing molecular modeling and machine learning techniques to predict adsorption isotherms.

Teaching Interests:

Excellence in teaching has the potential to multiply progress made toward a more sustainable future through the engineers who will come after us.

I have served as a teaching assistant in both undergraduate and graduate thermodynamics courses, and I have been a guest lecturer in kinetics and reaction engineering at 3 institutions. In addition, I organized a computational chemistry workshop for the graduate reaction kinetics course, to guide students through exercises using the same software I use in my research.

I am particularly excited about introducing machine learning and data science into undergraduate and graduate Chemical Engineering education. By broaching these topics in core courses and going deeper in electives, students will become acquainted with cutting-edge data analysis tools and equipped for careers in data science and its intersection with traditional chemical engineering careers.

Courses of expertise: thermodynamics and statistical mechanics, kinetics and reaction engineering, numerical methods and linear algebra, computational chemistry, and engineering statistics.

First-Year Postdoctoral Fellow

Postdoctoral Project: Monte Carlo Simulations for Novel Materials and Process Design

Advisor: Prof. J. Ilja Siepmann, Department of Chemistry and Department of Chemical Engineering and Materials Science, University of Minnesota

PhD Dissertation: Leveraging First-Principles Simulations for Deeper Insights in Biomass Processing

Advisors: Prof. Dionisios Vlachos and Dr. Stavros Caratzoulas, Department of Chemical and Biomolecular Engineering, University of Delaware

AIChE Conference Talk:

[8] Josephson,T. R., Joseph, K.E., Dauenhauer, P. J., and Siepmann, J. I. Solution-Phase Adsorption of Furan and Carboxylic Acid in Hierarchical Zeolites: Insights from Molecular Simulation. Molecular Simulation of Adsorption II, Wednesday, October 31, 2018, 3:30 PM.

Selected Publications

[7] Josephson, T. R., DeJaco, R., Pahari, S., Ren, L., Guo, Q., Tsapatsis, M., Siepmann, J.I., Vlachos, D., Caratzoulas, S. “Cooperative Catalysis by Surface Lewis Acid/Silanol for Selective Fructose Etherification on Sn-SPP Zeolite.” 2018. In Review.

[6] Minkara, M. S., Josephson, T. R., Venteicher, C. L., Chen, J. L., Stein, D. J., Peters, C. J., Siepmann, J. I., 2018. “Monte Carlo simulations probing the liquid/vapour interface of water/hexane mixtures: adsorption thermodynamics, hydrophobic effect, and structural analysis.” Molecular Physics. doi: https://doi.org/10.1080/00268976.2018.147123

[5] Josephson, T. R., Jenness, G. R., Caratzoulas, S., Vlachos, D. G., 2017. Distribution of open sites in the Sn-Beta zeolite." Microporous and Mesoporous Materials. doi: 10.1016/j.micromeso.2017.02.065

[4] Josephson, T. R., Brand, S. K., Caratzoulas, S., Vlachos, D. G., 2016. 1,2-H versus 1,2-C-shift on Sn-silsesquioxane." ACS Catalysis, 7. doi: 10.1021/acscatal.6b03128

[3] Brand, S. K., Josephson, T. R., Labinger, J. A., Caratzoulas, S., Vlachos, D. G., Davis, M. E., 2016. Methyl-ligated tin silsesquioxane catalyzed reactions of glucose." Journal of Catalysis. doi: 10.1016/j.jcat.2016.06.013

[2] Josephson, T. R., Tsilomelekis, G., Bagia, C., Nikolakis, V., Vlachos, D. G., and Caratzoulas, S., 2014. Solvent-induced frequency shifts of 5-hydroxymethylfurfural deduced via infrared spectroscopy and ab initio calculations." Journal of Physical Chemistry A. doi: 10.1021/jp508340p

[1] Tsilomelekis, G., Josephson, T. R., Nikolakis, V., and Caratzoulas, S., 2013. Origin of 5-hydroxymethyl furfural stability in water/dimethyl sulfoxide mixtures." ChemSusChem. Cover article. doi: 10.1002/cssc.201300786