Seyed Mohamad Moosavi

Assistant Professor (starting January 1, 2023)
B.Sc. (Sharif Univ.), M.Sc. & Ph.D. (EPFL)
Principal Investigator, AI4ChemS group and Faculty Affiliate at the Vector Institute for Artificial Intelligence

Room: WB365 | Tel.: 416-978-7532 | Email: mohamad.moosavi@utoronto.ca

New Projects Available

• Large Language Models for Chemical Discovery
• Deep Learning for Chemical Geometry
• Materials Discovery for Carbon Capture and Battery thermal Cooling

Accepting graduate students? Yes

• PhD & MASc

Memberships

American Chemical Society (ACS)
Vector Institute for Artificial Intelligence
Acceleration Consortium

Awards

ISIC Award: Best Ph.D. thesis in Chemistry and Chemical Engineering at EPFL (2020)
Postdoctoral Fellowship, Swiss National Science Foundation (2020)
Doctoral Mobility Fellowship, Swiss National Science Foundation (2018)

Research Interests

The Artificial Intelligence for Chemical Science (AI4ChemS) group will be a highly interdisciplinary research unit working at the intersection of machine learning, computation, and their applications in innovation in accelerated materials design and discovery. We have strong expertise in developing machine learning and computational methods in the field of reticular chemistry and porous materials, including metal-organic frameworks, covalent organic frameworks, and zeolites. We aim to solve methodological challenges to eventually enable the autonomous multi-scale design of these materials for our global sustainability challenges, for example in carbon capture, energy storage, and catalysis applications.

AI4ChemS is residing in the Chemical Engineering and Applied Chemistry department at UofT and is affiliated with the Vector Institute for Artificial Intelligence. This provides an environment where different disciplines are bridged through close collaboration of chemical engineers, chemists, physicists, and computer scientists toward our common research goals.

Our principle research areas include:

• Physics-constrained deep learning methods to improve the computational data quality
• Inverse design strategies, e.g., deep generative models, for accelerated materials discovery
• Machine learning methods to develop realistic design objectives for multi-scale design
• Developing and maintaining materials datasets

Selected Publications

The complete list is on google scholar.

• Moosavi, S.M., Novotny, B.Á., Ongari, D., Moubarak, E., Asgari, M., Kadioglu, Ö., Charalambous, C., Guerrero, A., Farmahini, A.H., Sarkisov, L. and Garcia, S., 2022. A Data-Science Approach to Predict the Heat Capacity of Nanoporous Materials. Nature Materials, in press.
• Moosavi, S.M., Jablonka, K.M. and Smit, B., 2020. The role of machine learning in the understanding and design of materials. Journal of the American Chemical Society, 142(48), pp.20273-20287.
• Boyd, P.G., Chidambaram, A., García-Díez, E., Ireland, C.P., Daff, T.D., Bounds, R., Gładysiak, A., Schouwink, P., Moosavi, S.M., Maroto-Valer, M.M. and Reimer, J.A., Navarro, J., Woo, T., Garcia, S., Stylianou, K., Smit, B., 2019. Data-driven design of metal–organic frameworks for wet flue gas CO2 capture. Nature, 576(7786), pp.253-256.
• Moosavi, S.M., Nandy, A., Jablonka, K.M., Ongari, D., Janet, J.P., Boyd, P.G., Lee, Y., Smit, B. and Kulik, H.J., 2020. Understanding the diversity of the metal-organic framework ecosystem. Nature communications, 11(1), pp.1-10.
• Moosavi, S.M., Chidambaram, A., Talirz, L., Haranczyk, M., Stylianou, K.C. and Smit, B., 2019. Capturing chemical intuition in synthesis of metal-organic frameworks. Nature communications, 10(1), pp.1-7.
• Lee, Y., Barthel, S.D., Dłotko, P., Moosavi, S.M., Hess, K. and Smit, B., 2017. Quantifying similarity of pore-geometry in nanoporous materials. Nature communications, 8(1), pp.1-8.
• Moosavi, S.M., Boyd, P.G., Sarkisov, L. and Smit, B., 2018. Improving the mechanical stability of metal–organic frameworks using chemical caryatids. ACS central science, 4(7), pp.832-839.
• Boyd, P.G., Moosavi, S.M., Witman, M. and Smit, B., 2017. Force-field prediction of materials properties in metal-organic frameworks. The journal of physical chemistry letters, 8(2), pp.357-363.