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From Concepts to Implementation: Machine Learning Accelerated Nanoporous Materials Discovery
					
 Nov 24, 2023 
 2:30PM to 3:30PM

									

				

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							BIMR Seminar Mohamad Moosavi - all details on event page							

															

						

						

	

		
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	Date/Time

	Date(s) - 24/11/2023
2:30 pm - 3:30 pm





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Prof. Mohamad Moosavi


Chemical Engineering and Applied Chemistry, University of Toronto


 


Abstract:


Nanoporous materials, such as metal-organic frameworks (MOFs), hold great promise for revolutionizing several key energy-related applications by providing the platform to tailor-make materials with desired properties. However, the design of scalable materials that perform optimally in industrial processes remains a complex and slow process using conventional scientific methods. This complexity arises from the need for a holistic perspective over the entire materials design and discovery process, which involves exploring immense materials spaces and linking them to their properties, synthesis, as well as process design and engineering. To address this challenge, we are developing novel tools from the field of machine learning and artificial intelligence, which are particularly suited to navigate the vast design space and uncover hidden relationships between materials’ properties and their performance.


In my talk, I will demonstrate how these novel approaches are accelerating the nanoporous materials design by discussing our efforts on quantifying the structural diversity of large MOF material databases, developing physics-constrained machine learning methods for low-data design tasks (thermal properties), learning from failures to design better experiments, and performing inverse design of materials.


 


Bio:


Started in 2023, Mohamad Moosavi is an Assistant Professor of Chemical Engineering and Applied Chemistry at the University of Toronto and a Faculty Affiliate at the Vector Institute for Artificial Intelligence. Mohamad directs the Artificial Intelligence for Chemical Science (AI4ChemS) research group, focusing on leveraging AI and computational methods for the discovery of advanced materials. His team’s current research is concentrated on developing MOFs and nanoporous materials for carbon capture and conversion, aiming to contribute to technology development for our sustainable future. Mohamad’s academic journey began with an undergraduate degree in Mechanical Engineering from Sharif University of Technology, Iran, followed by a PhD in Chemistry and Chemical Engineering from EPFL Switzerland, and a postdoctoral fellowship in Mathematics and Computer Science at the Free University of Berlin, Germany.


 


In-Person: ABB 102


Online: https://mcmaster.zoom.us/j/93995402535


Meeting ID: 939 9540 2535


Passcode: 314010