In this presentation, I will explore the new trend of designing novel porous materials using artificial design principles. I will talk about using our in-house developed generative adversarial network (GAN) software to create (for the first time) porous materials. Moreover, we have successfully implemented inverse-design in our GAN prompting ways to train our AI to create porous materials with user-desired methane adsorption capacity [1]. Next, we incorporate machine learning with genetic algorithm to design optimal metal-organic frameworks suitable for many different applications including methane storage and gas separations [2-3]. Finally, we demonstrate usage of text mining to collect wealth of data from published papers to predict optimal synthesis conditions for porous materials [4]. Overall, machine learning and artificial design can accelerate the materials discovery and expedite the process to deploy new materials for many different applications.
Jihan Kim is an associate professor at KAIST (Korea Advanced Institute of Science and Technology). He received his B.S. degree in Electrical Engineering and Computer Science (EECS) at UC Berkeley in 1997 and received his M.S. and Ph.D. degrees in Electrical and Computer Engineering at University of Illinois at Urbana-Champaign in 2004 and 2009, respectively. He worked as a NERSC postdoc in the Petascale Post-doc project from 2009 to 2011 and worked as postdoctoral researcher in UC Berkeley/LBNL with Prof. Berend Smit from 2011 to 2013. His current research at KAIST focuses on using molecular simulations and machine learning methods to design novel porous materials (e.g. zeolites, MOFs, porous polymers) for various energy and environmental related applications (e.g. gas storage, gas separations, catalysis, sensors). He has published over 100 papers and has over 7000 Google Scholar citations.