DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jihan | ko |
dc.date.accessioned | 2022-11-18T05:02:28Z | - |
dc.date.available | 2022-11-18T05:02:28Z | - |
dc.date.created | 2022-07-14 | - |
dc.date.issued | 2022-06-14 | - |
dc.identifier.citation | NERSC Data Seminar | - |
dc.identifier.uri | http://hdl.handle.net/10203/299983 | - |
dc.description.abstract | 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. | - |
dc.language | English | - |
dc.publisher | Berkeley Lab | - |
dc.title | Artificial Design of Porous Materials | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | NERSC Data Seminar | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Berkeley, CA | - |
dc.contributor.localauthor | Kim, Jihan | - |
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