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S6E3: Jackson Burns: Avoiding Chemical Dead Ends

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Manage episode 482727530 series 2954335
Content provided by Sarah Webb and Krell Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sarah Webb and Krell Institute or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Chemists and chemical engineers have modeled molecules for decades, but artificial intelligence and foundation models offer the prospect that researchers could train models with predictive abilities in one area of chemistry that could be fine-tuned for another. Trustworthy chemistry foundation models could help streamline the experimental time and resources needed to discover new medicines or design new batteries. Massachusetts Institute of Technology Ph.D. student Jackson Burns is working on these questions. He describes the promise and challenges of building foundation models in chemistry, his work on chemprop, and his advice to other researchers interested in working on foundation models for chemistry and science in general.

You'll meet:

Jackson Burns is a Ph.D. student in William Green's chemical engineering group at MIT. He's also a third-year Department of Energy Computational Science Graduate Fellowship (DOE CSGF) recipient. He completed his undergraduate degree in chemical engineering at the University of Delaware.

  continue reading

32 episodes

Artwork
iconShare
 
Manage episode 482727530 series 2954335
Content provided by Sarah Webb and Krell Institute. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sarah Webb and Krell Institute or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Chemists and chemical engineers have modeled molecules for decades, but artificial intelligence and foundation models offer the prospect that researchers could train models with predictive abilities in one area of chemistry that could be fine-tuned for another. Trustworthy chemistry foundation models could help streamline the experimental time and resources needed to discover new medicines or design new batteries. Massachusetts Institute of Technology Ph.D. student Jackson Burns is working on these questions. He describes the promise and challenges of building foundation models in chemistry, his work on chemprop, and his advice to other researchers interested in working on foundation models for chemistry and science in general.

You'll meet:

Jackson Burns is a Ph.D. student in William Green's chemical engineering group at MIT. He's also a third-year Department of Energy Computational Science Graduate Fellowship (DOE CSGF) recipient. He completed his undergraduate degree in chemical engineering at the University of Delaware.

  continue reading

32 episodes

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