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Closing the Execution Gap in Generative AI for Chemicals and Materials: Freeways or Safeguards

Year: 2024

Type: article

Abstract: GenAI may enable computers to create drugs or sustainable materials. But impact in chemistry happens further downstream, following synthesis, testing, and scale-up. We propose paths for closing this execution gap and creating powerful, safe AIs that can realize novel chemicals.

DOI: 10.21428/e4baedd9.92e511e3

Authors: Akshay Subramanian, Wenhao Gao, Regina Barzilay, Jeffrey C. Grossman, Tommi Jaakkola, Stefanie Jegelka, Mingda Li, Ju Li, Wojciech Matusik, Elsa Olivetti, Connor W. Coley, Rafael Gomez-Bombarelli

Institutions: Massachusetts Institute of Technology, MIT Computer Science and Artificial Intelligence Laboratory, Technical University of Munich

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4393223582

FWCI: 0.662

Cites: 99

Cited by: 7

Related to: 10