Accelerated chemical science with AI

Seoin Back*, Alán Aspuru-Guzik, Michele Ceriotti, Ganna Gryn'ova, Bartosz Grzybowski, Geun Ho Gu, Jason Hein, Kedar Hippalgaonkar, Rodrigo Hormázabal, Yousung Jung, Seonah Kim, Woo Youn Kim, Seyed Mohamad Moosavi, Juhwan Noh, Changyoung Park, Joshua Schrier, Philippe Schwaller, Koji Tsuda, Tejs Vegge, O. Anatole von LilienfeldAron Walsh

*Corresponding author for this work

Research output: Contribution to journalReviewpeer-review

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Abstract

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of ‘Accelerated Chemical Science with AI’ at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: ‘Data’, ‘New applications’, ‘Machine learning algorithms’, and ‘Education’. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Original languageEnglish
JournalDigital Discovery
Volume3
Issue number1
Pages (from-to)23-33
Number of pages11
ISSN2635-098x
DOIs
Publication statusPublished - 2024

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