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 Lilienfeld; Aron Walsh

doi:10.1039/D3DD00213F

Accelerated chemical science with AI

Seoin Back,

*^a Alán Aspuru-Guzik,†^bc Michele Ceriotti,

^d Ganna Gryn'ova,

^ef Bartosz Grzybowski,

^ghi Geun Ho Gu,^j Jason Hein,

^k Kedar Hippalgaonkar,

^lm Rodrigo Hormázabal,

ⁿ Yousung Jung,

†^op Seonah Kim,

^q Woo Youn Kim,

^r Seyed Mohamad Moosavi,^s Juhwan Noh,^t Changyoung Park,ⁿ Joshua Schrier,

^u Philippe Schwaller,^v Koji Tsuda,

^wxy Tejs Vegge,

^z O. Anatole von Lilienfeld

†^caaab and Aron Walsh

^acad

Author affiliations

* Corresponding authors

^a Department of Chemical and Biomolecular Engineering, Institute of Emergent Materials, Sogang University, Seoul, Republic of Korea
E-mail: sback@sogang.ac.kr

^b Departments of Chemistry, Computer Science, University of Toronto, St. George Campus, Toronto, ON, Canada

^c Acceleration Consortium and Vector Institute for Artificial Intelligence, Toronto, ON, Canada

^d Laboratory of Computational Science and Modeling (COSMO), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

^e Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany

^f Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany

^g Center for Algorithmic and Robotized Synthesis (CARS), Institute for Basic Science (IBS), Ulsan, Republic of Korea

^h Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland

ⁱ Department of Chemistry, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

^j Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH), Naju, Republic of Korea

^k Department of Chemistry, University of British Columbia, Vancouver, BC, Canada

^l School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore

^m Institute of Materials Research and Engineering, Agency for Science Technology and Research, 2 Fusionopolis Way, 08-03, Singapore 138634, Singapore

ⁿ LG AI Research, Seoul, Republic of Korea

^o Department of Chemical and Biomolecular Engineering, KAIST, Daejeon, Republic of Korea

^p School of Chemical and Biological Engineering, Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea

^q Department of Chemistry, Colorado State University, 1301 Center Avenue, Fort Collins, CO 80523, USA

^r Department of Chemistry, KAIST, Daejeon, Republic of Korea

^s Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada

^t Chemical Data-Driven Research Center, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea

^u Department of Chemistry, Fordham University, The Bronx, NY 10458, USA

^v Laboratory of Artificial Chemical Intelligence (LIAC) & National Centre of Competence in Research (NCCR) Catalysis, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

^w Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan

^x Center for Basic Research on Materials, National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan

^y RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan

^z Department of Energy Conversion and Storage, Technical University of Denmark, 301 Anker Engelunds vej, Kongens Lyngby, Copenhagen, Denmark

^aa Departments of Chemistry, Materials Science and Engineering, and Physics, University of Toronto, St George Campus, Toronto, ON, Canada

^ab Machine Learning Group, Technische Universität Berlin and Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

^ac Department of Materials, Imperial College London, London SW7 2AZ, UK

^ad Department of Physics, Ewha Women's University, Seoul, Republic of Korea

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.

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Accelerated chemical science with AI

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