Accelerated chemical science with AI

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dc.contributor.authorBack, Seoin
dc.contributor.authorAspuru-Guzik, Alan
dc.contributor.authorCeriotti, Michele
dc.contributor.authorGryn'ova, Ganna
dc.contributor.authorGrzybowski, Bartosz
dc.contributor.authorHo Gu, Geun
dc.contributor.authorHein, Jason
dc.contributor.authorHippalgaonkar, Kedar
dc.contributor.authorHormazabal, Rodrigo
dc.contributor.authorJung, Yousung
dc.contributor.authorKim, Seonah
dc.contributor.authorYoun Kim, Woo
dc.contributor.authorSchwaller, Philippe
dc.contributor.authorTsuda, Koji
dc.contributor.authorVegge, Tejs
dc.contributor.authorvon Lilienfeld, O. Anatole
dc.contributor.authorWalsh, Aron
dc.contributor.organizationInstitute of Emergent Materials, Sogang University, Seoul, Republic of Korea
dc.contributor.organizationUniversity of Toronto, Canada
dc.contributor.organizationAcceleration Consortium and Vector Institute for Artificial Intelligence, Canada
dc.contributor.organizationEcole Polytechnique Federale de Lausanne, Switzerland
dc.contributor.organizationHeidelberg Institute for Theoretical Studies (HITS gGmbH), Germany
dc.contributor.organizationInterdisciplinary Center for Scientific Computing, Heidelberg University, Germany
dc.contributor.organizationCenter for Algorithmic and Robotized Synthesis, Institute for Basic Science, Republic of Korea
dc.contributor.organizationInstitute of Organic Chemistry, Polish Academy of Sciences
dc.contributor.organizationDepartment of Chemistry, Ulsan National Institute of Science and Technology, Republic of Korea
dc.contributor.organizationDepartment of Energy Engineering, Korea Institute of Energy Technology (KENTECH), Republic of Korea
dc.contributor.organizationUniversity of British Columbia, Canada
dc.contributor.organizationSchool of Materials Science and Engineering, Nanyang Technological University, Singapore
dc.contributor.organizationInstitute of Materials Research and Engineering, Agency for Science Technology and Research, Singapore
dc.contributor.organizationLG AI Research, Seoul, Republic of Korea
dc.contributor.organizationKAIST, Republic of Korea
dc.contributor.organizationSchool of Chemical and Biological Engineering, Interdisciplinary Program in Artificial Intelligence, Seoul National University, Republic of Korea
dc.contributor.organizationDepartment of Chemistry, Colorado State University, USA
dc.contributor.organizationChemical Engineering and Applied Chemistry, University of Toronto, Canada
dc.contributor.organizationChemical Data-Driven Research Center, Korea Research Institute of Chemical Technology, Republic of Korea
dc.contributor.organizationFordham University, The Bronx, USA
dc.contributor.organizationGraduate School of Frontier Sciences, The University of Tokyo, Japan
dc.contributor.organizationNational Institute for Materials Science, Tsukuba, Japan
dc.contributor.organizationRIKEN Center for Advanced Intelligence Project, Japan
dc.contributor.organizationTechnical University of Denmark, Copenhagen
dc.contributor.organizationMachine Learning Group, Technische Universitat Berlin and Berlin Institute for the Foundations of Learning and Data, Germany
dc.contributor.organizationImperial College London, UK
dc.contributor.organizationEwha Women's University, Republic of Korea
dc.date.accessioned2024-06-05T14:53:42Z
dc.date.available2024-06-05T14:53:42Z
dc.date.issued2024
dc.description.abstractIn 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.en
dc.description.sponsorshipIITP Korea (No. 2021-0-01343, Artificial Intelligence Graduate School Program for Seoul National University & No. 2021-0-02068, Artificial Intelligence Innovation Hub); NRF of Korea funded by Ministry of Science and ICT (RS-2023-00283902); NCCR Catalysis (grant number 180544).
dc.identifier.citationDigital Discovery, 2024, 3, 23–33. https://doi.org/10.1039/D3DD00213F
dc.identifier.doi10.1039/d3dd00213f
dc.identifier.issn2635-098X
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/24350
dc.language.isoen
dc.publisherRoyal Society of Chemistry
dc.rightsUznanie autorstwa-Użycie niekomercyjne 3.0 Unporteden
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/
dc.sourceDigital Discovery
dc.titleAccelerated chemical science with AI
dc.typearticle
dc.type.versionpublishedVersion
person.identifier.orcidGrzybowski, Bartosz [0000-0001-6613-4261]
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