SNU CBE

Accelerated chemical science with AI

2023 · Digital Discovery

저자

Seoin Back, Alán Aspuru‐Guzik, Michele Ceriotti, Ganna Gryn’ova, Bartosz A. Grzybowski, Geun Ho Gu, Jason E. Hein, Kedar Hippalgaonkar, Rodrigo Hormázabal, 정유성 (Yousung Jung), Seonah Kim, Woo Youn Kim, Seyed Mohamad Moosavi, Juhwan Noh, Chang-Young Park, Joshua Schrier, Philippe Schwaller, Koji Tsuda, Tejs Vegge, O. Anatole von Lilienfeld, Aron Walsh

연구주제

Machine Learning in Materials Science · Computational Drug Discovery Methods

NTIS 과학기술표준분류

화학 > 융합화학 > 화학정보학

배정 근거: 지원 과제·키워드 근거가 없어 AI가 논문 제목·초록을 읽고 고른 분류(정책 연계 맵과 같은 방법).

AI 연구 키워드·방법

accelerated chemical discovery artificial intelligence in chemistry

기계학습

키워드 (OpenAlex)

artificial intelligence in chemistry machine learning chemical information machine learning algorithms chemistry education materials discovery research acceleration renewable energy materials

초록

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 'Edu…

이 논문을 지원한 국가R&D 과제 (NTIS 성과)

NTIS 성과DB(2020–24)에서 DOI 매칭 없음

인접 그래프

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교수 1

정유성

연구주제 1

Machine Learning in Materials Science

정책분야 1

융합화학

방법 1

기계학습