透视图(图形)
领域(数学)
计算机科学
多样性(政治)
成熟度(心理)
人工智能
化学
数据科学
社会学
心理学
数学
发展心理学
人类学
纯数学
作者
Austin H. Cheng,Cher-Tian Ser,Marta Skreta,Andrés Guzmán-Cordero,Luca Thiede,Andreas Burger,Abdulrahman Aldossary,Shi Xuan Leong,Sergio Pablo‐García,Felix Strieth‐Kalthoff,Alán Aspuru‐Guzik
摘要
Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.
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