Cheng Austin H, Ser Cher Tian, Skreta Marta, Guzmán-Cordero Andrés, Thiede Luca, Burger Andreas, Aldossary Abdulrahman, Leong Shi Xuan, Pablo-García Sergio, Strieth-Kalthoff Felix, Aspuru-Guzik Alán
Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada.
Department of Computer Science, University of Toronto, Toronto, Ontario M5S 2E4, Canada.
Faraday Discuss. 2025 Jan 14;256(0):10-60. doi: 10.1039/d4fd00153b.
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.
机器学习已广泛渗透到许多科学领域。化学和材料科学也不例外。虽然机器学习已经产生了巨大影响,但它仍未充分发挥其潜力或达到成熟阶段。从这个角度来看,我们首先概述机器学习在化学领域各种问题上的当前应用。然后,我们讨论机器学习研究人员如何看待和处理该领域的问题。最后,我们给出在研究用于化学的机器学习时最大化其影响力的思考。