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Computer Science > Machine Learning

arXiv:2409.10304 (cs)
[Submitted on 16 Sep 2024 (v1), last revised 8 Oct 2024 (this version, v2)]

Title:Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

Authors:Austin 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
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Abstract: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.
Subjects: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Chemical Physics (physics.chem-ph)
Cite as: arXiv:2409.10304 [cs.LG]
  (or arXiv:2409.10304v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2409.10304
Journal reference: Faraday Discuss., 2024
Related DOI: https://doi.org/10.1039/D4FD00153B

Submission history

From: Austin Cheng [view email]
[v1] Mon, 16 Sep 2024 14:10:38 UTC (370 KB)
[v2] Tue, 8 Oct 2024 13:57:20 UTC (447 KB)
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