Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
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.
Authors
- Shi Xuan Leong (ORCID: https://orcid.org/0000-0001-8130-0897)
- Austin H. Cheng (ORCID: https://orcid.org/0000-0002-9497-3932)
- Sergio Pablo‐García (ORCID: https://orcid.org/0000-0002-3327-9285)
- Luca Thiede (ORCID: https://orcid.org/0000-0003-1202-6809)
- Felix Strieth‐Kalthoff (ORCID: https://orcid.org/0000-0003-1357-5500)
- Marta Skreta (ORCID: https://orcid.org/0000-0001-6831-8398)
- Alán Aspuru‐Guzik (ORCID: https://orcid.org/0000-0002-8277-4434)
- Abdulrahman Aldossary (ORCID: https://orcid.org/0000-0003-0700-7996)
- Cher Tian Ser (ORCID: https://orcid.org/0000-0003-0597-5758)
- Andreas Burger (ORCID: https://orcid.org/0000-0001-7417-3527)
- Andrés Guzmán-Cordero
Institutions
- Canadian Institute for Advanced Research (CA)
- University of Wuppertal (DE)
- Nanyang Technological University (SG)
- University of Toronto (CA)
- Tinbergen Institute (NL)
- Structural Genomics Consortium (CA)
- Vector Institute (CA)
- University of Amsterdam (NL)
Publication Details
- Journal
- Faraday Discussions
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1039/d4fd00153b
- Citations
- 14
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.64
Funders
- University of Toronto
- King Abdullah University of Science and Technology
- Canada First Research Excellence Fund