Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910640609689600 |
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| author | Cheng, Austin 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 |
| author_facet | Cheng, Austin 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science Cheng, Austin 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 Machine Learning Materials Science Artificial Intelligence Chemical Physics 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. |
| title | Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science |
| topic | Machine Learning Materials Science Artificial Intelligence Chemical Physics |
| url | https://arxiv.org/abs/2409.10304 |