Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 36 references that are in the map. First posted on arXiv 16 Sep 2024.
Builds on
4 of 36 references.
References · 4
- 2021Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objectscited by 39
- 2023Matbench Discovery -- A framework to evaluate machine learning crystal stability predictionscited by 34
- 2023Prefix-Tree Decoding for Predicting Mass Spectra from Moleculescited by 16
- 2023Efficiently predicting high resolution mass spectra with graph neural networkscited by 13
Built on by
Citing papers per year: 2025: 1.
1 paper.