Owing to the diminishing returns of deep learning and the focus on model accuracy, machine learning for chemistry might become an endeavour exclusive to well-funded institutions and industry. Extending the focus to model efficiency and interpretability will make machine learning for chemistry more inclusive and drive methodological progress.
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References
Ivakhnenko, A. G. The group method of data handling, a rival of the method of stochastic approximation. Sov. Automat. Contr. 13, 43–55 (1968).
Keith, J. A. et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem. Rev. 121, 9816–9872 (2021).
Probst, D. et al. Biocatalysed synthesis planning using data-driven learning. Nat. Commun. 13, 964 (2022).
Thompson, N. C., Greenewald, K., Lee, K. & Manso, G. F. Deep learning’s diminishing returns. IEEE Spectr. 58, 50–55 (2021).
Ahmed, N. & Wahed, M. The de-democratization of AI: deep Learning and the compute divide in artificial intelligence research. Preprint at https://arxiv.org/abs/2010.15581 (2020).
Jurowetzki, R., Hain, D., Mateos-Garcia, J. & Stathoulopoulos, K. The privatization of AI research(-ers): causes and potential consequences–from university-industry interaction to public research brain-drain? Preprint at https://arxiv.org/abs/2102.01648 (2021).
Schwartz, R., Dodge, J., Smith, N. A. & Etzioni, O. Green AI. Commun. ACM 63, 54–63 (2020).
Patterson, D. et al. The carbon footprint of machine learning training will plateau, then shrink. Preprint at https://arxiv.org/abs/2204.05149 (2022).
Probst, D. Social and environmental impact of recent developments in machine learning on biology and chemistry research. Preprint at https://arxiv.org/abs/2210.00356 (2022).
Scao, T. L. et al. BLOOM: a 176B-parameter open-access multilingual language model. Preprint at https://arxiv.org/abs/2211.05100 (2022).
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The author thanks Pierre Vandergheynst for hosting them as a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne.
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Probst, D. Aiming beyond slight increases in accuracy. Nat Rev Chem 7, 227–228 (2023). https://doi.org/10.1038/s41570-023-00480-3
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DOI: https://doi.org/10.1038/s41570-023-00480-3
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Accelerating material design with the generative toolkit for scientific discovery
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