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The case for data science in experimental chemistry: examples and recommendations

Abstract

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to ‘co-design’ chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

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Fig. 1: Role of data science in experimental processes.
Fig. 2: Artificial intelligence and machine learning deployed to accelerate, autonomously control and understand experiments, using state-of-the-art mathematics coupled to advances in data science.
Fig. 3: Application of machine learning to conduct new types of experiments at XFEL facilities.
Fig. 4: Visualizing a data network.
Fig. 5: Interplay of experiments, workflow and data.

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Acknowledgements

This article evolved from presentations and discussions at the workshop ‘At the Tipping Point: A Future of Fused Chemical and Data Science’ held in September 2020, sponsored by the Council on Chemical Sciences, Geosciences, and Biosciences of the US Department of Energy, Office of Science, Office of Basic Energy Sciences. The authors thank the members of the Council for their encouragement and assistance in developing this workshop. In addition, the authors are indebted to the agencies responsible for funding their individual research efforts, without which this work would not have been possible.

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Correspondence to Junko Yano, Kelly J. Gaffney, John Gregoire, Linda Hung, Abbas Ourmazd, Joshua Schrier, James A. Sethian or Francesca M. Toma.

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Yano, J., Gaffney, K.J., Gregoire, J. et al. The case for data science in experimental chemistry: examples and recommendations. Nat Rev Chem 6, 357–370 (2022). https://doi.org/10.1038/s41570-022-00382-w

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