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

Nature Reviews Chemistry volume 6, pages 357–370 (2022)Cite this article

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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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Authors and Affiliations

  1. Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Junko Yano

  2. SLAC National Accelerator Laboratory, Menlo Park, CA, USA

    Kelly J. Gaffney

  3. PULSE Institute, SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA

    Kelly J. Gaffney

  4. Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

    John Gregoire

  5. Accelerated Materials Design and Discovery, Toyota Research Institute, Los Altos, CA, USA

    Linda Hung

  6. University of Wisconsin, Milwaukee, WI, USA

    Abbas Ourmazd

  7. Fordham University, Department of Chemistry, The Bronx, NY, USA

    Joshua Schrier

  8. Department of Mathematics, University of California, Berkeley, CA, USA

    James A. Sethian

  9. Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    James A. Sethian

  10. Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    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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