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

Junko Yano¹, Kelly J Gaffney^{2

3}, John Gregoire⁴, Linda Hung⁵, Abbas Ourmazd⁶, Joshua Schrier⁷, James A Sethian^{8

9}, Francesca M Toma¹⁰

Affiliations

¹ Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. JYano@lbl.gov.
² SLAC National Accelerator Laboratory, Menlo Park, CA, USA. kgaffney@slac.stanford.edu.
³ PULSE Institute, SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA. kgaffney@slac.stanford.edu.
⁴ Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA. gregoire@caltech.edu.
⁵ Accelerated Materials Design and Discovery, Toyota Research Institute, Los Altos, CA, USA. linda.hung@tri.global.
⁶ University of Wisconsin, Milwaukee, WI, USA. ourmazd@uwm.edu.
⁷ Fordham University, Department of Chemistry, The Bronx, NY, USA. jschrier@fordham.edu.
⁸ Department of Mathematics, University of California, Berkeley, CA, USA. sethian@berkeley.edu.
⁹ Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory, Berkeley, CA, USA. sethian@berkeley.edu.
¹⁰ Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. fmtoma@lbl.gov.

PMID: 37117931
DOI: 10.1038/s41570-022-00382-w

Review

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

Junko Yano et al. Nat Rev Chem. 2022 May.

. 2022 May;6(5):357-370.

doi: 10.1038/s41570-022-00382-w. Epub 2022 Apr 21.

Authors

Junko Yano¹, Kelly J Gaffney^{2

3}, John Gregoire⁴, Linda Hung⁵, Abbas Ourmazd⁶, Joshua Schrier⁷, James A Sethian^{8

9}, Francesca M Toma¹⁰

Affiliations

¹ Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. JYano@lbl.gov.
² SLAC National Accelerator Laboratory, Menlo Park, CA, USA. kgaffney@slac.stanford.edu.
³ PULSE Institute, SLAC National Accelerator Laboratory, Stanford University, Stanford, CA, USA. kgaffney@slac.stanford.edu.
⁴ Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA. gregoire@caltech.edu.
⁵ Accelerated Materials Design and Discovery, Toyota Research Institute, Los Altos, CA, USA. linda.hung@tri.global.
⁶ University of Wisconsin, Milwaukee, WI, USA. ourmazd@uwm.edu.
⁷ Fordham University, Department of Chemistry, The Bronx, NY, USA. jschrier@fordham.edu.
⁸ Department of Mathematics, University of California, Berkeley, CA, USA. sethian@berkeley.edu.
⁹ Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory, Berkeley, CA, USA. sethian@berkeley.edu.
¹⁰ Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. fmtoma@lbl.gov.

PMID: 37117931
DOI: 10.1038/s41570-022-00382-w

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.

PubMed Disclaimer

References

1. Ourmazd, A. Science in the age of machine learning. Nat. Rev. Phys. 2, 342–343 (2020). - DOI
1. National Science Foundation. Framing the Role of Big Data and Modern Data Science in Chemistry. NSF https://www.nsf.gov/mps/che/workshops/data_chemistry_workshop_report_032... (2018).
1. Mission Innovation (Energy Materials Innovation, 2018); http://mission-innovation.net/wp-content/uploads/2018/01/Mission-Innovat... .
1. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018). - PubMed - DOI
1. Morgan, D. & Jacobs, R. Opportunities and challenges for machine learning in materials science. Annu. Rev. Mater. Res. 50, 71–103 (2020). - DOI

Publication types

Actions

LinkOut - more resources

Full Text Sources
- Nature Publishing Group

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

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

Affiliations

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

Authors

Affiliations

Abstract

References

Publication types

LinkOut - more resources

Full Text Sources