Automating the practice of science: Opportunities, challenges, and implications
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.
Authors
- Suhas Mahesh (ORCID: https://orcid.org/0000-0002-3897-7963)
- Ross D. King (ORCID: https://orcid.org/0000-0001-7208-4387)
- Sebastian Musslick (ORCID: https://orcid.org/0000-0002-8896-639X)
- Marina Dubova (ORCID: https://orcid.org/0000-0001-5264-0489)
- Suyog Chandramouli (ORCID: https://orcid.org/0000-0002-3450-1909)
- Sabina J. Sloman (ORCID: https://orcid.org/0000-0001-7479-4286)
- Laura Bartlett (ORCID: https://orcid.org/0000-0001-5202-4504)
- Jessica Hullman (ORCID: https://orcid.org/0000-0001-6826-3550)
- Franco Pestilli (ORCID: https://orcid.org/0000-0002-2469-0494)
- Fernand R. Gobet (ORCID: https://orcid.org/0000-0002-9317-6886)
- Thomas L. Griffiths (ORCID: https://orcid.org/0000-0002-5138-7255)
- William R. Holmes (ORCID: https://orcid.org/0000-0001-6683-4647)
- J. Nathan Kutz (ORCID: https://orcid.org/0000-0002-6004-2275)
- Christopher G. Lucas (ORCID: https://orcid.org/0000-0002-6655-8627)
Institutions
- Northwestern University (US)
- University of Alberta (CA)
- Osnabrück University (DE)
- University of Toronto (CA)
- Princeton University (US)
- University of Washington (US)
- University of Cambridge (GB)
- Brown University (US)
- Science North (CA)
- University of Manchester (GB)
- Indiana University Bloomington (US)
- Division of Materials Science and Engineering (AU)
- Indiana University (US)
- Chalmers University of Technology (SE)
- The University of Texas at Austin (US)
- University of Roehampton (GB)
- Hologic (Germany) (DE)
- London School of Economics and Political Science (GB)
- University of Edinburgh (GB)
- Aalto University (FI)
Publication Details
- Journal
- Proceedings of the National Academy of Sciences
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1073/pnas.2401238121
- Citations
- 22
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 11.39
Funders
- National Science Foundation
- Brown University
- UK Research and Innovation
- Knut och Alice Wallenbergs Stiftelse
- Nokia Foundation
- NOMIS Stiftung
- Finnish Center for Artificial Intelligence
- National Institutes of Health
- Engineering and Physical Sciences Research Council
- European Research Council
- Office of Advanced Cyberinfrastructure
- Division of Information and Intelligent Systems