The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The future of self-driving laboratories: From Human in the Loop Interactive AI to Gamification
Open accessPreprint — published as 10.1039/d4dd00040d · Paper page
Abstract
Recent developments in artificial intelligence (AI) and machine learning (ML), implemented through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities for the accelerated discovery and optimization of materials. This paper provides a joint analysis of SDLs from both academic and industry perspectives, highlighting the importance of integrating human intelligence in these systems. It discusses the necessity of careful planning in SDL design across physical, data, and workflow dimensions, including instrumental setup, experimental workflow, data management, and human-SDL interaction. The significance of integrating human input within SDLs, especially as the focus shifts from individual tools and tasks to the creation and management of complex workflows, is emphasized. The paper stresses the crucial role of reward function design in developing forward-looking workflows and examines the interplay between hardware evolution, ML application across chemical processes, and the influence of reward systems in research. Ultimately, the article advocates for a future where SDLs blend human intuition in hypothesis formulation with AI's precision, speed, and data-handling capabilities.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Metadata
- Authors
- Hysmith, H., Foadian, E., Padhy, S. P., Kalinin, S. V., Moore, R. G., Ovchinnikova, O. S., & Ahmadi, M.
- Affiliations
- University of Tennessee at Knoxville (US)
- Pacific Northwest National Laboratory (US)
- Oak Ridge National Laboratory (US)
- Thermo Fisher Scientific (United States) (US)
- Corresponding author
- Ahmadi, Mahshid
- ORCID
- Venue
- Digital Discovery
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-01-12
- Volume
- 3
- Issue
- 4
- Pages
- 621-636
- ISSN
- 2635-098X (ISSN-L)
- OpenAlex
- W4392805407
- Cited by
- 103
- References
- 1
- Citations by year
- 2026: 51 · 2025: 37 · 2024: 15
- FWCI
- 15.10
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Digital Discovery (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Alfred P. Sloan Foundation (FG-2022-18275)
- U.S. Department of Energy (DE-AC05-00OR22725, DE-SC0021118)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- self-driving laboratories, automated instrumentation, interactive AI, gamification, scientific discovery, artificial intelligence in research, human-AI collaboration
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