Accelerated Emergence of Self-Driving Laboratories for Accelerating Materials Discovery
The rise of self-driving laboratories (SDLs) marks a transformative era in materials science. By leveraging automation and machine learning, SDLs provide unprecedented opportunities to rapidly explore complex chemical landscapes and accelerate discoveries. In this Outlook, we explore the growth of the global SDL ecosystem in materials science, describe recent advances, and discuss how SDLs are poised to drive more discoveries and accelerate commercialization.
Authors
- Abhishek Soni (ORCID: https://orcid.org/0000-0003-3662-511X)
- Amanda K. Brown
- Curtis P. Berlinguette
- Daniel Lin
Institutions
- Canadian Institute for Advanced Research (CA)
- University of British Columbia (CA)
Publication Details
- Journal
- ACS Central Science
- Published
- 2026-02-18
- DOI
- https://doi.org/10.1021/acscentsci.5c01624
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00