Accelerated Emergence of Self-Driving Laboratories for Accelerating Materials Discovery

ACS Central Science 2026 Open access

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Authors: Amanda K. Brown, Abhishek Soni, Daniel Lin, Curtis P. Berlinguette
DOI: 10.1021/acscentsci.5c01624
Cited by: 1
References: 45

Abstract

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.

Fields of Study & Subjects

Machine Learning in Materials Science Micro and Nano Robotics Modular Robots and Swarm Intelligence

Publication details

Journal / source
ACS Central Science
Publisher
American Chemical Society (ACS)
Volume
12
Issue
3
Pages
300–306
Publication date
18 February 2026
Language
EN