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The accelerating depletion of natural resources undoubtedly demands a radical reevaluation of research practices addressing the escalating climate crisis. From traditional approaches to modern-day advancements, the integration of automation and artificial intelligence (AI)-guided decision-making has emerged as a transformative route in shaping new research methodologies. Harnessing robotics and high-throughput automation alongside intelligent experimental design, self-driving laboratories (SDLs) offer an innovative solution to expedite chemical/materials research timelines while significantly reducing the carbon footprint of scientific endeavors, which could be utilized to not only generate green materials but also make the research process itself more sustainable. In this Perspective, we examine the potential of SDLs in driving sustainability forward through case studies in materials discovery and process optimization, thereby paving the way for a greener and more efficient future. While SDLs hold an immense promise, we discuss the challenges that persist in their development and deployment, necessitating a holistic approach to sustainability in both design and implementation.
Sadeghi et al. (Tue,) studied this question.