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.