Self-driving laboratories for sustainable materials discovery: Autonomous experimentation and AI-driven optimization

The increasing need for better materials due to enhanced sustainability and environmental friendliness of various products has compelled scientists to rethink their model of conducting research in science and engineering. Materials Acceleration Plants (MAPs) are self-driving laboratories and paradigm changing design research frameworks merging high-throughput automation, something that robotizes virtually all of the scientific method. These systems go beyond High-Throughput Screening technology itself, as they incorporate algorithms such as Bayesian optimization and genetic algorithms for efficient screening of large, multidimensional parameter spaces. Consequently, we and the viewpoints provided here highlight key drivers, applications and challenges ahead for self-sufficient platforms in the less consolidated application fields such as polymer synthesis (and their role in controlling nanomaterial properties), search for nanomaterials, solid-state chemistry or even nanomedicine etc., as initiatives that promise to define their future impact on scientific output, data processing requirements and human-machine resiliency over policy making. Beyond accelerating materials discovery, SDLs are increasingly emerging as enablers of sustainable chemical research by reducing reagent consumption, minimizing experimental waste, improving resource utilization through intelligent optimization, and enabling data-driven multi-objective decision-making. Future SDLs are expected to integrate sustainability metrics, including waste generation, energy consumption, atom economy, and lifecycle considerations, directly into autonomous optimization workflows. The principles, technological components and potential applications of self-driving laboratories are discussed in this review document along with the opportunities and challenges that they provide for integrated chemical and materials research.