The development of novel materials with tailored properties is a complex, multiobjective optimization problem that has long been a challenge in materials research. The integration of artificial intelligence (AI) and machine learning (ML) techniques has shown great promise in accelerating materials discovery, design, and development by uncovering hidden correlations between processing, structure, and properties. Autonomous experimentation platforms, also known as self-driving laboratories (SDLs), have emerged as a powerful tool in this endeavor, enabling the rapid and efficient acquisition of critical data through a closed-loop feedback process. In this review, we explore the applications of AI/ML techniques to materials research and development through the lens of SDLs and examine the challenges and opportunities associated with the development and deployment of SDLs. We provide a detailed analysis of the components of an SDL, including AI-driven decision-making, experimental data generation, and knowledge representation, and discuss the current barriers to industrial adoption.
Warren et al. (2026) studied this question.