The rise of self-driving labs in chemical and materials sciences

Accelerating the discovery of new molecules and materials, as well as developing green and sustainable ways to synthesize them, will help to address global challenges in energy, sustainability and healthcare. The recent growth of data science and automated experimentation techniques has resulted in the advent of self-driving labs (SDLs) via the integration of machine learning, lab automation and robotics. An SDL is a machine-learning-assisted modular experimental platform that iteratively operates a series of experiments selected by the machine learning algorithm to achieve a user-defined objective. These intelligent robotic assistants help researchers to accelerate the pace of fundamental and applied research through rapid exploration of the chemical space. In this Review, we introduce SDLs and provide a roadmap for their implementation by non-expert scientists. We present the status quo of successful SDL implementations in the field and discuss their current limitations and future opportunities to accelerate finding solutions for societal needs. Self-driving labs (SDLs) combine machine learning with automated experimental platforms, enabling rapid exploration of the chemical space and accelerating the pace of materials and molecular discovery. In this Review, the application of SDLs, their limitations and future opportunities are discussed, and a roadmap is provided for their implementation by non-expert scientists.