Accelerated Development of Energy Materials through Automation and Artificial Intelligence

Batteries, fuel cells, and electrolyzers mostly rely on advances in solid-state inorganic materials, yet discovering and optimizing these materials remains a complex and time-intensive challenge. Self-driving laboratories (SDLs), through advances in computation, automation and artificial intelligence (AI), are revolutionizing the pace of solid-state material discovery. SDLs can rapidly navigate vast chemical spaces, optimizing multi-dimensional synthesis and processing conditions and accelerating the identification of high-performance materials relevant for commercialization. The approach not only enhances efficiency and experimental precision but also mitigates the trial-and-error limitations of traditional methods. In this perspective, we examine how computation, high throughput automation, and advanced AI techniques can accelerate the discovery of energy materials, highlighting key capabilities, current applications, and providing a perspective on future directions in the field. We will also discuss how despite challenges such as high implementation costs and the complexity of automating solid-state synthesis, the continued success of SDLs relies on more democratized, accessible, and collaborative frameworks.