Yan Zeng

Developing new materials from design to synthesis and eventually scale-up span years, if not decades. In the domain of electrochemical energy storage, the technological advancement is reliant on the creation of new solid-state materials for use as electrodes or electrolytes. Identifying a material design strategy that yields promising candidates is just the first step; the greater challenge is in their efficient synthesis and validation. To streamline and accelerate the design–make–measure process, we have built an autonomous solid-state synthesis laboratory harnessing AI and automation. This laboratory autonomously generates synthesis recipes drawn from the vast expanse of historical literature, performs experiments via robotic systems and automated instruments, and utilizes machine learning for data interpretation, with active learning algorithms steering the subsequent experimental direction. I will present how this integrative setup not only improves existing synthesis methods but also explores the synthesis of oxide-based powder materials, thus accelerating the speed of material development.