Autonomous self-driving labs and AI: Energy Materials

There is a growing demand for novel materials to resolve global problems involving sustainability, health, and global warming. These pressing issues result in an unprecedented urge and pressure to be faster and more cost-efficient in running material R&D programs. In this presentation, I show how Kebotix accelerates innovation by shifting from a traditional disconnected and serendipitous R&D approach to a tightly integrated closed-loop design-make-test process.

The engine powering our process is the Kebotix self-driving lab, where we combine materials modeling, artificial intelligence, data management, and lab automation into an integrated platform. The software prioritizes tested ideas, orchestrates each step in the workflow, and collects data on the fly. Our digital technologies increase the operational efficiency of R&D programs by up to an order of magnitude, as we demonstrate in various success stories, ranging from accelerated materials formulation design to novel material discovery for optoelectronics applications such as smart windows, OLEDs, or Fluorescent Biomarkers.