A Self-Driving Laboratory for Accelerated Materials Discovery

C.P. Berlinguette, J.E. Hein, A. Aspuru-Guzik, B.P. MacLeod, F.G.L. Parlane
The University of British Columbia,
Canada

Keywords: materials discovery, machine learning, robotics, flexible automation

Summary:

This presentation will detail our self-driving laboratory for thin-film materials discovery and optimization. Discovering high-performance, low-cost materials is an integral component of technology innovation cycles, particularly in the clean energy sector. The linear methodology currently used to develop optimal materials can take decades, which impedes the translation of innovative technologies from conception to market. Our interdisciplinary team is utilizing advanced robotics, machine learning, and computational screening to overcome this challenge. We are closing the feedback loop in thin-film materials research by enabling our self-driving robotics platform named “Ada” to design, perform, and learn from its own experiments efficiently and in real time (Figure 1). This modular robotic platform, driven by a model-based optimization algorithm, is currently equipped to autonomously optimize the optical and electronic properties of thin-film materials by modifying composition and processing conditions. As a proof-of-principle, I will show how Ada can be leveraged to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. I will also showcase how Ada’s modular design can enable the automated and autonomous discovery of organic and inorganic materials for other clean energy technologies.