Discovering and optimizing commercially viable materials for clean energy applications typically takes over a decade. Self-driving laboratories that iteratively design, execute, and learn from material science experiments in a fully autonomous loop present an opportunity to accelerate this research. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.
@article{arxiv.1906.05398,
title = {Self-driving laboratory for accelerated discovery of thin-film materials},
author = {Benjamin P. MacLeod and Fraser G. L. Parlane and Thomas D. Morrissey and Florian Häse and Loïc M. Roch and Kevan E. Dettelbach and Raphaell Moreira and Lars P. E. Yunker and Michael B. Rooney and Joseph R. Deeth and Veronica Lai and Gordon J. Ng and Henry Situ and Ray H. Zhang and Michael S. Elliott and Ted H. Haley and David J. Dvorak and Alán Aspuru-Guzik and Jason E. Hein and Curtis P. Berlinguette},
journal= {arXiv preprint arXiv:1906.05398},
year = {2020}
}