Self-driving laboratory for accelerated discovery of thin-film materials

TL;DR

This paper presents a modular autonomous robotic platform that uses model-based optimization to rapidly discover and optimize thin-film materials with desired electronic and optical properties for clean energy applications.

Contribution

It introduces a novel self-driving laboratory system capable of autonomously optimizing material properties, accelerating the discovery process in materials science.

Findings

Successfully maximized hole mobility in organic materials

Demonstrated autonomous optimization of thin-film properties

Showcased potential for accelerating materials discovery

Abstract

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.