Self-driving laboratories present the opportunity to accelerate the discovery and optimization of materials. A major challenge associated with this optimization process is that useful materials must satisfy multiple objectives, where the optimization
of one objective is often at the expense of another. The Pareto front reports the optimized trade-offs between competing objectives. Here we report a self-driving laboratory, Ada, that defines the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis using various oxidants and fuels. Ada successfully identified previously untested synthesis conditions that resulted in the discovery of lower processing temperatures (190 {deg}C) relative to the previous state of the art (250 {deg}C), a temperature difference that makes the coating of different commodity plastic materials possible (e.g., Nafion, polyethersulfone, polyethylene naphthalate). These conditions enabled us to use combustion synthesis to spray coat uniform palladium films with conductivities approaching those of sputtered films. This work shows how self-driving laboratories can discover materials satisfying multiple objectives.
The sensitivity of thin-film materials and devices to defects motivates extensive research into the optimization of film morphology. This research could be accelerated by automated experiments that characterize the response of film morphology to synt
hesis conditions. Optical imaging can resolve morphological defects in thin films and is readily integrated into automated experiments but the large volumes of images produced by such systems require automated analysis. Existing approaches to automatically analyzing film morphologies in optical images require application-specific customization by software experts and are not robust to changes in image content or imaging conditions. Here we present a versatile convolutional neural network (CNN) for thin-film image analysis which can identify and quantify the extent of a variety of defects and is applicable to multiple materials and imaging conditions. This CNN is readily adapted to new thin-film image analysis tasks and will facilitate the use of imaging in automated thin-film research systems.
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 p
resent 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.
