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

Abstract

Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. 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 the power of 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.

Progress and prospects for accelerating materials science with automated and autonomous workflows

“…4, including emerging reports from perovskite synthesis 78 and molecular materials for of organic photovoltaics 79 and organic hole transport materials. 80 Continuation of these concerted efforts to increase automation and develop tailored AI algorithms will enable the materials science community to realize a paradigm shi in scientic discovery where expert scientists can dedicate a substantially larger fraction of their time to performing the critical tasks of identifying important problems and communicating critical insights.…”

Chemical Robotics Enabled Exploration of Stability and Photoluminescent Behavior in Multicomponent Hybrid Perovskites via Machine Learning

“…The automated combinatorial synthesis provides the opportunity of accelerating the production of materials with large compositional space. To date, several groups have demonstrated the applications of the automated experiment to materials discovery, including synthesis 38 and full device-preparation workflows 39 . Here, we show that a combination of laboratory automation and machine learning can be used for the rapid mapping of both the concentration-dependent physical properties and long-term stability in broad concentration spaces of hybrid organic-inorganic perovskites, demonstrating the presence of the regions with high stability.…”

Quantifying defects in thin films using machine vision

“…To illustrate the applicability of our method to thin film optimization, we used DeepThin to resolve a 2-dimensional filmmorphology response surface in a set of experiments where both film composition and processing were varied. Following our previous work 30 , thin films of spiro-OMeTAD doped with varying amounts of FK102 Co(III) TFSI salt and annealed for varying durations were prepared and then imaged using a robotic platform (see "Methods" section). These experiments provided an array of images exhibiting morphological trends as a function of both film composition and processing.…”

“…These films were deposited by spincoating, annealed, and imaged by a flexible robotic platform equipped with a darkfield photography system (see "Methods" section, ref. 30 ). The images in this darkfield dataset were labeled with respect to the extent of dewetting and of cracking by materials scientists with expertise in thin-film materials research (see "Methods" section).…”