Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems

Abstract

Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends, which represents a clear trend toward multicomponent active layer blends. The development of high‐throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent polymer blends for OPVs. A method for automated film formation enabling the fabrication of up to 6048 films per day is introduced. Equipping this automated experimentation platform with a Bayesian optimization, a self‐driving laboratory is constructed that autonomously evaluates measurements to design and execute the next experiments. To demonstrate the potential of these methods, a 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot‐based platform can screen 2000 combinations with less than 10 mg, and machine‐learning‐enabled autonomous experimentation identifies stable compositions with less than 1 mg.

Deep Neural Networks for Multicomponent Molecular Systems

“…To find optimal recipes, redefinition of the design space and screening within it often need to be repeated, which hinders the application of high-throughput screening methods to the multicomponent materials design, and therefore, the trial-and-error-based small-scale screening on combination and composition spaces of multicomponent systems is still the primary approach for the development of multicomponent materials. Meanwhile, MEIA-based models will allow screening on full combination and composition freedoms of all candidate raw materials, in combination with self-driving laboratories 58 composed of high-throughput screening equipment and machine learning-based sequential optimization methods such as Bayesian optimization, which will largely expand accessible design spaces of multicomponent systems. Furthermore, MEIA-based models will speed-up the nonhigh-throughput screening by providing recommendations for better raw materials that is expected to improve material properties from libraries of available materials based on sparse experimental datasets.…”

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

“…We are aware of several research groups that are building autonomous experiments in the "next generation" regime of Fig. 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.…”

Benchmarking the Acceleration of Materials Discovery by Sequential Learning

“…Adding axes with highly nonlinear behaviour to the search space such as processing or device-related parameters is likely to make random sample selection less effective, creating the opportunity for SL to be drastically more impactful. For example, recent reports of SL for the synthesis and casting of organic thin films 20 has shown enhancement factors in excess of 30× 33 compared to a comprehensive (conservatively chosen) grid search sampling. As the community continues to establish benchmarks for evaluating SL techniques, it is important to consider the amount and type of data that is required to establish accurate benchmarks.…”