Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems

TL;DR

This paper presents a high-throughput, autonomous platform using Bayesian optimization for efficiently designing and optimizing multi-component organic photovoltaic blends, significantly reducing material usage and accelerating discovery.

Contribution

It introduces a fully automated experimental platform with Bayesian optimization for rapid multi-component OPV blend optimization, enabling high-throughput screening and autonomous decision-making.

Findings

Automated film formation of up to 6048 films per day.

Efficient mapping of four-dimensional quaternary OPV blends.

Identification of stable compositions with less than 1 mg material.

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 towards multi-component active layer blends. We report the development of high-throughput and autonomous experimentation methods for the effective optimization of multi-component 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 four-dimensional parameter space of quaternary OPV blends is mapped and optimized for photo-stability. While with conventional approaches roughly 100 mg of material would be necessary, the robot based platform can screen 2,000 combinations with less than 10 mg and machine learning enabled autonomous experimentation identifies the stable compositions with less than 1 mg.