There is worldwide trend in materials science research and discovery that involves the production and computer-aided analysis of large bodies of data points from which patterns can be identified and future developments guided. This review, by Xabier Rodríguez, Enrique Pascual and Mariano Campoy-Quiles, from the NANOPTO group, present the latest advances in high-throughput experiments used to accelerate materials discovery and device optimization and the advanced statistical and artificial intelligence (AI) used in this field to study the data generated and extract hidden patterns towards the quest for organic solar cells optimization. 

High-throughput screening procedures to speed-up device optimization

The discovery of high-performing materials has paved the way towards record performance figures in organic photovoltaics, with efficiencies now approaching 20 %. Accordingly, the catalogue of organic solar cell materials has grown at an unprecedented pace, thus setting the bottleneck for further technological development on the limited availability of human workforce time and resources. Under these circumstances, the organic photovoltaic field has evolved introducing high-throughput screening procedures applied at both the theoretical and experimental scales: these are thought to speed up the device optimization and provide further guidance to scientists in the search of next generation materials.

Researchers from the NANOPTO group, supervised by Mariano Campoy-Quiles, have reviewed state-of-the-art approaches to efficiently accelerate materials discovery and device optimization in the field of organic photovoltaics. In this work, the authors start by reviewing computer-aided screening procedures of organic semiconductor molecules, including data mining studies and machine-learning approaches combined with generative molecular models. Experimentally, the study reviews and compares a variety of high-throughput combinatorial screening techniques, which are applied to accelerate the optimization of device parameters once the high-performing materials have been identified in silico.

In this regard, two main experimental approaches are presented to scan efficiently the device parameter space: the use of lateral gradients in the parameters of interest and the use of robotic arms in automated laboratories. These two strategies are demonstrated to be time- and cost-efficient in the generation of large datasets (big data), which are employed to feed artificial intelligence (AI) algorithms and discover statistical patterns in the experimental data.

To conclude, self-driven laboratories, where AI guides the experimentation of robotic arms, are also introduced as one of the future key strategies in the identification of ground-breaking organic photovoltaic materials.

While the review focuses on organic photovoltaics, many of the methods and concepts introduced are of general interest for other material science applications, such as LEDs, transistors, superconductors, batteries or thermoelectrics.

We invite you read this Open Access review! 

Accelerating organic solar cell material's discovery: high-throughput screening and big data
Xabier Rodríguez-Martínez, Enrique Pascual-San-José and Mariano Campoy-Quiles
Energy Environ. Sci., 2021, Advance Article
doi.org/10.1039/D1EE00559F

Text written by Xabier Rodríguez, Enrique Pascual and Mariano Campoy-Quiles
NANOPTO group