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Related Experiment Video

Updated: Oct 30, 2025

In situ Grazing Incidence Small Angle X-ray Scattering on Roll-To-Roll Coating of Organic Solar Cells with Laboratory X-ray Instrumentation

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Published on: March 2, 2021

Accelerating organic solar cell material's discovery: high-throughput screening and big data.

Xabier Rodríguez-Martínez¹, Enrique Pascual-San-José¹, Mariano Campoy-Quiles¹

¹Institut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus UAB 08193 Bellaterra Spain m.campoy@csic.es.

Energy & Environmental Science

|July 2, 2021

Summary

High-throughput screening and computational methods accelerate the discovery of new materials for organic solar cells. Machine learning aids in analyzing vast datasets to identify optimal molecular designs for improved photovoltaic performance.

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Last Updated: Oct 30, 2025

In situ Grazing Incidence Small Angle X-ray Scattering on Roll-To-Roll Coating of Organic Solar Cells with Laboratory X-ray Instrumentation

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Published on: March 2, 2021

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A Guided Materials Screening Approach for Developing Quantitative Sol-gel Derived Protein Microarrays

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Published on: August 26, 2013

Area of Science:

Materials Science
Renewable Energy
Organic Electronics

Background:

Organic solar cells (OSCs) have seen significant efficiency gains due to novel materials like non-fullerene acceptors and low band gap donor polymers.
The expanding library of OSC materials makes traditional experimental evaluation time-consuming and resource-intensive, hindering technological advancement.

Purpose of the Study:

To review computational and experimental high-throughput methodologies for accelerating the discovery of new organic photovoltaic materials.
To highlight the role of machine learning in analyzing large datasets for material design in OSCs.

Main Methods:

Computational (pre)screening approaches for selecting or generating promising molecular candidates.
High-throughput experimental screening, including lateral parametric gradients and automated device prototyping.
Machine learning algorithms for quantitative structure-activity relationship retrieval and molecular design rationale extraction.

Main Results:

Computational methods enable efficient selection or generation of potential organic photovoltaic materials.
High-throughput experimental techniques generate large datasets rapidly, enhancing "big data" readiness.
Machine learning effectively extracts design principles from experimental data, facilitating faster material discovery.

Conclusions:

High-throughput computational and experimental strategies are crucial for overcoming the limitations of traditional methods in organic photovoltaic material discovery.
Machine learning plays a vital role in interpreting the generated big data, driving innovation and accelerating the pace of discovery in organic solar cells.