The photovoltaics market has advanced tremendously over the nearly seven decades since the first practical silicon solar cell was demonstrated. While silicon has dominated the market for years, organic photovoltaics are gaining ground as a promising alternative with many benefits in performance, traits and processing.

With the support of the Marie Skłodowska-Curie Actions programme, the IDEAL project is tackling the challenge of moving this promising technology from the laboratory to the field. Using machine learning on data collected from high throughput processing techniques and numerous materials, the team expects to define the best compromise between efficiency and stability through prediction of the most important molecular descriptors for candidate materials.

The overall objective of IDEAL is the development of highly efficient and stable OPV modules for diffuse light applications such as building integration and powering of internet of things (IoT) sensors.

Organic photovoltaics (OPV) are a promising emerging renewable energy technology due to several attractive traits, including the possibility to broadly tune colour and transparency, light weight, insensitivity to the angle of illumination, and very high efficiency under low and indoor illumination. Moreover, their amenability for solution processing at low thermal budgets enables the roll-to-roll (R2R) fabrication of OPV modules, ensuring cost-efficient production in terms of energy and economics. Nowadays, the most important challenge is to transfer the large potential of OPV from lab scale to industry and improving its stability.

In this project we will use high throughout fabrication methodology using combinatorial screening to evaluate the performance of a material system much faster than the conventional methods. The big data produced will be analysed by machine learning algorithms to predict the full photocurrent versus composition curves from vary basic molecular descriptors and to determine which are the most important parameters that define the best compromise between efficiency and stability.

The expertise of the researcher on fabrication organic electronic devices by solution processed techniques will be combined with the host group experience in combinatorial screening methodology and the use of machine learning in OPV. This project will be instrumental for the researcher to become a cutting-edge scientist and create his own group.

Coordinator

• AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS

Start date: 1 September 2022 - End date: 31 August 2024

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101025608.