Skip to main content

Finding a needle in a haystack: efficient identification of high performing organic energy materials

Periodic Reporting for period 4 - FOREMAT (Finding a needle in a haystack: efficient identification of high performing organic energy materials)

Reporting period: 2020-04-01 to 2022-01-31

Organic materials have a huge potential as the main ingredient in cost-effective and clean energy technologies based on abundant and non-toxic materials. In particular, these compounds are being thoroughly investigated as the active layer in devices that convert light or waste heat into electricity, i.e. photovoltaic and thermoelectric technologies, respectively. Interestingly, no fundamental limitation has yet been discovered that says that organics cannot deliver devices with high efficiencies. Indeed, the library of potential candidates is literally infinite, as there is an infinite way in which carbon atoms can form conjugated systems through alternating single and double bonds. The issue is how to find such the best possible material for a given application. While theory points towards some directions, the final performance of the synthetized compounds strongly depends on specific properties of the materials made which, thus far, cannot be predicted. One of the major bottlenecks is the time needed to evaluate each compound. For instance, at lab scale, the fabrication of an organic solar cell takes from days to a few hours (if processed in parallel), while measuring its efficiency takes just minutes.

In order to identify very efficient organic energy systems in a time effective manner, the project FOREMAT proposed the use of high throughput screening methodologies based on samples with controlled gradients in the parameters of interest acting as processing libraries, combined with innovative imaging methods that enable the correlation between device performance and the information about the local parameters of the device.

The FOREMAT team has successfully introduced a series of methods to produce samples with 1D and 2D gradients in film thickness, composition, nanostructure, molecular orientation and doping level. We have also developed imaging methods to co-locally determine the device properties (e.g. photocurrent in solar cells, or thermal conductivity in thermoelectrics) and material´s properties. The resulting platform provides hundreds to thousands data points per system, making the evaluation of a novel system 50 times faster and saving up to 90% of the raw materials in the process. We have applied this technology to investigate tens of photovoltaic and thermoelectric systems, making significant advances both in terms of fundamental understanding and at applied level.

This technology addresses an important challenge for society, namely, the decarbonisation of the energy sources. In particular, the advanced technology will be used to efficiently identify materials and processes with very low carbon footprint, and simultaneously, finely adapted to the application. For instance, heat sources have all very different specifications in terms of operational temperatures and shapes, and therefore, the opportunity of choose thermoelectric materials with targeted performance is extremely relevant to maximize heat waste recovery, and thus energy efficiency. On the other hand, the widespread implementation of photovoltaics requires very different technologies depending on the application, for instance, very efficient cells for solar farms, semi-transparent and light weight for agrovoltaics, flexible for powering portable electronics, in-door lighting optimized for IoT applications, and colour tuneable and resilient to light incidence angle for façade decoration. FOREMAT´s technology will have a huge impact to society providing the means to advance photovoltaics a la càrte.
During the first part of the project, we placed special emphasis on developing the combinatorial platform for evaluation of photovoltaic and thermoelectric materials, while the second half was devoted to make use of the technology to produce important advances in the field. The development required the combination of a new mathematical formalism, advancing a series of methods to produce samples with gradients in parameters of interest, as well as the development of the experimental platform to measure device and materials properties simultaneously.

The second half of the project has focused on using the aforementioned platform to push the boundaries of knowledge in the fields of organic photovoltaics and thermoelectrics. Two highlights for photovoltaics include the prediction of the performance of a system by combining high throughput experimental data and machine learning algorithms and the largest study of the optical properties of organic materials conducted so far, that helps to guide the design of new molecules with very strong absorption coefficients. In terms of thermoelectrics, two major contributions include the advancement of recyclable, stable and high performance composites and the discovery of a strong reduction in the thermal conductivity of doped organics through alloying effects.

The work described has been published in more than 40 scientific papers, including publications in some of the most prestigious journals, such as Nature Materials, Energy and Environmental Science and Advanced Materials. A full list can be found here: We have also spare no efforts in disseminating the work, and participated in about 40 conferences, as well as a stream of dissemination activities targeting academics, industry, and the general public. The work has resulted in three patents, one of which has prompted the founding of a spin-off company (Molecular Gate S.L.). Moreover, during the course of the project, 20 early stage researchers have been trained in different aspects of the project.
We have demonstrated a rational to predict the absorption of novel compounds and demonstrated an increase of 50% with respect to state-of-the-art in the absorption of low band gap polymers using our design rules. Our discoveries will help to bring photovoltaic efficiencies higher than ever.

We have successfully demonstrated a combinatorial evaluation of organic photovoltaic cells method that is 50 times faster and saves about 90% of the material compared to the conventional evaluation protocols.

On the other hand, the combinatorial platform is allowing us to screen large libraries of materials and combinations thereof, thus providing design rules for the development of organic materials. One example, is the use of high throughput data to feed artificial intelligence algorithms, which has enabled the prediction of the performance of binary systems as a function of composition, thickness and microstructure. Another example is the discovery of alloying effects in the doped thermoelectric materials, which opens a new avenue for significantly increasing the efficiency of carbon based thermoelectrics. As these technologies are based on abundant, non-toxic materials processed using very little amounts of energy, they are amongst the most sustainable material candidates to employ in one of the most important societal challenges: the need to swift the energy paradigm towards ever cleaner energy sources and harvesting technologies.
High throughput screening of organic thermoelectrics
High throughput screening of organic photovoltaics