MALTOSE · Machine Learning for Tailoring Organic Semiconductors
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-03-01 → 2024-07-30
- EU contribution
- €172,932
- Participants
- 1
- Scheme
- MSCA-IF
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Results in brief
Machine Learning for Tailoring Organic Semiconductors
MALTOSE, the acronym of the project stands for "MAchine Learning for Tailoring Organic SEmiconductors". Let's go through this from back to front. Organic semiconductors are carbon-based materials, either molecules or polymers that have extended pi electron systems and have a certain electrical conductivity. They can absorb and emit light in the visible spectrum and can be used for organic light-emitting diodes, organic photovoltaic cells and even for organic circuits. There is much potential in organic semiconductors because they are potentially low cost, mechanically flexible, and the chemical compound space if conceivable molecules is huge. In this project, we focus on organic semiconductors for photovoltaic cells, where the main figure of merit is the power-conversion efficiency. An organic photovoltaic consists among other details of a donor material and an acceptor material. For good power-conversion efficiency, these need to have the right optical and electronic properties, matching each other and the spectrum of sun light. Finding materials with just the right properties is what the term "tailoring" from the title refers to. The challenge is, however, that the search space is vast because already the chemical compound space for one material is huge, but here two materials have to be found together. It is simply not feasible to determine the properties of all possible materials by full-fledged simulations (e.g. density-functional theory computations). Rather, a machine learning model shall be used to guide the search, such that only the promising candidate materials have to be investigated in detail. The broader context is the research field of Materials Discovery, and the concrete application is the search for new materials for better organic semiconductors for applications such as organic electronics, organic light-emitting diodes (OLEDs), or organic photovoltaic cells. Thus, the main objective is to develop a machine-learning method for the search of materials with desired properties. This method has to be able to handle large organic compounds, but will also transfer to other kinds of materials.
Data: CORDIS, © European Union
Project objective
“Machine Learning for Tailoring Organic Semiconductors” (MALTOSE) connects fundamental materials research with machine-learning (ML) techniques, focusing on the electronic properties of organic semiconductors. The aim of this innovative project is to discover and design novel materials with exciting properties, the prime example being the design of compounds for better organic photovoltaic cells, i.e., that reach higher power-conversion efficiencies and are more stable and more environmentally friendly.The methodology relies on a deep tensor neural network, the so-called PredictNet, that is designed and trained to predict electronic properties of molecules and polymers, at a fraction of the numerical cost compared to density-functional theory (DFT) computations, not to mention experimental measurements. PredictNet will be particularly fruitful in combination with a genetic algorithm that will be developed to propose candidate compounds from crossover and mutation from previously successful compounds. MALTOSE will enable the identification and design of promising compounds, out of the immense pool of imaginable molecules and materials, for future technological applications in fields like organic photovoltaic solar cells, large-area electronic displays, flexible organic electronics, or sensors.The project will bring together the fellow, a recognized quantum physicist and data scientist with academic and industry research experience, and a top research host institution under the supervision of a leading expert in materials science, genetic algorithms, modelling, simulation and knowledge transfer. The fellow will receive an advanced training programme in research skills and complementary non-research-oriented skills in order to enhance his future career prospects and to provide a strong basis for an independent career.
Original text from CORDIS.
Participants
- FUNDACION ICAMCYL · CUBILLOS DEL SIL LEONCoordinatorSpain
Links
Data: CORDIS, © European Union