Project description
Physics-informed AI for next-gen solar devices
Achieving efficient, stable and scalable solar energy technologies remains a challenge in the transition to a carbon-neutral energy system. Supported by the Marie Skłodowska-Curie Actions programme, the MatLearn project will develop a physics-informed digital twin to accelerate the design and optimisation of tandem semiconductor devices for next-generation photovoltaics. It will embed semiconductor physics into machine learning neural networks to produce physically interpretable surrogate models. These models will reduce trial-and-error and accelerate photovoltaic development. By analysing nanoscale disorder under real operating conditions, the project will improve understanding of device stability and performance. MatLearn will deliver open-source tools that support Europe’s clean energy transition.
Objective
The development of more efficient and stable semiconductor energy generation devices, such as photovoltaics (PVs), is key to achieve the EU’s 2050 decarbonisation targets. Tandem architectures, which stack multiple semiconductor junctions, are a promising technology that can enable more efficient and longer-lasting optoelectronic devices that far surpass those of single junction architectures. However, optimizing such complex multi-junction systems requires navigating a vast material and deposition parameter space that cannot be addressed through trial-and-error or conventional modeling alone. MatLearn introduces a novel physics-informed digital twin framework that combines drift-diffusion simulations with machine learning. By embedding semiconductor physics into Physics-Informed Neural Networks (PINNs), we aim to achieve physically consistent surrogate models that go far beyond black-box approaches, reducing data needs and providing interpretable insights. These models will enable processing and rapidly inferring key material and device parameters from large mutlimodal operando microscopy data, which would be impossible with existing approaches. This will allow us gain insight into nanoscale disorder in tandem devices under real-world operation to link to device performance and stability. Dr. Brenes brings unique expertise from academia and industry, spanning semiconductor physics, high-performance computing (HPC), device modeling, and automation. In this project, he will expand his skills in advanced characterization, supervision, and collaborative research, while transferring industry-driven approaches to data and modeling. By merging physics-informed modeling, machine learning, and operando measurements, MatLearn will deliver transferable, open-source tools for the rapid optimisation of tandem semiconductor devices, driving Europe’s progress toward clean energy and sustainable technology.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences physical sciences optics microscopy
- natural sciences physical sciences electromagnetism and electronics semiconductivity
- natural sciences computer and information sciences artificial intelligence machine learning
- engineering and technology environmental engineering energy and fuels renewable energy solar energy photovoltaic
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
CB2 1TN CAMBRIDGE
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.