18 April 2024
Together with its partner institutions within the Helmholtz Association, Forschungszentrum Jülich aims to build a new generation of AI foundation models for science. The associated pilot projects are pioneering the development of foundation models to take the application of AI in science to a new level. These projects are part of the newly established Helmholtz Foundation Model Initiative (HFMI), which receives funding of around € 23 million from the Helmholtz Association.
Two of the four selected pilot projects are coordinated by Forschungszentrum Jülich. The researchers aim, for example, to develop one of the first AI foundation models for climate research, which will form the basis for one of the most precise weather and climate models in the world. The use of AI should also help to pool the huge amount of new data and findings in materials research and thus accelerate the implementation of innovative solar cell concepts. In another pilot project involving Forschungszentrum Jülich, scientists aim to use a new AI foundation model to gain a better understanding of the global carbon cycle.
Foundation models are AI applications that, built upon a very broad knowledge base, are capable of solving a range of complex problems. The well-known chatbot ChatGPT from OpenAI is also based on such a model. Foundation models are significantly more powerful and flexible than traditional AI models, making them suitable for scientific applications. Through targeted training with extensive datasets and the use of generative AI, they can understand complex relationships based on learned patterns, generate new connections, and make predictions.
What if we could make predictions about future climate even more accurately, quickly, and efficiently? Could we better combat the causes of climate change and mitigate its consequences as a result? Could we make the impacts of global warming impressively visible to everyone? The HClimRep project aims to answer exactly these questions. By building one of the first AI foundation models for climate research, which combines data from the atmosphere, ocean, and sea ice, researchers are developing one of the most precise weather and climate models in the world. This deep-learning model, with billions of parameters, will be capable of conducting complex "what-if" experiments and other modeling tasks related to the ocean and atmosphere, thanks to extensive training on Europe's first exascale computer.
Participating Helmholtz Centers: Forschungszentrum Jülich, Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Karlsruher Institute for Technology, and Helmholtz-Zentrum Hereon
Head of research group Earth System Data Exploration and co-lead of division Large Scale Data Science, University professor in Computational Earth System Science at the University of Cologne.
Photovoltaics is a key technology for the energy transition. In order to achieve the necessary increase in global use of low-cost solar power, innovative solar cell concepts must be implemented more quickly. Activities in research and development in this area are rapidly increasing, leading to a wealth of scientific publications. However, the sheer volume of data is creating limitations in implementing the latest findings. SOL-AI aims to create a foundation model that will fundamentally reform materials informatics in this field. It is capable of integrating the diversity of experimental data and results in the research of photovoltaic materials, advancing innovations in various areas: from accelerated component development and optimization to the discovery of new solar materials. SOL-AI is expected to develop solutions that will have practical relevance for both research and industry.
Participating Helmholtz Centers: Forschungszentrum Jülich, Karlsruher Institute for Technology, Helmholtz-Zentrum Berlin für Materialien und Energie, and Helmholtz-Zentrum Hereon
Director of the Institute for Advanced Simulation Materials Data Science and Informatics (IAS-9)
To mitigate the effects of global climate change, we need comprehensive knowledge about the global carbon budget, which comprises CO2 sources and sinks such as wetlands, forests, or permafrost soils. Until now, researchers have struggled to quantify how changes in land areas, vegetation, or soils affect the carbon cycle due to heterogeneous and scattered data. The foundation model 3D-ABC will target the integration and modeling of data from various sources such as satellites, drones, or local CO2 monitoring stations. This allows key parameters of the global carbon cycle of vegetation and soils to be captured, quantified, and characterized with high spatial resolution.
Participating Helmholtz Centers: Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Forschungszentrum Jülich, Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Center Potsdam – GFZ German Research Center for Geosciences, Helmholtz Center for Environmental Research, and German Aerospace Center
While individual projects focus on their specific issues, a Synergy Unit concentrates on overarching questions relevant to all participating projects. For example, it addresses concerns such as model scalability or training with datasets. However, it's not just about exchanging solutions; it's primarily about advancing research on foundation models across disciplines as rapidly as possible. Thus, the Synergy Unit ensures a long-term impact of the Helmholtz Foundation Model Initiative for the benefit of the general public.
Participating Helmholtz Centers: Deutsches Krebsforschungszentrum, Helmholtz Munich, Forschungszentrum Jülich, and Max Delbrück Center
The goal of the three-year Helmholtz Foundation Model Initiative (HFMI) is to develop fully functional models. Four pilot projects have been selected for this purpose, involving scientists from twelve Helmholtz Centers. Over a period of three years, the projects will receive funding totaling 11 million euros. An additional 12 million euros will be invested in expanding necessary infrastructure. A Synergy Unit will also research interdisciplinary questions, promote knowledge exchange between projects, and undertake overarching activities. The funded projects aim to not only provide clear value for science but also make their final results available to society as open source—this includes the code, training data, and trained models.
