Add to favorites:
Share:
This funding call aims to develop AI foundation models tailored to materials science, advancing European innovation and competitiveness. The initiative focuses on leveraging artificial intelligence to accelerate materials design and discovery, addressing critical challenges in fields such as energy, mobility, health, and electronics. The proposal emphasizes foundation models—versatile AI systems trained on multimodal data—which can be fine-tuned for diverse, specific tasks within materials science. These models will facilitate interdisciplinary collaboration, integrating domain expertise and advanced AI methodologies. The call supports the EU’s goals of sustainability and strategic autonomy, targeting advancements in environmentally friendly materials, alternatives to hazardous substances, and breakthroughs in technologies like quantum computing and photovoltaic systems.
Proposals should demonstrate the capability to access and utilize high-quality datasets, adhere to FAIR data principles, and address challenges in data standardization and management. Deliverables must include open-source, publicly accessible AI models, backed by robust documentation and case studies. Multidisciplinary teams are encouraged, incorporating both domain-specific and computational expertise. Collaborative efforts across Horizon Europe’s digital and materials research initiatives are recommended.
This Research and Innovation Action (RIA) will achieve a Technology Readiness Level (TRL) of 4-6, contributing to open science and reproducibility in materials research. The expected projects will democratize access to AI resources, foster data-driven discoveries, and provide practical solutions to societal and industrial challenges.
Opening: 22 May 2025
Deadline(s): 23 Sep 2025
Expected Outcome
- Accelerate materials science research and development.
- Advance adaptable AI technology for materials science.
- Address societal and industrial challenges with innovative materials.
- Bridge knowledge gaps and foster interdisciplinarity.
- Contribute to a Materials Commons.
- Democratize science with open-source models.
- Enhance reproducibility of scientific results.
Scope
- Develop AI foundation models in materials science.
- Train models on multimodal datasets for diverse applications.
- Demonstrate model utility in materials design and discovery tasks.
- Integrate domain and interdisciplinary knowledge into models.
- Address societal challenges such as hazardous materials and environmental sustainability.
- Promote open science and reproducibility.