Project description
New meta-learning framework to describe atomic interactions
Machine-learning interatomic potentials (MLIPs) are powerful tools that use AI to simulate materials with quantum-level accuracy, enabling researchers to predict their properties. Despite their potential, the complexity of building and optimising MLIPs has limited their widespread use. The ERC-funded META-LEARN project will introduce a meta-learning framework that should help simplify and further enhance MLIP development. A key innovation will be MLIP-COPILOT, an AI-driven tool that provides tailored recommendations for algorithms, datasets, and training methods. By making MLIPs more efficient and accessible, META-LEARN will unlock new possibilities for designing sustainable materials, while supporting researchers in tackling complex material challenges.
Objective
Materials engineers have dreamed for decades of optimizing materials starting from the quantum mechanical laws of nature. Mastering the inherent chemical and microstructural complexity promises access to outstanding properties of structural and functional materials. Machine-learning interatomic potentials (MLIPs) ignited hope by offering quantum-mechanical accuracy for systems with many atoms. However, the full capacity of MLIPs remains untapped due to the complexity of the MLIP construction process and the required simulations.
META-LEARN will cut the MLIP Gordian knot and raise MLIP construction to the next level. Our vision is a meta-learning framework that unleashes the full strength of MLIPs for large-scale simulations to a broad community. We will meta-learn the optimal MLIP-construction processes by acquiring and exploiting domain-expert knowledge from various branches of advanced ab initio and large-scale simulations.
META-LEARN leverages a comprehensive pool of algorithms to ensure an optimal, task-oriented accuracy-efficiency trade-off. MLIPs that account for magnetic and electronic excitations will broaden the materials spectrum. MLIP-based sampling will boost the efficiency of thermodynamic predictions. Deciphering the microstructure genome will provide the optimal training for large-scale defects. Sustainability challenges will be tackled at the limit of chemical and microstructural complexity: Multicomponent H-storage and coating materials.
META-LEARN will encode the knowledge of the MLIP construction and make it openly available via the MLIP-COPILOT, a knowledge-graph-based artificial intelligence tool. The MLIP-COPILOT will provide the optimal combinations of MLIP algorithms, hyperparameters, training datasets, and training sequences for different materials and simulation tasks. The MLIP-COPILOT will remain flexibly extensible for the community beyond the project’s scope, allowing for the addition of new types of simulations, materials, and MLIPs.
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.
- engineering and technology materials engineering coating and films
- natural sciences biological sciences genetics genomes
- social sciences law
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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)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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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-ERC - HORIZON ERC Grants
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-ADG
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70174 Stuttgart
Germany
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