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Materials map for microstructures of inorganic materials

Project description

New artificial intelligence model captures microstructures in battery materials

Material discovery is vital for Europe’s global competitiveness. Yet, conventional methods rely heavily on the experience of seasoned researchers, leaving novices at a disadvantage. With the support of the Marie Skłodowska-Curie Actions programme, the SPECIALS project will create a machine learning-powered map to guide researchers in exploring inorganic materials, particularly battery materials. Unlike conventional machine learning approaches, SPECIALS will focus on material microstructures as their functionalities depend on these structures. The map will track structural changes upon charge and discharge, enabling easy comparison of materials. By using multi-fidelity simulations to handle computational challenges, SPECIALS provides a powerful tool for battery research.

Objective

Machine learning (ML) is an emerging tool to accelerate the materials discovery, a crucial part of the global competitiveness of Europe. Conventional materials discovery has been based on voyages of chemists and materials scientists in the materials space. They have navigated themselves according to their experience-based maps in their brains to find novel materials from innumerable candidate materials. Then, how can novice researchers navigate themselves? The project SPECIALS (materialS maP for microstructurEs of inorganiC materIALS) will provide a map by leveraging expertise of the host and the experienced researcher (ER): inorganic chemistry, physical chemistry, and computer science. The project approach is distinct from the conventional ML approaches: the ER will focus on microstructures of target materials, because functionalities of the inorganic materials are microstructure-dependent. The target materials are battery materials considering the experiences of the ER. The map will be developed by ML to describe their structural changes upon charge and discharge in continuous manners so that novice researchers can easily compare similarities/dissimilarities of various materials. Required datasets will be provided by the host utilizing existing simulation results and new simulation to cover even amorphous structures. Because the simulation of microstructures is computation intensive, multi-fidelity methods will be applied to leverage various types of simulations at varying computational cost and accuracies. The research outcome has potential to be grown up to scientific and societal impact on the European battery research community. The project also focuses on the two-way transfer of knowledge by transferring data science knowledge of the ER to the host, and simulation and soft skills of the host to the ER. This two-way transfer will enhance the employability of the ER and the value of the host.

Fields of science (EuroSciVoc)

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UPPSALA UNIVERSITET
Net EU contribution
€ 236 340,00
Address
VON KRAEMERS ALLE 4
751 05 Uppsala
Sweden
Region
Östra Sverige Östra Mellansverige Uppsala län
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data