Battery Interface Genome - Materials Acceleration Platform

  • von Lilienfeld-Toal, Otto Anatole (Projektleiter*in)

Projekt: Forschungsförderung

Projektdetails

Abstract

Today, energy production and transport are evolving fast to meet challenging environmental targets and growing demand. The Achilles’ heel is energy storage, which is incapable of providing both low cost and high-performance solutions. The answer is not a simple evolution of existing batteries but disruptive technologies that must be discovered fast.
The BIG-MAP vision is to develop a modular, closed-loop infrastructure and methodology to bridge physical insights and data-driven approaches to accelerate the discovery of sustainable battery chemistries and technologies. BIG-MAP’s strategy is to cohesively integrate machine learning, computer simulations and AI-orchestrated experiments and synthesis to accelerate battery materials discovery and optimization. The project will be a lever to create the infrastructural backbone of a versatile and chemistry-neutral European Materials Acceleration Platform, capable of reaching a 10-fold increase in the rate of discovery of novel battery materials and interfaces.
To succeed in this unprecedented international initiative, the BIG-MAP consortium covers the entire battery discovery value chain from atoms to battery cells, totaling 34 partners from 15 countries and spanning world-leading academic experts, research laboratories and industry leaders. The consortium is a joint European battery community effort, and the large-scale European Research Initiative BATTERY 2030+ stands united behind the BIG-MAP consortium. In addition to 13 core partners from BATTERY 2030+, the BIG-MAP consortium includes 21 leading European partners with complementary battery skills and essential competences from critical research areas such as quantum machine learning, deep learning and autonomous synthesis robotics. All partners will work to create an innovative methodology relying on unique competences and cross-cutting initiatives to deliver a shared infrastructure and 12 key demonstrators to showcase the value of AI-orchestrated materials discovery.
AkronymBIG-MAP
StatusAbgeschlossen
Tatsächlicher Beginn/ -es Ende1/09/2031/08/23

Projektbeteiligte

  • Universität Wien (Leitung)
  • Technical University of Denmark (DTU)
  • Uppsala University
  • Karlsruher Institut für Technologie
  • Centre National De La Recherche Scientifique (CNRS)
  • Universität Münster
  • French Alternative Energies and Atomic Energy Commission (CEA)
  • National Institute of Chemistry
  • SINTEF The Foundation for Scientific and Industrial Research at the Norwegian Institute of Technology (NTH)
  • Politecnico di Torino
  • Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V.
  • Chalmers University of Technology
  • Warsaw University of Technology
  • Spanish National Research Council (CSIC)
  • Delft University of Technology
  • Consiglio Nazionale delle Ricerche
  • École polytechnique fédérale de Lausanne
  • University of Cambridge
  • University of Oxford
  • University of Tartu
  • University of Liverpool
  • European Synchrotron Radiation Facility ESRF
  • Institut Laue-Langevin (ILL)
  • SOLEIL Synchrotron
  • FUNDACION CIDETEC
  • ENERGY MATERIALS INDUSTRIAL RESEARCH INITIATIVE AISBL
  • UMICORE
  • Solvay SA
  • BASF SE
  • Northvolt AB
  • Universität Basel
  • IT University of Copenhagen
  • DASSAULT SYSTEMES DEUTSCHLAND GMBH
  • Forschungszentrum Jülich
  • Saft
  • How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

    Bosoni, E., Beal, L., Bercx, M., Blaha, P., Blügel, S., Bröder, J., Callsen, M., Cottenier, S., Degomme, A., Dikan, V., Eimre, K., Flage-Larsen, E., Fornari, M., Garcia, A., Genovese, L., Giantomassi, M., Huber, S. P., Janssen, H., Kastlunger, G., Krack, M., &25 mehrKresse, G., Kühne, T. D., Lejaeghere, K., Madsen, G. K. H., Marsman, M., Marzari, N., Michalicek, G., Mirhosseini, H., Müller, T. M. A., Petretto, G., Pickard, C. J., Poncé, S., Rignanese, G. M., Rubel, O., Ruh, T., Sluydts, M., Vanpoucke, D. E. P., Vijay, S., Wolloch, M., Wortmann, D., Yakutovich, A. V., Yu, J., Zadoks, A., Zhu, B. & Pizzi, G., Jan. 2024, in: Nature Reviews Physics. 6, S. 45–58 14 S.

    Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

  • Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design

    Karandashev, K., Weinreich, J., Heinen, S., Arismendi Arrieta, D. J., von Rudorff, G. F., Hermansson, K. & von Lilienfeld, O. A., 12 Dez. 2023, in: Journal of Chemical Theory and Computation. 19, 23, S. 8861-8870 10 S.

    Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

    Open Access
  • Alchemical geometry relaxation

    Domenichini, G. & von Lilienfeld, O. A., Mai 2022, in: Journal of Chemical Physics. 156, 18, 13 S., 184801.

    Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

    Open Access