Peptide-based Supramolecular Co-assembly Design: Multiscale Machine Learning Modeling Approach

Project Information

SupraModel

Grant agreement ID: 101077842

DOI

10.3030/101077842

EC signature date 20 February 2023

Start date 1 April 2023

End date 31 March 2028

Funded under

• European Research Council (ERC)

Total cost

€ 1 474 182,50

EU contribution

€ 1 474 182,00

Coordinated by

TECHNISCHE UNIVERSITAET MUENCHEN
Germany

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Data Management Plan (1)

Data Management Plan

Data Management Plan document.

Publications

Peer reviewed articles (5)

Learning non-local molecular interactions via equivariant local representations and charge equilibration

Author(s): Fuchs, Paul; Sanocki, Michał; Zavadlav, Julija
Published in: npj Computational Materials, Issue 11, 2025, ISSN 2057-3960
Publisher: Springer Nature
DOI: 10.48550/ARXIV.2501.19179

chemtrain-deploy: A Parallel and Scalable Framework for Machine Learning Potentials in Million-Atom MD Simulations

Author(s): Paul Fuchs; Weilong Chen; Stephan Thaler; Julija Zavadlav
Published in: Journal of Chemical Theory and Computation, Issue 21, 2025, ISSN 1549-9626
Publisher: American Chemical Society
DOI: 10.48550/ARXIV.2506.04055

JaxSGMC: Modular stochastic gradient MCMC in JAX

Author(s): Stephan Thaler, Paul Fuchs, Ana Cukarska, Julija Zavadlav
Published in: SoftwareX, Issue 26, 2025, ISSN 2352-7110
Publisher: Elsevier BV
DOI: 10.1016/J.SOFTX.2024.101722

chemtrain: Learning deep potential models via automatic differentiation and statistical physics

Author(s): Paul Fuchs, Stephan Thaler, Sebastien Röcken, Julija Zavadlav
Published in: Computer Physics Communications, Issue 310, 2025, ISSN 0010-4655
Publisher: Elsevier BV
DOI: 10.1016/J.CPC.2025.109512

Predicting solvation free energies with an implicit solvent machine learning potential

Author(s): Sebastien Röcken, Anton F. Burnet, Julija Zavadlav
Published in: The Journal of Chemical Physics, Issue 161, 2024, ISSN 0021-9606
Publisher: AIP Publishing
DOI: 10.1063/5.0235189