Data-Integrated Design of Functional Matter Across Scales

Project Network 3

Relation of the RQs in PN3

Particle and continuum methods are established tools for the simulation of materials, biological matter, and technical processes. However, isolated approaches in these fields face substantial problems, e.g., for predictive materials design from first principles. Thus, the overarching goal of this Project Network is to develop novel methods for data integration into particle and continuum simulation models. Specifically, the predictive power of model, dimensionality, or complexity reduction in materials science and biotechnology will be enhanced by incorporating data from different sources, including experiments, simulations, or rapidly growing available databases. In contrast to standard parameter identification across scales, PN 3 will pioneer a holistic integration of data into model reduction techniques and machine-learned models respecting physical constraints. Target applications beckon in several fields of engineering and sciences, and our methods will therefore contribute to each of our three Visionary Examples.

Research Questions

RQ 1 Scale bridging for particle models: How can we realize scale-bridging and coarse-graining of particle models by data-assisted model reduction?

RQ 2 Complexity reduction in continuum models: How can we reduce complexity in continuumbased models by data-assisted model reduction?

RQ 3 Machine-learned models: How can we develop particle and continuum models based on machine learning from experimental and simulation data?

Project Overview

PN 3-1 Processing uncertain microstructural data
PN 3-1 (II) Data-integrated scale bridging for all-solid state batteries: micro- to mesoscale
PN 3-2 Data-based model reduction and reanalysis
PN 3-2 (II) Reduced Model Reanalysis for Path-dependent Multifield Problems
PN 3-3 Electrokinetic characterization of conducting two-phase flows in model porous media
PN 3-3 (II) Electrokinetic characterization of conducting two-phase flows in model porous media
PN 3-4 Characterization of potential energy surfaces using machinge-learning techniques
PN 3-4 (II) Simulation of Surface Processes using Machine-Learned Potentials
PN 3-5 Data-integrated Multiscale Modeling of Diffusion-driven Processes in Porous Media
PN 3-5 (II) Data-driven multi-scale stability analysis of multi-stimuli-responsive hydrogels
PN 3-6 Data-integrated simulation of enzymes
PN 3-7 Strengthening mechanisms of Cu-Ni-Si-Cr alloys through simulations and Machine Learning potentials
PN 3-8 Optimization of transferable force fields based on reduced order and surrogate models
PN 3-8 (II) Pore topology and surface design for energy storage applications
PN 3-10.1 Simulations of hydrogen embrittlement in Nibased super alloys
PN 3-10 (II) Data-integrated scale bridging for all-solid state batteries: From electrons to atoms
PN 3-11 Biological Molecular Dynamics Simulations 2.0
PN 3-12 Data-Integrated Simulation Of Magnetic Gels
PN 3-13 A Data-Driven Approach to Viscous Fluid Dynamics
PN 3-14 Approximation and learning density matrices

Associated Projects

Marc-André Keip

Prof. Dr.-Ing.
This image shows Blazej Grabowski

Blazej Grabowski

Prof. Dr. rer. nat.

[Photo: (c) SimTech/Max Kovalenko]

To the top of the page