The Materials Informatics group

© 2021 PantherMedia

The first goal of the research group Material Informatics is to develop machine learning methods that can be applied exploratively, i.e. informatics for materials.

  • We identify and classify active (smart/intelligent) materials and structures based on their stimulus sensitivity: An input non-mechanical environmental value leads to a mechanical output; the normalized stimulus-strain-curve can be linearized at the working point to extract this sensitivity parameter. The influences from multi-sensitivity in the material can be used to tune this sensitivity in the unified response.
  • We find and visualize data for various active materials like Hydrogels, Dielectric Elastomer Actuators, Conductive Polymers, Shape Memory Alloys, Shape Memory Polymers, Piezo-ceramics, Ionic Polymer Metal Composites. The data is applied for the training of different deep learning model classes to mirror their physically described behavior. For data visualization, we experiment with state-of-the-art augmented reality devices.

As a second goal, we focus on the logical behavior of multisensitive active materials, i.e. informatics by materials:

  • We use the methods from signal theory to investigate the transfer of information between physical fields. Therefore, we apply continuum-based multi-field models that can accurately describe the interactions, e.g. between the thermal and chemical field. Applications are e.g. an automatically rain-proofing bike-helmet.
  • We identify and model multisensitive materials that can act as neurons in an autonomous non-electrical deep learning network.

If you are interested in our group's activities, please contact us!

Dr.-Ing. Adrian Ehrenhofer graduated from Technische Universit├Ąt Dresden in 2014 and started his PhD thesis with the main focus on modeling and simulation of smart materials. He worked on the description of Ionic Polymer Metal Composites (IPMC) and permeation through biological membranes using the multifield Poisson-Nernst-Planck multi-field approach. In his PhDthesis, he developed an analogy description for the swelling behavior of active hydrogels. The model was applied for active hydrogel-layered polymeric membranes which are used for microfluidic cell-sorting. He defended his PhD thesis in 2018 and worked as a Post-Doc in the field of smart material modeling for chemo-physical intelligence at Technische Universit├Ąt Dresden, Germany. He also works as a freelance media creator for tutorial videos in mechanics.

Name Description
ORCID iD: ORCID logo0000-0002-2370-8381
Publons: 4202905
Scopus ID: 57188759919
Google Scholar ID: Adrian Ehrenhofer
ResearchGate: Adrian Ehrenhofer
LinkedIn: 3703421a4
Tel.: +49 351 463-39171
Fax: +49 351 463-32450
E-mail: adrian.ehrenhofer@tu-dresden.de
Room: ZEU/344

Open Access

© 2021 PantherMedia / Freeograph

I belive that science should be free and accessible for everyone. Therefore, all my publications are Open Access, either by the publication in Open Aceess journals or by officially publishing them as Green Open Access articles through the University Library SLUB. The publications are stored in the correct version on the QUCOSA server.

If you find any of my publications that aren't publicly available yet, please contact me!