A cooperation of TU Darmstadt
and RWTH Aachen University

Simulation and Data Lab

The SDL Materials Design uses advanced computational methods to design better materials and optimize their mechanical and functional properties.

To this end we adapt deep learning methods and numerical algorithms to our data and evaluate them on materials specific metrics. The final goal is targeting material scientists from both experimental and computational background and providing cross-sectional showcases to bring them together.

Using high resolution panoramic images from Scanning Electron Microscopy (SEM) allows to have a large field of view of the microstructure. We use convolutional neural networks (CNNs) in tasks such as semantic segmentation, object detection and classification to find and locate damages and defects.

Further, we use the segmented images of microstructure as input to generate Finite Element (FE) mesh with interface details and carry out multiphase Finite Element (FE) simulations to evaluate the elastoplastic and fracture behavior of the samples in the framework of computational homogenization.

In the future, we plan to apply 3D convolutional models to SEM images in depth of the material to unravel the full nature of microstructures and damages. Additionally, Atom Probe Tomography (APT) which has the ability to explore the chemical composition of materials at near-atomic resolution will be used. We use deep learning to find good representations for the input 3D discrete data from APT, to enable them to perform tasks such as 3D segmentation and classification. The final goal of this analysis is to detect phase transformations in materials on an atomic scale.

One of the accomplished tasks is a deep learning segmentation model for nanowires which we made publicly available via a web-based application.

The SDL Materials Design benefits from the open-source visualization software OVITO, commonly used to analyze the 3D data from atomistic simulations. The plan is to extend its functionality and adapt the existing algorithms to experimental data.

If you have questions for other groups or general questions like access to the HPC infrastructure, have a look at our support website.

Current research topics:

  • Deep learning models for microstructural analysis of electron microscopy images
  • Finite element simulation on deep learning segmented microstructures as input
  • Deep learning models for 3D data from atom probe tomography
  • Robust structure identification for experimental and simulation data using machine learning

Support activities:

  • Providing an interactive web app for material scientists to use our models
  • Finding solutions for the community of materials scientists for large scale visualization within the OVITO software

Teaching activities:

  • Providing lectures on computational materials science to interested students
  • Gitlab/Github courses for material scientists
  • Organizing Hackathons for students and the community


Project partners


Prof. Dr. Karsten Albe

TU Darmstadt

Dr. Michal Fecik

RWTH Aachen University

Vasilios Karanikolas

TU Darmstadt

Prof. Dr. Sandra Korte-Kerzel

RWTH Aachen University

Dr. Ganesh Kumar Nayak

RWTH Aachen University

Aditi Mohadarkar

TU Darmstadt

Janis Sälker

RWTH Aachen University

Prof. Jochen Schneider, Ph.D.

RWTH Aachen University

Prof. Dr. Bai-Xiang Xu

TU Darmstadt