Materials data science and AI in Materials Science and Engineering with a focus on micromechanics

Date

Jun 10, 2026

Time

1:00 PM - 2:00 PM

Speaker

Prof. Dr. Chris Eberl

Affiliation

Fraunhofer IWM, Freiburg

Series

IFW Kolloquium

Language

en

Main Topic

Materialien

Host

Description

The advancement of available AI models allows to tackle different challenges in Materials Science and Engineering. In this talk, applications of different AI models shall be discussed in the context of micromechanics. This allows to accelerate scientific workflows and automate tasks. The following examples will be discussed: (1) materials selection via simple models, (2) classification and segmentation for microstructure analysis as well as damage identification and quantification via deep learning, (3) fatigue lifetime prediction via graph neural networks and explainable AI approaches based on structured data from high-throughput small scale testing, (4) data and information extraction from publications via Large Language Models and (5) semantic extraction and ontology learning for MSE via agentic AI. While AI accelerates and enriches our understanding of materials, reliability and uncertainties need to be critically reviewed. Therefore, training data, training methodologies as well as model selection are all relevant ingrediencies and will be discussed. The talk will conclude by introducing national and international initiatives: NFDI, NFDI-MatWerk (since 2021), MaterialDigital (since 2019), and Materials Commons (2026 - 2030 EU) and how to participate.

Links

• Seminars at the IFW

Last modified: May 27, 2026, 7:37:10 AM