WG4: Machine Learning

First topical workshop on Machine Learning

The abstract submission is still open for our 2-day event related to the WG4 “Machine Learning” here: https://mecanano-wg4-24.sciencesconf.org/

The workshop will be held on 22-23 February 2024 in Louvain-la-Neuve, Belgium. (Note: this is a different city to Louvain!)

The community is rather diverse and has a large range of experienced in the use of ML: many of you already use this technique routinely while others do not have hands-on experience with ML, but would be interested to exploit its powerful capacities on their research data. Thus, the meeting is designed to meet everyone’s needs: four one-hour tutorials are planned that will provide in-depth case studies through examples of using ML on experimental and modelling data. In addition, contributed talks and posters will give insights into various successful applications of ML.

We want to emphasize that in accordance with the concept of the meeting, we not only accept abstracts on already established applications of ML, but also on promising ideas of future applications.

In addition, everyone interested to ML is welcome to attend, even without submitting an abstract. Registration will open soon!


Prof. Dr. Stefan Sandfeld

Director of the Institute for Advanced Simulation
Materials Data Science and Informatics (IAS-9)

Forschungszentrum Jülich GmbH
52428 Jülich

Péter Dusán Ispánovity
Associate Professor
Department of Materials Physics
Eötvös Loránd University
Pázmány Péter sétány 1/A, 1117 Budapest

Dr. Flavio Abreu Araujo

 Louvain School of Engineering

Institute of Condensed Matter and Nanosciences

Bio and soft matter

Croix du Sud 1/L7.04.02

1348 Louvain-la-Neuve


The Action will promote the use of machine learning in combination with nanoscale techniques in order to boost the amount and value of information extracted from a single experiment.

Task 4.1 Disseminate mainstream machine learning techniques in the community (milestone 4)

Task 4.2 Identify applications of ML with high efficiency gains for nanocharacterization

Task 4.3 Identify applications of ML with high efficiency gains for nanomechanical testing.