Sixth FAIRmat Users Meeting

Jul 2, 2025, 9:00 AM → Jul 3, 2025, 4:00 PM Europe/Berlin

UFO (Ruhr-Universität Bochum)

We are pleased to announce the Sixth FAIRmat Users Meeting, taking place at Ruhr-Universität Bochum on July 2-3, 2025.

The FAIRmat Users Meeting is a central event for scientists in physics, chemistry, and materials science who are passionate about research data management (RDM) and its role in accelerating scientific discovery.

The upcoming meeting will focus on AI- and ML-driven materials science and how the FAIR data principles make research data "AI-ready". Learn how NOMAD, our free web-based platform, is more than just a data repository – it is an enabler for next-generation materials discovery using machine learning and artificial intelligence.

Whether you are a seasoned NOMAD user, exploring its potential, or just starting your RDM journey, this event will offer valuable insights, practical tools, and meaningful connections.

What to expect:

• Invited talks from experts using NOMAD and NOMAD Oasis for AI/ML applications in computation, experimentation, and synthesis.
• Hands-on tutorials on the NOMAD AI Toolkit, downloading ML-ready datasets, and getting started with NOMAD.
• Poster session open to external contributions.
• Informal support desks for direct interaction with NOMAD developers and FAIRmat domain experts.
• Networking opportunities.

We look forward to two days of stimulating discussions, hands-on sessions, and community building!

Wednesday, July 2

Harnessing Data-Driven Methods in Materials Discovery

1 FAIR Research Data Management with FAIRmat and NOMAD

In this talk, I will present the latest developments of FAIRmat and the NOMAD ecosystem, emphasizing the critical aspects of FAIR research data management (RDM) in condensed-matter physics. The NOMAD ecosystem, which includes platforms such as NOMAD, NOMAD Oasis, and NOMAD CAMELS, provides a complete solution for an RDM framework tailored to the needs of the condensed-matter physics and materials-science communities. I will demonstrate how these platforms facilitate the systematic acquisition and management of research data from diverse sources, including materials synthesis, advanced characterization techniques, and simulations. The presentation will also cover how NOMAD ensures that research data is not only securely stored, but also remains accessible and interoperable across different research domains. This in turn supports collaborative research efforts and accelerates innovation by ensuring that data is AI-ready.

Speaker: Hampus Näsström (Humboldt-Universität zu Berlin, FAIRmat)

2 Quantitative XPS Analysis Using Convolutional Neural Networks

X-ray photoelectron spectroscopy (XPS) is a powerful tool for studying the electronic structure and chemical composition of solid surfaces. Quantitative analysis of XP spectra typically relies on manual curve fitting by expert spectroscopists. However, recent advancements in the ease of use and reliability of XPS instruments have led to a growing number of (novice) users generating large datasets that are becoming difficult to analyze manually. Additionally, the expansion of publicly available XPS databases further increases the volume of data requiring efficient analysis. Reflecting these developments, more automated techniques are desirable to assist users in processing large XPS datasets.

Here we present a scalable framework for automated XPS quantification using convolutional neural networks (CNNs). By training CNN models on artificially generated XP spectra with known quantifications (i.e., for each spectrum, the concentration of each chemical species is known), it is possible to obtain models for auto-quantification of transition metal XP spectra [1]. CNNs are shown to be capable of quantitatively determining the presence of metallic and oxide phases, achieving accuracy comparable to or exceeding that of conventional data analysis methods. The models are flexible enough to handle spectra containing multiple chemical elements and acquired under varying experimental conditions. The use of dropout variational inference for the determination of quantification uncertainty is discussed. Finally, we demonstrate how these network models are integrated into NOMAD [2], enabling real-time analysis of newly generated data.

References
[1] Pielsticker, L.; Nicholls, R.; DeBeer, S.; Greiner, M., Analytica Chimica Acta 2023 1271, 341433.
[2] Scheidgen, M., et al., Journal of Open Source Software 2023 8.90, 5388.

Lukas Pielsticker [1], [2], Rachel L. Nicholls [1], Serena DeBeer [1], Walid Hetaba [1], Florian Dobener [2], Laurenz Rettig [3], José A. Márquez [2], Sandor Brockhauser [2], Heiko Weber [4], Claudia Draxl [2], Mark Greiner [1]

[1] Max Planck Institute for Chemical Energy Conversion, Mülheim an der Ruhr, Germany
[2] Physics Department and CSMB, Humboldt-Universität zu Berlin, Germany
[3] Department of Physical Chemistry, Fritz Haber Institute of the Max Planck Society, Berlin, Germany
[4] Chair of Applied Physics, FAU Erlangen-Nürnberg, Erlangen, Germany

Speaker: Lukas Pielsticker (MPI for Chemical Energy Conversion)

3 Small-Data Models for Materials Design

The modeling of macroscopic properties of materials often require to accurately evaluate physical quantities at several time and length scales. Here we show how symbolic inference, i.e., the machine learning of simple analytical expressions that explain and generalize the available data, can effectively bridge physical scales. The focus is on learning models that are as simple as possible (but not simpler…), with as few as possible data points. I will demonstrate the application of the methods to the modeling of catalytic properties of materials, thermal conductivity, and more.

Speaker: Luca Ghiringhelli

10:30 AM Coffee Break

Exploring AI & ML in Simulations, Databases, and Computational Workflows

12:00 PM Lunch

Hands-on Workshop

4 Role-Based NOMAD Usage and Development

NOMAD now provides a broad ecosystem of data infrastructure software and tools, enabling robust data management at the individual, research group, and institutional level. To navigate this ecosystem, it is useful to clearly identify your role and desired usage. In this tutorial, we will assist you in getting started down the right path, whether you are already an experienced user or are brand new to NOMAD.

You will choose from one of the following roles/topics to explore NOMAD’s capabilities:

USER
• Project and workflow management (recommended for users with a basic Python setup and minimal coding knowledge): organize, process, share, and publish datasets; interact programmatically via a simple Python API; customize entries and workflows.
• Basic NOMAD usage via the GUI: upload, share, and publish data; use NOMAD’s electronic laboratory notebook (ELN) interface with built-in schemas; create ELNs with custom extensions.

APPLICATION ADMINISTRATOR (ADVANCED USER)
• Plugin development: create a plugin repository using a cookie-cutter template; transform custom YAML/JSON schemas into Python code; add automation and plotting features.

SYSTEM ADMINISTRATOR
• Local infrastructure setup: install and configure your own NOMAD Oasis; set up CI pipelines and create custom images with plugins.

Speakers: Joseph Rudzinski (Humboldt University), Hampus Näsström (Humboldt-Universität zu Berlin, FAIRmat)

3:00 PM Coffee Break

Poster Session & Meet Our Experts

7:00 PM Dinner

Thursday, July 3

AI & ML Activities Across NFDI Consortia

10:30 AM Coffee Break

Language Models for Materials Science

12:00 PM Lunch

Hands-on Workshop

5 Large Language Models for Scientific Data Extraction

Structured data is key to machine learning – but much of the world’s information is unstructured. This tutorial introduces how Large Language Models (LLMs) can be used to extract structured data from scientific publications. After a 30-minute introduction talk, participants will dive into a hands-on session exploring practical steps of the extraction workflow.

Speakers: Mara Schilling-Wilhelmi, Sharat Patil