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Sixth FAIRmat Users Meeting

Jul 2 – 3, 2025
Ruhr-Universität Bochum
Europe/Berlin timezone

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7. FAIR Research Data Management with FAIRmat and NOMAD
Hampus Näsström (Humboldt-Universität zu Berlin, FAIRmat)
7/2/25, 9:00 AM

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...
8. Quantitative XPS Analysis Using Convolutional Neural Networks
Lukas Pielsticker (MPI for Chemical Energy Conversion)
7/2/25, 9:30 AM

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...
9. Small-Data Models for Materials Design
Luca Ghiringhelli
7/2/25, 10:00 AM

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...
17. The Alexandria Database of Materials
Miguel Marques
7/2/25, 11:00 AM

The Alexandria database represents an important resource for materials science research, containing more than 5 million density-functional theory calculations for periodic three-, two-, and one-dimensional compounds. This open-access database addresses the critical challenge of data scarcity in materials science, where large, high-quality, and consistent datasets are rare. The Alexandria...
18. Creating Data Analysis Pipelines Using the MADAS Framework
Martin Kuban
7/2/25, 11:30 AM

The NOMAD data infrastructure provides access to vast amounts of data that can be used for data analytics and machine learning (ML). Often, however, not all (meta)data are relevant for every task, making it necessary to apply filtering and processing steps to prepare input data for ML.
Here, we present MADAS, a Python framework that supports all steps of data analytics and machine learning,...
6. Role-Based NOMAD Usage and Development
Joseph Rudzinski (Humboldt University), Hampus Näsström (Humboldt-Universität zu Berlin, FAIRmat)
7/2/25, 1:00 PM

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...
21. Artificial Neural Network Inference on FPGAs in PUNCH4NFDI
Johann C. Voigt
7/3/25, 9:00 AM

Upcoming experiments in particle physics and astrophysics like the High-Luminosity LHC and the Square Kilometre Array will be producing data at a rate of multiple TBit/s, pushing beyond the limits of what can reasonably be stored permanently. This drives the need for a fast readout and real-time reconstruction of the data as part of a trigger system, that selects interesting events to store,...
22. Towards FAIR Workflows in Computational Materials Science
Sarath Menon
7/3/25, 9:30 AM

Computational materials science workflows often involve numerous steps, integrate diverse software tools, span multiple length and time scales, and cover a broad range of material compositions, structures, and thermodynamic conditions. Therefore, ensuring reproducibility, data reusability, and meaningful interpretation requires not only detailed descriptions of data and metadata at each stage...
20. Neuro-symbolic Organization of Research Contributions with Knowledge Graphs and Large Language Models
Sören Auer
7/3/25, 10:00 AM

In the evolving landscape of scientific knowledge management, the integration of neuro-symbolic AI approaches offers opportunities to enhance the organization, discovery, and synthesis of research contributions. We explore how knowledge graphs and large language models (LLMs) can be synergistically combined to advance the representation and accessibility of scholarly knowledge. At the heart...
14. Text Mining in Materials Science
Markus Stricker
7/3/25, 11:00 AM

Lei Zhang, Doaa Mohamed, Sepideh Baghaee Ravari, Markus Stricker

Beyond the direct raw data sources experiments and simulations, scientific publication are an underused resource at scale. The content of scientific publications can be converted into high-dimensional vector representations to gain access to the underlying correlations. Raw text can be converted to word embeddings (word2vec)...
15. Transforming Materials Science with Transformers
Nawaf Alampara
7/3/25, 11:30 AM

The advent of large-scale transformer models has opened new frontiers in scientific discovery, with materials science poised for a significant transformation. These models promise to accelerate research by automating tasks from literature review to property prediction. This talk will provide a critical evaluation of the current state of transformer-based models in chemistry and materials...
5. Large Language Models for Scientific Data Extraction
Mara Schilling-Wilhelmi, Sharat Patil
7/3/25, 1:00 PM

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.
23. Bridging Materials Communities: Area E’s Testbed for FAIR Data Infrastructure

The FAIRmat Area E initiative demonstrates the value of FAIR (Findable, Accessible, Interoperable, and Reusable) data practices through targeted use-case demonstrators across diverse materials-science domains. Seven representative tasks (E1–E7) form the backbone of Area E, each illustrating how FAIRmat infrastructure supports real-world scientific workflows:

In batteries (E1), integration...
12. Extending NOMAD parsers for some codes used in the UMIL NFFA-DI Theory&Simulation Installation

In this poster I am presenting the work we are performing at the UMIL Operative Unit (OU) of the NFFA-DI project on extending the functionalities of some of the NOMAD parsers for computational materials science codes.
The NFFA-DI project (Nano Foundries and Fine Analysis - Digital Infrastructure, nffa-di.it [1]) aims at building an italian distributed Research Infrastructure (RI) for...
10. FAIR data management for computational materials science using NOMAD

J.F. Rudzinski, E. Boydas, N. Daelman, M. Kuban, and B. Mohr

Physics Department and CSMB Adlershof, Humboldt-Universität zu Berlin

NOMAD [nomad-lab.eu] [1] is an open-source, community-driven data infrastructure, focusing on materials science data. The NOMAD software [nomad-lab.eu] can automatically extract data from the output of over 60 simulation codes, has been extensively expanded...
16. HZB’s Tailored Digital Lab Workflows Towards AI-ready Datasets
Dr Ana Velazquez (Helmholtz-Zentrum Berlin (HZB)), Carla Terboven (Helmholtz-Zentrum Berlin (HZB))

Digital transformation in experimental Science is driven by efficient data management and workflow integration across all processes, from sample synthesis to specialized measurements, allowing for structured, machine-readable, AI-ready datasets that are aligned with FAIR principles.
At HZB, we focus on driving a paradigm shift by supporting the transition from analog workflows to seamless...
19. ICSP@FAU Poster: Developing an all-in-one ELN and RDM tool for university scale facilities

Research data management (RDM) on the scale of university facilities in contrast to largescale facilities has different requirements and challenges. First and foremost are the need to catalogue data from a large variety of different techniques and instruments as well as reliably document the work of frequently changing researchers and projects. For this reason, the ICSP@FAU group within use...
13. Making FAIR Data Work in the Lab: How NOMAD Is Changing the Game

Research labs generate vast amounts of data capable of unlocking new opportunities in materials science when analyzed with specialized algorithms, yet most remains trapped on local data silos without contextual metadata, inaccessible for advanced analysis. At Helmholtz Zentrum Berlin, we developed a suite of Python applications that motivate scientists to actively use the NOMAD workflow. Our...
24. NFFA-DI infrastructure as nanoscience FAIR data generator

In our poster we present the NFFA-DI infrastructure as a powerful usecase of NOMAD Oasis. NFFA-DI is an italian distributed infrastructure dedicated to nanoscience research activities from sample production and caracterization to theoretical modelling, allowing users to access multiple state-of-the-art facilities with a single scientific proposal.

One of the strategic challenges faced by...
11. RefXAS: XAS reference database under DAPHNE4NFDI

Abstract
With rapid increase in the advance beamline set ups across Synchrotron facilities for time-resolved as well as space resolved measurements and availability of laboratory-based spectrometers optimized for day to day measurements, X-ray Absorption Spectroscopy (XAS) has become a standard approach for the structural characterisation of complex materials. With the growing volume of...