Prof. Claudia Draxl, FAIRmat’s spokesperson and Einstein Professor of Physics at Humboldt-Universität zu Berlin, has been elected to the German National Academy of Sciences Leopoldina, one of the highest scientific honors in Germany.

This prestigious recognition highlights her outstanding scientific contributions to condensed-matter theory, computational materials science, and the advancement of data-centric approaches to materials research.

In her role at FAIRmat, Prof. Draxl has actively shaped the development of research data infrastructure for materials science. Under her leadership, FAIRmat supports researchers in making their data Findable, Accessible, Interoperable, and Reusable (FAIR), promoting transparency, reproducibility, and reusability in scientific research.

Her election to the Leopoldina reflects not only her scientific excellence, but also her pioneering efforts to build a sustainable and open-data culture in science. It is a proud moment for the entire FAIRmat community.

We warmly congratulate Prof. Draxl on this well-deserved honor!

Recordings of FAIRmat Tutorial 16 are now available on the FAIRmat and NOMAD YouTube channel! The full playlist includes:

• Introduction to NOMAD public service and its main functionalities by Adrianna Wojas
• Exploring NOMAD entries with search filters and interactive widgets by Siamak Nakhaie
• From raw data files to published datasets by Ahmed Mansour
• Guiding through the steps for documenting research using NOMAD ELN functionality by Siamak Nakhaie

Follow along with the tutorial using the full tutorial guide. Slides and example files are available here.

For more FAIRmat hands-on tutorials, visit our website.

The FAIRmat team took part in the DPG Spring Meeting of the Condensed Matter Section at the University of Regensburg from March 16 to 21, 2025. Our colleagues from different Areas delivered multiple talks on recent developments in NOMAD to enable FAIR research data management in computational materials science and experimental research. Their presentations provided valuable insights into the practical implementation of the FAIR principles in various aspects of materials science.

As in previous years, together with DAPHNE4NFDI, we hosted a joint information booth at the DPG exhibition for scientific instruments and literature. The strong interest in NOMAD and research data management from the community keeps inspiring us to push forward!

A pleasant learning environment highlighted our tutorial session on Using NOMAD’s Workflow Utilities to Improve Data Management and Facilitate Discovery in Materials Science. If you missed our DPG Tutorial, it is available online for self-learning.

Last but not least, two insightful symposia stood out at this year's DPG Spring Meeting. The first focused on AI-driven Materials Design and was organized by Jörg Neugebauer, Silvana Botti, and Luca Ghiringhelli. The second, organized by Frank Schreiber, Bridget Murphy, and Claudia Draxl in collaboration with DAPHNE4NFDI, explored Pushing the Boundaries of FAIR Data Practices for Condensed Matter Insights.

We thank the organizers for a fantastic event and look forward to the next meeting in 2026!

We are excited to share the recordings of users' talks and invited speakers' presentations from our latest Users Meeting last November! The full playlist includes:

• FAIR Research Data Management with FAIRmat and NOMAD – Ahmed Mansour
• The FAIRification of PV research data – Eva Unger
• Enabling high-throughput materials discovery of phosphosulfides by developments in FAIR data management, visualization and analysis in NOMAD – Lena Mittmann
• Martignac: Computational workflows for reproducible, traceable, and composable coarse-grained Martini simulations – Tristan Bereau
• Achieving semantic interoperability in materials science data and simulation workflows – Abril Azocar Guzman
• An overview of NFDI4Cat´4 services and tools with a special focus on Voc4Cat the shared vocabularies for catalysis and related disciplines – David Linke

Subscribe to the FAIRmat and NOMAD YouTube channel now to stay updated on our latest insightful videos!

Last week, the FAIRmat team participated in Love Data Week 2025, an international campaign promoting research data and effective data management.

Siamak Nakhaie and Ahmed Mansour hosted insightful coffee talks on researchers' rights to the data they create and the challenges of managing diverse, large datasets. Both talks are available on our YouTube channel.

Meanwhile, our colleague Julia Schumann participated as an invited speaker at the NFDI4Cat consortium workshop, exploring various RDM solutions for catalysis research.

Let’s keep the spirit of Love Data Week alive and celebrate our passion for data all year round!

Our work on the symmetry-based clustering for efficient and accurate classification of atomistic structures has now been published in the journal npg Computational Materials.

The publication, titled “Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering” presents a novel symmetry-based clustering (SBC) algorithm for the automated identification and classification of atomistic structures, including bulk materials, two-dimensional materials, and interfaces. Unlike machine learning approaches, SBC requires no prior training, relying instead on the recognition of common unit cells within atomic systems. The publication is authored by FAIRmat members: Thea Denell, Lauri Himanen, Markus Scheidgen, and Claudia Draxl.

SBC is an iterative approach that decomposes complex structures into distinct components based on their symmetry properties, allowing for a comprehensive material description. It finds repeating patterns within the atomistic structure by selecting an atom of choice, searching its neighborhood for repetitions of the same atomic species, and then constructing the most likely unit cell. When the unit cell is found, all atoms that can be replicated by repeating that unit cell are assigned to a single component.

It's implemented in the MatID Python package, which is part of the NOMAD platform, and has been benchmarked against stacked 2D structures and grain boundaries, showing high accuracy and robustness even with noisy data. SBC effectively streamlines the exploration of large materials datasets by providing precise labeling of structural component, ensuring reproducible and consistent results, and offering versatility across diverse materials research applications.

On January 15-16, our colleague Andrea Albino (Area A: Synthesis) led a two-day workshop on the topic “On-boarding of New Users for the Customization of NOMAD Oasis.” The workshop introduced PhD students and researchers from Italian CNR institutes and universities to the essential features of the NOMAD platform and its local counterpart, NOMAD Oasis. The event aimed to provide practical guidance on how to use NOMAD for efficient research data management (RDM), and to equip scientists with the tools and knowledge to streamline data workflows in their experimental and computational research.

The workshop struck a perfect balance between theoretical concepts and practical sessions. It began with an exploration of NOMAD's schemas, including base sections, community standards, and custom YAML schemas. Participants received detailed guidance on extending existing schemas to align with specific experimental workflows. Another key topic was the development of parsers to populate NOMAD with experimental and computational data. Using template repositories, researchers created Python-based plugins for data processing.

Hands-on sessions reinforced theoretical knowledge, focusing on setting up the dedicated environment for NOMAD plugin development. A significant part of the workshop showcased real-life examples of extending data schemas, automating data processing workflows (available here), and integrating NOMAD into existing RDM frameworks.

By creating a collaborative environment and emphasizing practical learning, the workshop catalyzed participants' adoption of the platform. Additionally, their integration into the broader NOMAD community through its Discord channel ensures continued knowledge exchange and innovation, contributing to the platform's growth and development.

The recording of the workshop is available on FAIRmat’s YouTube channel.

The 6th edition of the FAIRmat newsletter is now available for download! Stay up to date with the latest project developments, enjoy an insightful interview with NOMAD user Lena Mittmann, and explore more exciting articles from the FAIRmat community. Download it now from our website!

Due to the great public interest in the Fourth FAIRmat Users Meeting at the FAU in Erlangen, we are excited to share the recordings of users' talks and invited speakers' presentations on the FAIRmat and NOMAD YouTube channel! The full playlist includes:

• Research Data Management in Collaborative Research Centres by Brit Redöhl
• Designing RDM in collaborative funding schemes by Heiko Weber
• Knowledge management and online AI training in NOMAD by Ta-Shun Chou
• Advancing Catalysis Research by Julia Schumann
• Low-scaling algorithms developed in NOMAD by Antonio Delesma Diaz
• Taylored RDM with NOMAD by Lauri Himanen
• Employing NOMAD CAMELS in an atom beam experiment by Carina Kanitz

You can read more about the event and see the contributions here.

We're happy to announce that our tutorial on structured data extraction with large language models (LLMs) has been published in Chemical Society Reviews!

The publication, titled 'From Text to Insight: Large Language Models for Chemical Data Extraction,' provides an in-depth overview of LLM-based structured data extraction in materials science and chemistry, synthesizing current knowledge and exploring future directions. It is a product of the FAIRmat AI Toolkit task led by Kevin Maik Jablonka, with contributions from Mara Schilling-Wilhelmi (Friedrich Schiller University Jena), Martiño Ríos-García and María Victoria Gil (CSIC, Oviedo), Sherjeel Shabih, Christoph T. Koch, and José A. Márquez (Humboldt-Universität zu Berlin), and Santiago Miret (Intel Labs).

To improve interactivity and applicability, we've created a Jupyter book with practical examples tailored to chemistry and materials science. These examples can now be run directly using the Jupyter4NFDI service provided by Base4NFDI - Basic Services for the NFDI.

• Read the online book.
• Run it in Jupyter4NFDI.