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On April 6 & 7, our FAIRmat hands-on tutorial series continues with a tutorial on the NOMAD Artificial-Intelligence (AI) Toolkit, the platform for running (jupyter) notebooks to analyse with AI tools the data contained in the NOMAD Archive.
We will cover, in an interactive, hands-on fashion, the several aspects of the AI-toolkit: the query over the NOMAD Archive via the NOMAD API, the basic notebooks for learning AI methods, and the advanced notebooks, where the workflow of relevant publications, in which AI is applied to materials science, can be interactively reproduced and further explored. Furthermore, we will introduce the local AI-toolkit app that allows to run a local version of the notebooks, e.g., to combine own data with the NOMAD Archive data.
At the end of the first day, few tutorial notebooks will be suggested to be perused by the participants before the second day starts. In the second day, break-out rooms will be organized, and in each room one of the selected tutorial notebooks will be discussed.
NOMAD CoE researchers from TU Wien and the Fritz Haber Institute have developed novel computer codes to enable massively parallel and highly accurate coupled cluster theory simulations of materials.
More and more demand is generated by large funding agencies around the world that researchers publish their data in open-access repositories in a reusable, FAIR way. This is also supported by EU and German federal research agencies as shown by the developments of the European Open Science Cloud (EOSC), and German National Research Data Infrastructure (NFDI) projects.
Such a movement requires that the data is annotated with well defined metadata, where the naming convention also follows standards. In order to achieve this, researchers have to be able to access the measured raw data together with any results derived by subsequent data processing.
The workshop reviews the opportunities provided by the scientific community standard NeXus.
We are happy to invite you for the NFDI NeXus Workshop to discuss the use of the NeXus glossary/ontology, corresponding data format and FAIR Data management in general.
On March 9 and 10, our FAIRmat hands- on tutorial series continues with a tutorial on ELNs and FAIR data management.
This tutorial is reviewing the need of using electronic lab notebooks (ELNs) in materials science research labs, synthesis, and experimental characterisation facilities. A special emphasis is put on efficiently collecting all metadata and performing FAIR data management which not only facilitates organising the work better and making the processes in the lab more performant but also guarantees the possibility of data reuse via offering machine readability and machine interpretability.
The tutorial is also addressing the questions: Which kind of choices are available and how to set up such ELN system for a laboratory? A demonstration of such a lab setup will also be provided on the first day.
On the second day, three more presentations will guide you through the details of implementing such a setup for your own lab. For this purpose, we have chosen relevant, but simple use cases which can help you in mapping our strategies and solutions onto your institute.
Program
Mar 9, 2022:
Overview Talk
15:00 CET - John Henry Scott (NIST): Data-Centric Materials Science - The Critical Role of ELNs
16:00 CET - Mark Greiner: ELNs in FAIR data management
Hands-on Tutorial
17:00 CET - Christoph T. Koch: Demonstration of the eLabFTW and NOMAD in daily use
18:00 CET - End of the 1st day
Mar 10, 2022:
10:00 & 16:00 CET - José Márquez: Setting up eLabFTW for a simple lab
10:30 & 16:30 CET - Sherjeel Shabih: Integrating eLabFTW and NOMAD
11:15 & 17:15 CET - Markus Scheidgen: NOMAD ELN - ELN features integrated into NOMAD
12:00 & 18:00 CET- End of the 2nd day
Tutorial Chair: Sandor Brockhauser
You can find all videos and exercises of the first FAIRmat tutorial on Publishing and Exploring Data with NOMAD on our website and on our YouTube channel!
Website with exercises: https://www.fair-di.eu/fairmat-tutorials-1
YouTube playlist with videos of the overview talk by Matthias Scheffler and the hands-on tutorials by Markus Scheidgen:
On February 9, the brand new FAIRmat hands-on tutorial series begins!
Feb 9, 15:00 CET
Matthias Scheffler: Making the Data Revolution Happen – How Sharing and FAIRification Is Changing the Science Already Today
15:45-18:00 CET
Markus Scheidgen: Publishing and Exploring Data with NOMAD: How-to and Exercises
Feb 10, 10:00-12:00 and 16:00-18:00 CET
Markus Scheidgen: Questions and Answers
The tutorial series continues every second Wednesday of the month and the following Thursday.
More info here: https://www.fair-di.eu/fairmat-tutorials-home
Our FAIRmat - NFDI4Cat collaboration was accepted for publication in ACSCatalysis. You can download the preprint of "Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence" here.
L. Foppa, C. Sutton, L. M. Ghiringhelli, S. De, P. Löser, S.A. Schunk, A. Schäfer, and M. Scheffler,
Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence
Preprint Download: chemrxiv
In a newly accepted Nature Perspective article Matthias Scheffler and colleagues describe the challenges of establishing a FAIR (Findable, Accessible, Interoperable, and Re- usable) data infrastructure.
Hands-on FAIRmat Tutorials
Every second Wednesday of the month and the following Thursday
https://www.fair-di.eu/fairmat-tutorials-home
FAIRmat introduces its efforts towards a FAIR data infrastructure for condensed-matter physics and the chemical physics of solids in its new hands-on tutorial series. We will show how FAIRmat can help research work already today and how you will get prepared for the new scientific opportunities of tomorrow.
The tutorials consist of overview talks by internationally renowned scientists and hands-on tutorials by our FAIRmat developers.
We are looking forward to seeing you there!
Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides
A. Mazheika, Y. Wang, R. Valero, F. Vines, F. Illas, L. Ghiringhelli, S. Levchenko, and M. Scheffler of the NOMAD Laboratory of the Fritz Haber Institute developed and advanced artificial intelligence methods that enable the identification of basic materials parameters that correlate with materials properties and functions of interest (here the activation of CO2).
