On Wednesday December 7 2022 at 10:30 CET, John R. Helliwell of the University of Manchester will give the talk Applying the FAIR Principles to Crystallography Data Publication – a use case for DAPHNE4NFDI? at the NFDI Physical Sciences Joint Colloquium in Hamburg and online.

For more information and the registration link see the event page. 

Abstract

Crystallography is a discipline which has strived for decades to ensure availability of its data with its publications. This has involved harnessing digital storage media at every stage of their development through punched cards, magnetic tapes, disks and exemplified today by ‘the cloud’. Crystallography has a highly developed databases’ infrastructure which commenced with the Cambridge Structure Database in the 1960s and to the Protein Data Bank from 1971 onwards. There are community-agreed processed diffraction data and model validation checks that are routinely made, known as the Crystallographic Information Framework. Although this system is not perfect, it provides the best chance for ensuring reliability and thereby trust in what we do. This approach is summed up by the FAIR (Findable, Accessible, Interoperable, and Reusable) movement. More generally, the funding agencies, in their response to governments and taxpayers, also seek faster discoveries and, if possible, better value for their money. Thus, raw data could be released for use beyond the original research team, usually after an embargo period of typically 3 years. There is an expansion of the synchrotron, X-ray laser and neutron facilities’ capacities to archive raw data. The colossal expansion of the raw data archives presents excellent
opportunities to all scientists, including users of the photon and neutron facilities. In Germany the National Research Data Infrastructure Germany (NFDI) is bringing proper data management tools and metadata harvesting to many science areas including the photon and neutron sciences (DAPHNE4NFDI, DAta from PHoton and Neutron Experiments). DAPHNE4NFDI offers an exemplary approach to research raw data management strategy from proposal, to data catalogue to linking to publication.

Recordings of the FAIRmat tutorial on NOMAD Oasis and FAIR data collaboration and sharing are now available on YouTube !

The YouTube playlist includes the following sessions:

• Talk by Christoph T. Koch: The potential of digital encyclopedias in materials science
• Hands-on session with Lauri Himanen: Introduction to the NOMAD Encyclopedia

All information about the tutorial including slides & the link to the YouTube playlist can be found on the tutorial 5 page. 

On September 22-23, 2022, FAIRmat Area B held the Workshop on data exchange and storage in ellipsometry in Leipzig. This was the first workshop in the Community meets technology partners series, which aims to lay a foundation of cooperation for users and vendors to work together on making experimental data FAIR. At this first meeting, members of the scientific community and the technology partners worked together towards the goal of FAIR data handling in ellipsometry. Their very fruitful discussion was focused on reviewing a specific application definition, NXellipsometry, in detail.

You can read more about this workshop and the whole event series on the event page.

In October 2022 we are celebrating our first anniversary! As we look back on everything we have accomplished in our first year, we want to thank all of the researchers, developers and other colleagues who have joined our team so far!

• To see what we have been up to so far, check out our news page.
• To see what we have coming up, see our events page.
• If you are interested in joining us you can subscribe or apply for one of our open positions.

On Friday Septmber 9, Christopher M Wolverton of Northwestern University gave the talk The Phase Diagram of All Inorganic Materials at the NFDI Physical Sciences Joint Colloquium in Berlin.
The colloquium is now available to watch on our YouTube channel.

You can also read a review of the event on the MatWerk website. 

Abstract
One of the holy grails of materials science, unlocking structure-property relationships, has largely been pursued via bottom-up investigations of how the arrangement of atoms and interatomic bonding in a material determine its macroscopic behavior. Here we consider a complementary approach, a top-down study of the organizational structure of networks of materials, based on the interaction between materials themselves. We demonstrate the utility of applying network theory to materials science in two applications: First, we unravel the complete “phase stability network of all inorganic materials” as a densely-connected complex network of 21,000 thermodynamically stable compounds (nodes) interlinked by 41 million tie-lines (edges) defining their two-phase equilibria, as computed by high-throughput density functional theory. Using the connectivity of nodes in this phase stability network, we derive a rational, data-driven metric for material reactivity, the “nobility index”, and quantitatively identify the noblest materials in nature. Second, we apply network theory to the problem of synthesizability of inorganic materials, a grand challenge for accelerating their discovery using computations. We use machine-learning of our network to predict the likelihood that hypothetical, computer generated materials will be amenable to successful experimental synthesis.

The paper Similarity of materials and data‑quality assessment by fingerprinting by Martin Kuban, Šimon Gabaj, Wahib Aggoune, Cecilia Vona, Santiago Rigamonti and Claudia Draxl appeared in the October 2022 MRS Bulletin.

Abstract
Identifying similar materials (i.e., those sharing a certain property or feature) requires interoperable data of high quality. It also requires means to measure similarity. We demonstrate how a spectral fingerprint as a descriptor, combined with a similarity metric, can be used for establishing quantitative relationships between materials data, thereby serving multiple purposes. This concerns, for instance, the identification of materials exhibiting electronic properties similar to a chosen one. The same approach can be used for assessing uncertainty in data that potentially come from different sources. Selected examples show how to quantify differences between measured optical spectra or the impact of methodology and computational parameters on calculated properties, like the density of states or excitonic spectra. Moreover, combining the same fingerprint with a clustering approach allows us to explore materials spaces in view of finding (un)expected trends or patterns. In all cases, we provide physical reasoning behind the findings of the automatized assessment of data.

The latest edition of the Adlershof Journal included an article interviewing our spokesperson Claudia Draxl about the work of FAIRmat. 

You can read the article online for free or find a paper copy in one of the Adlershof Technology Center buildings. 

On Friday Septmber 9 at 11:00 CEST, Christopher M Wolverton of Northwestern University will give the talk The Phase Diagram of All Inorganic Materials at the NFDI Physical Sciences Joint Colloquium in Berlin and online.

For full information and the registration link see the event page. 

Abstract

One of the holy grails of materials science, unlocking structure-property relationships, has largely been pursued via bottom-up investigations of how the arrangement of atoms and interatomic bonding in a material determine its macroscopic behavior. Here we consider a complementary approach, a top-down study of the organizational structure of networks of materials, based on the interaction between materials themselves. We demonstrate the utility of applying network theory to materials science in two applications: First, we unravel the complete “phase stability network of all inorganic materials” as a densely-connected complex network of 21,000 thermodynamically stable compounds (nodes) interlinked by 41 million tie-lines (edges) defining their two-phase equilibria, as computed by high-throughput density functional theory. Using the connectivity of nodes in this phase stability network, we derive a rational, data-driven metric for material reactivity, the “nobility index”, and quantitatively identify the noblest materials in nature. Second, we apply network theory to the problem of synthesizability of inorganic materials, a grand challenge for accelerating their discovery using computations. We use machine-learning of our network to predict the likelihood that hypothetical, computer generated materials will be amenable to successful experimental synthesis.

The FAIRmat hands-on tutorial series will resume on October 5-6, 2022 with Tutorial 5: NOMAD Encyclopedia. 

The tutorial will take place on Zoom. For a full description and registration see the tutorial page. 

FAIRmat is delighted to welcome Dr. Walid Hetaba as the leader of Task B3: Core-level Spectroscopy. 

Dr. Hetaba is the leader of the Electron Microscopy group in the department Heterogeneous Reactions at the Max Planck Institute for Chemical Energy Conversion (MPI CEC).