FAIRmat provides research data management concepts and solutions for the field of solid-state physics. Its NOMAD portal has developed mature concepts and technological solutions for storing data according to the FAIR principles for selected theoretical and experimental data. Generalizing this approach is challenging due to the field’s diversity and complexity and due to missing standards.
In...
Efforts in materials science face challenges due to the heterogeneity and complexity of data sources, disparate data formats, and the need for standardized metadata. The NFDI-MatWerk ontology (MWO) [1] and the Materials Science and Engineering Knowledge Graph (MSE-KG) [2] aim to address these challenges by providing a unified framework for representing and integrating diverse data types and...
NFFA-DI (Nanoscience Foundries and Fine Analysis – Digital Infrastructure) is the NFFA upgrade for realizing a Full-Spectrum Research Infrastructure for nanoscience and
nanotechnology, capable of enhancing the Italian research competitiveness on the fundamental interactions of multi-atomic matter to
explore the origins of materials behaviour. The rationale of NFFA-DI is to integrate...
In order to fulfill the interoperability requirement for FAIR research data, (meta)data need to comply with a community-agreed-upon language.
In the NOMAD Archive, materials science data are collected from heterogeneous sources, spanning synthesis, experimental characterization, and computations for modelling and analysis. This diversity necessitates flexible storage options, allowing users...
NOMAD [nomad-lab.eu] [1] is an open-source, community-driven data infrastructure, focusing on materials science data. Originally built as a repository for data from DFT calculations, the NOMAD software can automatically extract data from the output of over 60 simulation codes. Over the past 2 years, NOMAD’s functionalities have been extensively expanded to support advanced many-body...
When gathering your research data and creating a knowledge graph, two aspects are key for achieving high data quality: making your data globally understandable and meaningful by semantic enrichment, and ensuring local conformance and completeness of your data by running validations. There are widely used languages within the Resource Description Framework (RDF) ecosystem to support these...
An extensible open-source platform to support digitalization in materials science is proposed. The platform provides a modular framework for flexible web-based implementation of research data management strategies at scales ranging from a single laboratory to international collaborative projects involving multiple organizations.
The platform natively supports object types related to...
Developing new materials requires extensive experimentation in synthesis and characterization, generating vast data sets. To keep this wealth of knowledge and adhere to the FAIR principles, effective data management is essential, involving standardized metadata schemas and integrated analysis tools. NOMAD has recently incorporated structured metadata schemas to manage experimental data from...
Research data management i(RDM) has been receiving much attention, being in the focus of many institutes often upon pressure from funding agencies and by thriving for good scientific practice.
Several solutions are being developed, mostly focusing on central database systems allowing for structured data storage. These solutions allow for classification, access control, publishing of data....
Material databases contain vast amounts of information, often harboring intricate connections and dependencies within material systems, some of which may remain undiscovered. Their structured organization naturally lends itself to the application of machine learning techniques. Through machine learning, we can unlock the tools necessary to discover potentially hidden structure-property...
In materials development, creating new data points is often very costly due to the effort needed for materials synthesis, sample preparation and characterization. Therefore, all available knowledge in terms of data, physical models and expert knowledge should be exploited in the most efficient way (optimal knowledge exploitation). Moreover, the number of new samples/data points to be...
X-ray absorption spectroscopy (XAS) is one of the characterisation techniques which can be employed to probe electronic structure as well as local structure of functional materials. XAS data analysis involves comparison with theoretical or experimental references and processing of the data includes steps, i.e., calibration, background subtraction, normalization etc. Thus, for the extraction of...
Abstract:
With advancements in the sensitivity of present synchrotron facilities and the refinement of analytical methods, X-ray based techniques have become a standard approach for the structural characterization of intricate solid material systems. X-ray absorption spectroscopy (XAS) stands out as one of the most effective methodologies utilized for the analysis of various...
Automating instrumentation is a big challenge for any lab. In established labs, there is often large amount of existing infrastructure, with the benefits of automation only tangible after several components of a set-up are automated. In smaller labs, automation is often hampered by lack of personnel and know-how.
Here, we present tomato, an open-source, python-based, cross-platform...
The advent of data-driven approaches in materials science requires the aggregation of heterogeneous data from various sources, including simulation and experiments, which span different length scales and encompass a wide range of compositions, structures and thermodynamic conditions. In materials design, a major challenge arises from the combination of different software and file formats,...
State-of-the-art Bayesian optimization algorithms have the shortcoming of relying on a rather fixed experimental workflow. The possibility of making on-the-fly decisions about changes in the planned sequence of experiments is usually excluded and the models often do not take advantage of known structure in the problem or of information given by intermediate proxy measurements [1-3]. We...
Nanophotonic structures that enhance light-matter interaction can increase the sensitivity of spectroscopic optical measurements, such as detection and enantiomer discrimination of chiral molecules. However, this improved sensitivity comes at the cost of complicated modification of the spectra, and it is necessary to account for this during the experiment and in data analysis. This calls for...
In the rapidly evolving field of materials science, the shift towards data-centric research needs enhanced strategies for data management, sharing, and publication. This presentation introduces NOMAD (https://nomad-lab.eu), a web-based platform developed by the NFDI consortium FAIRmat. Designed to address these challenges, NOMAD pioneers the application of FAIR principles (Findable,...
Data Science (DS) is a multidisciplinary field combining different aspects of mathematics, statistics, computer science, and domain-specific knowledge to extract meaningful insights from diverse data sources. DS and AI involve various artifacts, e.g., datasets, models, ontologies, code repositories, execution platforms, repositories, etc. The NFDI4DataScience (NFDI4DS) project endeavors to...
Self-assembling peptides (SAPs) are a type of biomaterial consisting of short aminoacid sequences that can be controlled under specific physicochemical conditions. SAPs form nanostructures that can mimic biological scaffolds giving them numerous applications such as in drug delivery, tissue engineering, biosensors, etc.
In this project we will create new SAP sequences based on desired...
Many phenomena and functional devices in optics and photonics rely on discrete objects, called scatterers, that interact with light in a predefined way. The optical properties of these scatterers are entirely described by the T-matrix. The T-matrix is computed for a given scatterer from a larger number of solutions to the Maxwell equations. Still, once known, various photonic materials made...
Vapor deposition encompasses a vast array of techniques ranging from chemical vapor deposition (CVD) processes like metal-organic vapor phase epitaxy (MOVPE) to physical vapor deposition (PVD) processes like pulsed laser deposition (PLD). These processes are used within a diverse set of industries to deposit thin films and coatings for everything from television screens to corrosion...
Introduction
The acquisition and storage of experimental data in the field of catalysis according to the FAIR principles (Findable, Accessible, Interoperable, and Reusable) necessitates the automation and digitization of experimental setups. In this work, we present our local solutions, in which we have integrated the concept of Standard Operating Procedures (SOPs) into automation...
The advancement of digitalization in catalysis and other scientific domains is marked by a transition from paper-based documentation to electronic lab notebooks, standardized protocols, and experiment automation. This shift promises enhanced reproducibility, comparability, and overall scientific progress. However, at the moment the field of catalysis still lacks universal standards for...
A transition from polluting fossil fuels to cleaner energy sources is underway. However, the intermittent nature of renewables such as solar and wind, dependent on fluctuating environmental conditions, presents a challenge for maintaining a reliable energy supply. Water electrolysis offers a solution by employing excess renewable energy to split water into $\mathrm{H}_2$ and $\mathrm{O}_2$,...
Computational databases are pivotal in modern chemistry, enabling the advanced data-driven exploration of chemical space. Transition metal complexes are a particularly versatile class of molecules due to their tunability of metal center and coordinating ligands, offering broad applications in therapeutics, catalysis and supramolecular chemistry. However, exploring the vast chemical space of...
Atom Probe Tomography (APT) is widely used for nanoscale structure and composition characterization across various disciplines, including materials science, geosciences, and biological sciences. Therefore, it is essential to have standardized workflows for analysis and post-processing that can combine software tools from different research communities in an interoperable manner. We demonstrate...
Aiming at data-driven design of magnetic materials as a demonstration of using NOMAD to integrate automated workflows, metadata formulation, and machine learning, we elucidate how research data management can be implemented for first-principles calculations on magnetic materials. On the one hand, we have established workflows to perform high-throughput calculations on the intrinsic magnetic...
Structured data, in which properties of materials, systems, or devices, are tabulated in a systematic way is a foundation for the methodical optimization and design of novel materials or devices. One of the most widely known databases in materials science is the metal-halide perovskite solar cells database. While this database found widespread use it is difficult to update and extend as it has...
Inorganic halide perovskites are promising for optoelectronic applications, offering greater thermal stability over hybrid counterparts but are prone to phase instabilities. Phase stability can be improved by compositional engineering, e.g., varying the Cs/Pb and I/Br ratio. Combinatorial vacuum coevaporation allows the investigation of the large compositional space of Cs(Sn,Pb)(I,Br)3 in the...
The vast chemical landscape of metal–organic frameworks (MOFs) offers a rich array of compositions, structures, and potential applications.[1] Advancements in Artificial Intelligence (AI) and computer-assisted techniques have not only enhanced MOF discovery but also the field of MOF synthesis.[2] In this presentation, I will offer an experimentalist’s perspective on integrating AI into MOF...
Integrating artificial intelligence (AI) with metal-organic frameworks (MOFs) and highly versatile and structurally diverse materials heralds a new era in material science, offering groundbreaking solutions to longstanding challenges in engineering and data analytics. MOFs, known for their exceptional porosity and customizable frameworks, have shown promising applications across various...
Hydride materials that can reversibly abs-/desorb hydrogen have been intensively investigated due to their potential as a hydrogen storage medium and functional materials for many applications. The dynamic reaction between hydride/hydride-forming material and the gaseous phase is complex and has several intermediary processes, making the insightful description of these phenomena impractical,...
ALBA synchrotron has pledged to follow the FAIR data management principles by which results produced by academic users will be available to the public. One key step of this commitment is to standardize the process by which data are stored. At ALBA this process is being made by rigorously following NeXusFormat application definitions that determine which metadata are essential to replicate the...
Surprisingly, despite the rapid progress of machine learning in materials science, the prediction of optical spectra for crystalline materials remains underexplored, although this gap presents an opportunity to discover novel or tailored materials for various optical applications, including photovoltaic systems, photocatalytic water splitting, epsilon-near-zero materials, optical sensors, and...
In an era of rapid technological advancement and data proliferation, the ability to efficiently access and utilise scientific knowledge has become paramount. Here, we present the development of an advanced research assistant chatbot specifically designed to navigate and interpret scientific publications. Our approach uses the Retrieval-Augmented Generation (RAG) architecture, a...
A lot of materials knowledge is obtained in an indirect manner, e.g. by fitting model parameters to data that is being acquired in some potentially very complex experiment. Electron microscopy data, for example, can be several 10s of GB; and especially for these very large sets of data, complex data analysis workflows (DAWs) must then be run, for extracting the materials property information...