This project uses artificial neural networks in an inverse design problem of finding nano-structured materials with optical properties on demand. Achieving this goal requires generating large amounts of data from 3D simulations of Maxwell's equations, which makes this a data-intensive computing problem. Tailored algorithms are being developed that address both the learning process and the efficient inversion. The project complements research in the SDL Materials Science on AI methods, large data sets generated by simulations, and workflows.
Nano-optics deals with the optical properties of structures that are comparable to or smaller than the wavelength. All optical properties of a scatterer are determined by its T-matrix. Currently, these T-matrices are recalculated over and over again and are not used systematically. This wastes computing resources and does not allow novel questions to be addressed. DAPHONA remedies this deficiency. The project provides technologies with which the geometric and material properties of an object and its optical properties are brought together in a data structure. This data is systematically used to extract the T-matrix for a given object. It should also be possible to identify objects with predefined optical properties. Using these approaches, the DAPHONA project will answer novel questions that can only be addressed using this data-driven approach. The aim of the project is also to train young scientists at various qualification levels and to anchor the described approach in teaching. In addition, the data structure is to be coordinated within the specialist community. The data will be discussed in workshops and available methods for its use will be disseminated. The DAPHONA concept is open, based on the FAIR principles and will bring sustainable benefits to the entire community.
The NFDI-MatWerk consortium receives a five-year grant within the framework of the National Research Data Infrastructure (NFDI) for the development of a joint materials research data space. NFDI-MatWerk stands for Materials Science and Engineering to characterize the physical mechanisms in materials and develop resource-efficient high-performance materials with the most ideal properties for the respective application. Data from scientific groups distributed across Germany are to be able to be addressed via a knowledge-graph-based infrastructure in such a way that fast and complex search queries and evaluations become possible. At KIT, the Scientific Computing Center (SCC) and the Institute for Applied Materials (IAM) are involved. In the SCC, we will establish the Digital Materials Environment with the infrastructure services for the research data and their metadata together with the partners.
NEP provides important resources for nanoscientific research and develop new cooperative working methods. The use of innovative research data and metadata management technologies is becoming increasingly important. In the NEP project, the SCC contributes with new methods for metadata enrichment, development of large data collections, and the provision of virtual services to the establishment of a joint research data infrastructure.
Within the Joint Lab VMD, the SDL Materials Science develops methods, tools and architectural concepts for supercomputing and big data infrastructures, which are tailored to tackle the specific application challenges and to facilitate the digitalization in materials research and the creation of digital twins. In particular, the Joint Lab develops a virtual research environment (VRE) that integrates computing and data storage resources in existing workflow managements systems and interactive environments for simulation and data analyses.
Within the framework of the Joint Lab "Integrated Model and Data Driven Materials Characterization" (MDMC), the SDL Materials Science is developing a concept for a data and information platform to make data on materials available in a knowledge-oriented way as an experimental basis for digital twins and for the development of simulation-based methods for predicting material structure and properties. It defines a metadata model to describe samples and datasets from experimental measurements and harmonizes data models for material simulation and correlative characterization using materials science vocabularies and ontologies.
NFDI4Ing is a consortium of engineering sciences and promotes the management of technical research data. NFDI4Ing was founded back in 2017 and is in close exchange with researchers from all engineering disciplines. The consortium offers a unique method-oriented and user-centered approach to make technical research data FAIR - discoverable, accessible, interoperable, and reusable. An important challenge here is the large number of sub-disciplines in engineering and their subject-specific peculiarities. KIT is involved with a co-spokesperson, Britta Nestler from the Institute for Applied Materials (IAM) and a co-spokesperson, Achim Streit from the Scientific Computing Center (SCC). As part of NFDI4Ing, the SCC is developing and implementing the concepts for federated research data infrastructures, data management processes, repositories and metadata management in close cooperation with the partners. The NFDI4Ing application https://doi.org/10.5281/zenodo.4015200 describes the planned research data infrastructure in detail. Translated with www.DeepL.com/Translator (free version)
The vision of NFDI4Chem is the digitization of all work processes in chemical research. To this end, infrastructure is to be established and expanded to support researchers in collecting, storing and archiving, processing and analyzing research data, as well as publishing the data in repositories together with descriptive metadata and DOIs, thus making them referencable and reusable. As a professional consortium, NFDI4Chem represents all disciplines of chemistry and works closely with the major professional societies to this end. Translated with www.DeepL.com/Translator (free version)
In the Research Training Group (RTG) "Tailored Scale-Bridging Approaches to Computational Nanoscience" we investigate problems, that are not tractable by computational chemistry standard tools. The research is organized in seven projects. Five projects address scientific challenges such as friction, materials aging, material design and biological function. In two further projects, new methods and tools in mathematics and computer science are developed and provided for the special requirements of these applications. The SCC is involved in projects P4. P5 and P6.
Together with partners at Forschungszentrum Jülich and Fritz Haber Institute Berlin, our goal is to develop a novel intelligent management system for electric batteries that can make better decisions about battery charging cycles based on a detailed surrogate model ("digital twin") of the battery and artificial intelligence.
