Markus Scheidgen
A5067114196- Machine Learning in Materials Science
- Model-Driven Software Engineering Techniques
- Advanced Materials Characterization Techniques
- Scientific Computing and Data Management
- Service-Oriented Architecture and Web Services
- Advanced Software Engineering Methodologies
- Ion-surface interactions and analysis
- Laser-induced spectroscopy and plasma
- X-ray Diffraction in Crystallography
- Software System Performance and Reliability
- Software Engineering Research
- Electron and X-Ray Spectroscopy Techniques
- Research Data Management Practices
- Inorganic Chemistry and Materials
- Business Process Modeling and Analysis
- Software Testing and Debugging Techniques
- Chalcogenide Semiconductor Thin Films
- Energy Efficient Wireless Sensor Networks
- Wireless Networks and Protocols
- Catalytic Processes in Materials Science
- Semantic Web and Ontologies
- Modeling and Simulation Systems
- Advanced Database Systems and Queries
- Electronic and Structural Properties of Oxides
- Logic, programming, and type systems
Humboldt-Universität zu Berlin
2014-2024
Fritz Haber Institute of the Max Planck Society
2020-2021
Humboldt State University
2006-2012
Abstract In recent years, we have been witnessing a paradigm shift in computational materials science. fact, traditional methods, mostly developed the second half of XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, often more accurate approaches. The new approaches, that collectively label machine learning, their origins fields informatics artificial intelligence, but making rapid inroads all other branches With this mind, Roadmap...
Materials science research is becoming increasingly data-driven, which requires more effort to manage, share, and publish data.NOMAD a web-based application that provides data management for materials data.In addition core functions like uploading sharing files, NOMAD allows entering structured using customizable forms providing the software with electronic laboratory notebook (ELN) functionalities.It automatically extracts rich meta-data from supported file formats, normalizes converts...
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of specification, v1.0, which is already supported by many leading several software packages. illustrate advantages OPTIMADE API through worked examples on each public that support full specification.
The expansive production of data in materials science, their widespread sharing and repurposing requires educated support stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation FAIR-data principles (Findable, Accessible, Interoperable, Reusable) must not be too narrow. Besides, wider materials-science community ought agree on strategies tackle challenges are specific its data, both from computations experiments. paper, we present result...
The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability materials chemical data. Since first release OPTIMADE specification (v1.0), API has undergone significant development, leading v1.2 release, underpinned multiple scientific studies. In this work, we highlight latest features format, accompanying software tools, provide...
Abstract Science is and always has been based on data, but the terms ‘data-centric’ ‘4th paradigm’ of materials research indicate a radical change in how information retrieved, handled performed. It signifies transformative shift towards managing vast data collections, digital repositories, innovative analytics methods. The integration artificial intelligence its subset machine learning, become pivotal addressing all these challenges. This Roadmap Data-Centric Materials explores fundamental...
We develop a materials descriptor based on the electronic density-of-states (DOS) and investigate similarity of it. As an application example, we study Computational 2D Materials Database (C2DB) that hosts thousands two-dimensional with their properties calculated by density-functional theory. Combining our clustering algorithm, identify groups similar structure. introduce additional descriptors to characterize these clusters in terms crystal structures, atomic compositions, configurations...
Abstract Characterization of the electronic band structure solid state materials is routinely performed using photoemission spectroscopy. Recent advancements in short-wavelength light sources and electron detectors give rise to multidimensional spectroscopy, allowing parallel measurements spectral function simultaneously energy, two momentum components additional physical parameters with single-event detection capability. Efficient processing photoelectron event streams at a rate up tens...
Science is and always has been based on data, but the terms ‘data-centric’ ‘4th paradigm’ of materials research indicate a radical change in how information retrieved, handled performed. It signifies transformative shift towards managing vast data collections, digital repositories, innovative analytics methods. The integration Artificial Intelligence (AI) its subset Machine Learning (ML), become pivotal addressing all these challenges. This Roadmap Data-Centric Materials explores fundamental...
If models consist of more and objects, time space required to process these becomes an issue. To solve this we can employ different existing frameworks that use model representations (e.g. trees in XMI or relational data with CDO). Based on the observation reach performance measures for operations characteristics, rise question if how be combined mitigate issues individual representations.
Science is and always has been based on data, but the terms ‘data-centric’ ‘4th paradigm’ of materials research indicate a radical change in how information retrieved, handled performed. It signifies transformative shift towards managing vast data collections, digital repositories, innovative analytics methods. The integration Artificial Intelligence (AI) its subset Machine Learning (ML), become pivotal addressing all these challenges. This Roadmap Data-Centric Materials explores fundamental...
Science is and always has been based on data, but the terms ‘data-centric’ ‘4th paradigm’ of materials research indicate a radical change in how information retrieved, handled performed. It signifies transformative shift towards managing vast data collections, digital repositories, innovative analytics methods. The integration Artificial Intelligence (AI) its subset Machine Learning (ML), become pivotal addressing all these challenges. This Roadmap Data-Centric Materials explores fundamental...
It is hard to experiment with test-beds for communication networks: data produced in the network has be retrieved and analyzed, networks must reconfigured before between experiments, often little structured (log-files) analysis methods tools are generic. Even though many problems of experimentation same all re-use sparse even simple experiments require large efforts. We present a framework that attempts solve these problems: we define set requirements experimenting test-beds, describe...
In this paper, we present the Smart Berlin Testbed as an infrastructure for experimental research on City scenarios. As part of Cities, applications will arise that build upon a variety information sources and provide user with near real-time about surrounding environment. An important role in scenarios be taken by wireless network infrastructures. They function interfaces to users who connect their smartphone or laptop access applications. Additionally, wired sensor networks are required...