scientific big data
Scientific research and consulting for materials informatics
Materials informatics is a junction of materials science and computer science, focused on the data processing and analytics for the new materials. This includes the advanced software development, database-driven simulations, and automated screening of the scientific data.
Tilde Materials Informatics ® is a brand name for the materials informatics services offered by a consultant and independent researcher Dr. Evgeny Blokhin. Under the name of Tilde Materials Informatics ® the following services are provided:
- computational research for perspective materials,
- high-throughput database-driven simulations (e.g. ab initio),
- full-stack software development for the scientific data repositories.
Managing the big amounts of scientific data, sharing them online, automation of computational research — all these topics are the applications of materials informatics. Would you like to learn more? Then please get into contact.
Materials platform for data science
Materials platform for data science (MPDS) is an online version of the well-known commercial PAULING FILE materials database from Switzerland, curated since 1993 (backs up Springer Materials™, ICDD PDF-4™, ASM Alloy Phase Diagrams™, MedeA Materials Design™, Pearson's Crystal Data, AtomWork Advanced, etc.). More than two millions of data entries (phase diagrams, crystalline structures, and physical property values) are available for the download by subscription. The whole dataset is also available in machine-readable developer-friendly format via the REST API. The source of the data is about 500'000 peer-reviewed publications in materials science, processed by an international team of the expert editors. The results are presented online under the quick search interface. The basic access tier is always free.
Perspective materials: simulated database
In this project various materials related to up-to-date commercial applications are considered at the ab initio level. The state-of-the-art theoretical approximations are adopted. To reduce computational costs, only the ideal systems without the defects are modeled. All the results, including raw and intermediate data, and all the detailed software workflows, will be published as the open-access online interactive encyclopedia for educational purposes, allowing reproduction and enhancement. As of now, the demonstration database contains: (a) selected data prepared by Evgeny Blokhin, (b) open-access data prepared by Zhongnan Xu, Jan Rossmeisl, and John Kitchin, and (c) tutorial datasets for simulation packages Quantum ESPRESSO and CRYSTAL. It is also a bibliographic manager. The two following projects are also relevant.
Server-free scientific web-applications for the browser
With the ubiquitous penetration of Internet, the web-browsers became powerful web-applications platform. However a centralized web-server is typically required for them, implying such drawbacks as software complexity, traffic overhead, and privacy issues. These drawbacks are avoided in the web-applications developed. They e.g. visualize crystalline structures in common formats (see also other solutions), identify the space groups, and provide the unified access to the materials APIs. The only browser and no plugins are required. These web-applications are open-source and ready to be used as the parts of more complex software.
Artificial intelligence techniques for materials data mining
In a narrow sense, a special-purpose artificial intelligence is the number of computer science techniques, copying the certain cognitive aspects of the human brain. Among them the deep learning and logic reasoning are of the special interest. They were already applied in materials design and chemoinformatics. The present study aims to review the existing experience and recognize the most promising use cases. Using these two artificial intelligence techniques, this study will offer the open-source solutions for some practical materials science problems, concerned with the big amount of data. As an introduction for the curious reader, the logic reasoning tutorial is available.
Metis: X-ray diffraction data platform
Metis was the collaboration with the X-ray crystallography laboratory of BASF SE (Ludwigshafen am Rhein, Germany). The X-ray diffraction is a powerful non-destructive experimental technique widely used for the characterization of the new materials. Here the focus was put on the data organizing, online graphical user interfaces, and high-throughput cloud simulations. The capacity at the experimental beamlines was considerably increased through the automation of experiments for fast characterisation methods (via automated data analysis). The ultimate goal was to speed up the typical processes in an industrial X-ray diffraction research laboratory. See also more details in the STREAMLINE Horizon 2020 funded project description. In 2023, the project was successfully funded by the BASF SE business incubator Chemovator GmbH under the new name.
NoMaD: Novel Materials Discovery Repository
This project was the collaboration of Fritz Haber Institute of the Max Planck Society and Humboldt University of Berlin (Germany) with the aim to create an international ab initio materials science data repository called NoMaD. The database structure was designed, and the repository program core was implemented. In 2014 the first version of NoMaD was launched. In 2015, NoMaD project was successfully funded by European Union's infrastructure call for Centers of Excellence (CoE) in computational sciences.
Defect thermodynamics of mixed conductors
Water adsorption on perovskite surfaces
The Quantum Chemistry Chair of St. Petersburg State University (Russia) had worked on several international grants by NATO, DFG, INTAS, and CRDF in fundamental research. One of the projects was to combine classical force-field and ab initio modeling techniques to describe water adsorption on the surfaces of SrXO3 perovskites (X = Ti, Zr, Hf), owing to its high technological importance. During this project, the number of utility software tools for data processing were created. For details please refer to Evarestov, Bandura, Blokhin, J. Phys. Conf. Ser. (2007) and Evarestov, Bandura, Blokhin, Surf. Sci. (2008).
Research and consulting
We explore the screening strategies for materials design using the artificial intelligence techniques, such as deep learning and semantic technologies. Let the advanced data analytics serve for materials.
You manage big amounts of scientific data or want to share data online? Or process your data heavily (e.g. with Python)? It is time to invest in your own data laboratory.