Bayesian optimization framework for data-driven materials design

The improvement of experimental design and the optimization of materials’properties with complex and partially unknown behaviors are common problems in material science. In the context of aqueous foams, the microstructure has a major influence on the properties of the resulting foam. Multiple interlinked parameters yield a large design space that requires tuning to tailor the microstructure evolution and resulting physical qualities. Our goal is a data-driven framework that uses machine learning to guide both experiments and simulations in an autonomous closed-loop. This iterative approach presents a valuable opportunity to accelerate materials development processes. A design of experiments methodology utilizing Bayesian Optimization is used to efficiently explore and exploit the search space, while minimizing the number of required evaluations. This approach allows to select the next most informative evaluation to perform, autonomously and adaptively learning from the already acquired data. The designed workflow is implemented into the data platform Kadi4Mat1, which provides the possibility of storing heterogeneous provenance data, along with a common workspace to integrate analysis methods and visualization. Our contribution within Kadi4Mat strongly relies on the reuse of data, and it is an example of the close interoperability between experimental and simulation research that the platform supports, in full alignment with the FAIR principles. Acknowledgements: This work is funded by the Ministry of Science, Research and Art Baden-Württemberg (MWK-BW) in the project MoMaF–Science Data Center, with funds from the state digitization strategy digital@bw (project number 57)

Impaired Recruitment of Grk6 and β-Arrestin2 Causes Delayed Internalization and Desensitization of a WHIM Syndrome-Associated CXCR4 Mutant Receptor

WHIM (warts, hypogammaglobulinemia, infections, and myelokatexis) syndrome is a rare immunodeficiency syndrome linked to heterozygous mutations of the chemokine receptor CXCR4 resulting in truncations of its cytoplasmic tail. Leukocytes from patients with WHIM syndrome display impaired CXCR4 internalization and enhanced chemotaxis in response to its unique ligand SDF-1/CXCL12, which likely contribute to the clinical manifestations. Here, we investigated the biochemical mechanisms underlying CXCR4 deficiency in WHIM syndrome. We report that after ligand activation, WHIM-associated mutant CXCR4 receptors lacking the carboxy-terminal 19 residues internalize and activate Erk 1/2 slower than wild-type (WT) receptors, while utilizing the same trafficking endocytic pathway. Recruitment of β-Arrestin 2, but not β-Arrestin 1, to the active WHIM-mutant receptor is delayed compared to the WT CXCR4 receptor. In addition, while both kinases Grk3 and Grk6 bind to WT CXCR4 and are critical to its trafficking to the lysosomes, Grk6 fails to associate with the WHIM-mutant receptor whereas Grk3 associates normally. Since β-Arrestins and Grks play critical roles in phosphorylation and internalization of agonist-activated G protein-coupled receptors, these results provide a molecular basis for CXCR4 dysfunction in WHIM syndrome

Characterization of porous membranes using artificial neural networks

Porous membranes have been utilized intensively in a wide range of fields due to their special characteristics and a rigorous characterization of their microstructures is crucial for understanding their properties and improving the performance for target applications. A promising method for the quantitative analysis of porous structures leverages the physics-based generation of porous structures at the pore scale, which can be validated against real experimental microstructures, followed by building the process–structure–property relationships with data-driven algorithms such as artificial neural networks. In this study, a Variational AutoEncoder (VAE) neural network model is used to characterize the 3D structural information of porous materials and to represent them with low-dimensional latent variables, which further model the structure–property relationship and solve the inverse problem of process–structure linkage combined with the Bayesian optimization method. Our methods provide a quantitative way to learn structural descriptors in an unsupervised manner which can characterize porous microstructures robustly

Kadi4Mat : A Research Data Infrastructure for Materials Science

The concepts and current developments of a research data infrastructure for materials science are presented, extending and combining the features of an electronic lab notebook and a repository. The objective of this infrastructure is to incorporate the possibility of structured data storage and data exchange with documented and reproducible data analysis and visualization, which finally leads to the publication of the data. This way, researchers can be supported throughout the entire research process. The software is being developed as a web-based and desktop-based system, offering both a graphical user interface and a programmatic interface. The focus of the development is on the integration of technologies and systems based on both established as well as new concepts. Due to the heterogeneous nature of materials science data, the current features are kept mostly generic, and the structuring of the data is largely left to the users. As a result, an extension of the research data infrastructure to other disciplines is possible in the future. The source code of the project is publicly available under a permissive Apache 2.0 license

An interdisciplinary approach to data management

Many scientific issues involve interdisciplinary approaches that demand scientists with diverse skills and research fields. For the design and fabrication of new materials, this is especially true since new materials with macroscopically observable properties must be proposed based on changes at the molecular level. Research projects of this kind pose particular challenges for efficient execution and documentation, as research data management (RDM) tools usually fit very well to a specific research area, but cannot provide solutions for interdisciplinary topics. In order to guarantee consistent research and its documentation across disciplines, different tools, which may be used in several groups, must be used cooperatively. In the context of the Science Data Center MoMaF, among other things, strategies are being developed to enable research data management across scales. The RDM tools used for this are Chemotion and Kadi4Mat. The systems cover research at the molecular level (chemotion ELN) as well as simulation activities on the meso- and macroscopic scale (Kadi4Mat), and will be extended within the Science Data Center to enable cooperative use of the systems for work across scales. A first use case shows how Chemotion ELN can be used to document necessary parameters at the molecular level, in order to then be able to manage simulations of phase separation processes on their basis in a further step with the help of Kadi4Mat. For this purpose, the procedure and documentation method of already completed projects were first analysed in order to be able to propose a concept for future processes. Chemotion ELN is used in the presented procedure to document molecular descriptions, the performance of polymerization reactions and their outcome, as well as the properties obtained experimentally and from the literature. Kadi4Mat manages and transfers the parameters from the molecular description as input for mesoscopic simulations that describe the phase separation process in a time-dependent manner. Finally, by applying analysis tools on the time-dependent data via Kadi4Mat, macroscopic properties can be derived across scales as a function of the molecular composition