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Eingeladener Vortrag
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Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
The presentations discusses a use case for the optimization of concrete structures where structural and material design are integrated in a computational workflow. The workflow is based on both physics-based and data-based models and experimental data is used to calibrate/train these models with a specific focus on the integration of ucertainties.
Übersicht über die Umsetzung des EU Green New Deal, des Circular Economy Action Plan sowie der Sustainable Product Initiative. Ebenfalls erfolgt eine Zusammenfassung der Auswirkungen der Änderungen im EU-Produktrecht auf die Revision des geltenden Rechts und das Zusammenspiel zwischen Rechtssetzung und Normung.
The variability and heterogeneity in their structural composition is significant for nearly all synthetic and natural polymers. Beside molar mass also functionality type distribution, copolymer composition distribution and architectural features are responsible for changing the material properties.
A wide range of analytical techniques are available to get insight into these parameters. However, in most cases it is essential to combine different sophisticated techniques to get closer to actual structures and to avoid statistical averages.
Different liquid chromatographic separation modes and their coupling in a two-dimensional way (SEC, LCCC, HILIC, 2D-LC) in combination with suitable detection techniques (e.g. UV, RI, LS, IR, MALDI/ESI-MS) as well as techniques like BET, DCS, SEM/TEM were applied to make structural features visible.
A few aspects are discussed for structural different polylactides from various synthesis routes and their behavior in LC-MS. Moreover, some analytical results for technical lignins valorized by mechanochemical treatment or modified by functional polymers are presented.