Development and application of force fields for molecular simulations

Simulationen weicher Materie umfassen ein breites Spektrum von Anwendungen, wie z. B. die Modellierung von Biomolekülen, Polymeren und Materialien für die organische Elektronik. Um die Längen- und Zeitskalen relevanter Phänomene zu erreichen, werden die Wechselwirkungen in diesen Systemen üblicherweise durch recheneffiziente analytische Kraftfelder berechnet. Ein Teil dieser Arbeit beschreibt eine Beispielanwendung für die kraftfeldbasierte Modellierung von amorphen organischen Halbleitern. Der konventionelle Kraftfeldansatz führt jedoch Parameter ein, die aus für das betrachtete Molekül geeigneten Parametersätzen zugewiesen werden müssen. Vor allem aufgrund der einfachen Funktionsausdrücke für die nicht-kovalenten Wechselwirkungen erfordert das Verfahren zur Bestimmung dieser Parametersätze empirische Zielwerte, die nicht immer verfügbar sind. Bottom-up-Ansätze, wie z. B. Bottom-up-Kraftfelder mit festen Funktionsausdrücken oder Potentiale basierend auf neuronalen Netzen, zielen darauf ab, die experimentellen Daten durch Ergebnisse aus ab initio Rechnungen zu ersetzen. Für die Anwendung in umfangreichen Molekulardynamiksimulationen weisen diese Methoden noch offene Herausforderungen auf. Feste Funktionsausdrücke leiden unter einer begrenzten Flexibilität, die ab initio Potentialenergieoberfläche zu reproduzieren und erfordern manuelle Typdefinitionen, um die Anzahl der Parameter zu reduzieren. Potentiale, die auf neuronalen Netzen basieren, verbessern beide Aspekte, aber ihre hohen Rechenanforderungen begrenzen die zugänglichen Längen- und Zeitskalen. In dieser Arbeit wird ein neuartiger Bottom-up-Ansatz zur Modellierung nicht-kovalenter Wechselwirkungen vorgestellt, der für großskalige Simulationen konzipiert ist. Das Konzept effizienter additiver Wechselwirkungen wird mit der Flexibilität künstlicher neuronaler Netze für die Interpolation verschiedener chemischer Zusammensetzungen und geometrischer Anordnungen kombiniert. Die Anwendung des Modells wird in Molekulardynamiksimulationen demonstriert, und der Vergleich der berechneten thermodynamischen Eigenschaften mehrerer kleiner organischer Moleküle mit experimentellen Daten und konventionellen Kraftfeldern zeigt eine vielversprechende Vorhersageleistung. Zusätzlich bewahrt das Modell die Energiezerlegung in physikalisch motivierte Komponenten, die von der symmetrieangepassten Störungstheorie, die für die ab initio Referenzrechnungen verwendet wird, bereitgestellt wird. Diese Trennbarkeit und die Unabhängigkeit von empirischen Daten machen dieses Modell potenziell nützlich für zukünftige Materialdesign-Anwendungen

The intake of wooden debris in running waters - a method for protecting fish stocks against predation by cormorants?

In sechs aufeinander folgenden Jahren wurde der Bestand an Bachforellen in einem naturnahen Zufluss der Donau dokumentiert. Nach dem ersten Jahr der Untersuchung wurden ufernahe Bäume gefällt und ins Gewässer gebracht, um den Fischbestand vor der vorhandenen Prädation durch Kormorane zu schützen. Diese strukturelle Aufwertung hatte jedoch keine positive Auswirkung auf den Bachforellenbestand. Eine Bestandszunahme wurde nur nach dem Besatz mit juvenilen, gezüchteten Bachforellen festgestellt. Allerdings war diese Bestandszunahme nur kurzfristig messbar, denn nach nur einem Winter wurde wiederum die vorherige niedrige Bestandsgröße bestimmt. Als Hauptgrund für diese niedrige Fischdichte wird der hohe Fraßdruck durch Kormorane während der Wintermonate angesehen. Daher erscheint ein Totholzeintrag, auch wenn diese Maßnahme durch Fischbesatz begleitet wird, nicht dafür geeignet, Fischbeständen nachhaltig vor der Prädation durch Kormorane zu schützen.The brown trout Salmo trutta stock in a small near-natural tributary of the river Danube was monitored during six consecutive years. To protect the local fish community against existing predation by cormorants, after the first year of the study riparian trees were cut down and placed into the river. However, this structural intake had no positive effect for the brown trout stock. An increase of the density was only measured after stocking hatchery-reared young brown trout. But this increase holds only for a short time, after one winter the former small trout abundance was measured again. The main reason for this low density seems to be the high predation rate by cormorants during wintertime. Therefore, the intake of wooden debris, even if it is supported by stocking, seems not suitable to guard fish stocks sustainable against predation by cormorants

Knowledge Graph Building Blocks: An easy-to-use Framework for developing FAIREr Knowledge Graphs

Knowledge graphs and ontologies provide promising technical solutions for implementing the FAIR Principles for Findable, Accessible, Interoperable, and Reusable data and metadata. However, they also come with their own challenges. Nine such challenges are discussed and associated with the criterion of cognitive interoperability and specific FAIREr principles (FAIR + Explorability raised) that they fail to meet. We introduce an easy-to-use, open source knowledge graph framework that is based on knowledge graph building blocks (KGBBs). KGBBs are small information modules for knowledge-processing, each based on a specific type of semantic unit. By interrelating several KGBBs, one can specify a KGBB-driven FAIREr knowledge graph. Besides implementing semantic units, the KGBB Framework clearly distinguishes and decouples an internal in-memory data model from data storage, data display, and data access/export models. We argue that this decoupling is essential for solving many problems of knowledge management systems. We discuss the architecture of the KGBB Framework as we envision it, comprising (i) an openly accessible KGBB-Repository for different types of KGBBs, (ii) a KGBB-Engine for managing and operating FAIREr knowledge graphs (including automatic provenance tracking, editing changelog, and versioning of semantic units); (iii) a repository for KGBB-Functions; (iv) a low-code KGBB-Editor with which domain experts can create new KGBBs and specify their own FAIREr knowledge graph without having to think about semantic modelling. We conclude with discussing the nine challenges and how the KGBB Framework provides solutions for the issues they raise. While most of what we discuss here is entirely conceptual, we can point to two prototypes that demonstrate the principle feasibility of using semantic units and KGBBs to manage and structure knowledge graphs

Height Change Feature Based Free Space Detection

In the context of autonomous forklifts, ensuring non-collision during travel, pick, and place operations is crucial. To accomplish this, the forklift must be able to detect and locate areas of free space and potential obstacles in its environment. However, this is particularly challenging in highly dynamic environments, such as factory sites and production halls, due to numerous industrial trucks and workers moving throughout the area. In this paper, we present a novel method for free space detection, which consists of the following steps. We introduce a novel technique for surface normal estimation relying on spherical projected LiDAR data. Subsequently, we employ the estimated surface normals to detect free space. The presented method is a heuristic approach that does not require labeling and can ensure real-time application due to high processing speed. The effectiveness of the proposed method is demonstrated through its application to a real-world dataset obtained on a factory site both indoors and outdoors, and its evaluation on the Semantic KITTI dataset [2]. We achieved a mean Intersection over Union (mIoU) score of 50.90 % on the benchmark dataset, with a processing speed of 105 Hz. In addition, we evaluated our approach on our factory site dataset. Our method achieved a mIoU score of 63.30 % at 54 H

Analyzing dynamical disorder for charge transport in organic semiconductors via machine learning

Organic semiconductors are indispensable for today's display technologies in form of organic light emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as inorganic semiconductors, limiting the efficiency of devices. To find or even design new organic semiconductors with higher charge carrier mobility, computational approaches, in particular multiscale models, are becoming increasingly important. However, such models are computationally very costly, especially when large systems and long time scales are required, which is the case to compute static and dynamic energy disorder, i.e. dominant factor to determine charge transport. Here we overcome this drawback by integrating machine learning models into multiscale simulations. This allows us to obtain unprecedented insight into relevant microscopic materials properties, in particular static and dynamic disorder contributions for a series of application-relevant molecules. We find that static disorder and thus the distribution of shallow traps is highly asymmetrical for many materials, impacting widely considered Gaussian disorder models. We furthermore analyse characteristic energy level fluctuation times and compare them to typical hopping rates to evaluate the importance of dynamic disorder for charge transport. We hope that our findings will significantly improve the accuracy of computational methods used to predict application relevant materials properties of organic semiconductors, and thus make these methods applicable for virtual materials design

Analyzing Dynamical Disorder for Charge Transport in Organic Semiconductors via Machine Learning

Organic semiconductors are indispensable for today’s display technologies in the form of organic light-emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as inorganic semiconductors, limiting the efficiency of devices. To find or even design new organic semiconductors with higher charge carrier mobility, computational approaches, in particular multiscale models, are becoming increasingly important. However, such models are computationally very costly, especially when large systems and long timescales are required, which is the case to compute static and dynamic energy disorder, i.e., the dominant factor to determine charge transport. Here, we overcome this drawback by integrating machine learning models into multiscale simulations. This allows us to obtain unprecedented insight into relevant microscopic materials properties, in particular static and dynamic disorder contributions for a series of application-relevant molecules. We find that static disorder and thus the distribution of shallow traps are highly asymmetrical for many materials, impacting widely considered Gaussian disorder models. We furthermore analyze characteristic energy level fluctuation times and compare them to typical hopping rates to evaluate the importance of dynamic disorder for charge transport. We hope that our findings will significantly improve the accuracy of computational methods used to predict application-relevant materials properties of organic semiconductors and thus make these methods applicable for virtual materials design

Dynamic Virtualized Deployment of Particle Physics Environments on a High Performance Computing Cluster

The NEMO High Performance Computing Cluster at the University of Freiburg has been made available to researchers of the ATLAS and CMS experiments. Users access the cluster from external machines connected to the World-wide LHC Computing Grid (WLCG). This paper describes how the full software environment of the WLCG is provided in a virtual machine image. The interplay between the schedulers for NEMO and for the external clusters is coordinated through the ROCED service. A cloud computing infrastructure is deployed at NEMO to orchestrate the simultaneous usage by bare metal and virtualized jobs. Through the setup, resources are provided to users in a transparent, automatized, and on-demand way. The performance of the virtualized environment has been evaluated for particle physics applications