Application of the Mixed Time-averaging Semiclassical Initial Value Representation method to Complex Molecular Spectra

The recently introduced mixed time-averaging semiclassical initial value representation molecular dynamics method for spectroscopic calculations [M. Buchholz, F. Grossmann, and M. Ceotto, J. Chem. Phys. 144, 094102 (2016)] is applied to systems with up to 61 dimensions, ruled by a condensed phase Caldeira-Leggett model potential. By calculating the ground state as well as the first few excited states of the system Morse oscillator, changes of both the harmonic frequency and the anharmonicity are determined. The method faithfully reproduces blueshift and redshift effects and the importance of the counter term, as previously suggested by other methods. Differently from previous methods, the present semiclassical method does not take advantage of the specific form of the potential and it can represent a practical tool that opens the route to direct ab initio semiclassical simulation of condensed phase systems.Comment: 11 figure

Semiclassical initial value representation for complex dynamics

Semiclassical initial value representations (SC-IVRs) are popular methods for an approximate description of the quantum dynamics of atomic and molecular systems. A very efficient special case is the propagator by Herman and Kluk, which will be the basis for the investigations in this work. It consists of a phase space integration over initial conditions of classical trajectories which are guiding Gaussian wavepackets. A complex phase factor in the integrand allows for interference between different trajectories, which leads to soft quantum effects being naturally included in the description. The underlying classical trajectories allow for an approximate description of the dynamics of large quantum systems that are inaccessible for a full quantum propagation. Moreover, they also provide an intuitive understanding of quantum phenomena in terms of classical dynamics. The main focus of this work is on further approximations to Herman-Kluk propagation whose applicability to complex dynamics is limited by the number of trajectories that are needed for numerical convergence of the phase space integration. The central idea for these approximations is the semiclassical hybrid formalism which utilizes the costly Herman-Kluk propagator only for a small number of system degrees of freedom (DOFs). The remaining environmental DOFs are treated on the level of Heller's thawed Gaussian wavepacket dynamics, a single trajectory method which is exact only for at most harmonic potentials. If the environmental DOFs are weakly coupled and therefore close to their potential minimum, this level of accuracy is sufficient to account for their effect on the system. Thus, the hybrid approximation efficiently combines accuracy and low numerical cost. As a central theoretical result, we apply this hybrid idea to a time-averaging scheme to arrive at a method for the calculation of vibrational spectra of molecules that is both accurate and efficient. This time-averaged hybrid propagation is then used to study the vibrational dynamics of an iodine-like Morse oscillator bilinearly coupled to a Caldeira-Leggett bath of harmonic oscillators. We first validate the method by comparing it to full quantum and Herman-Kluk propagation for appropriately sized environments. After having established its accuracy, we include more bath DOFs to investigate the influence of the Caldeira-Leggett counter term on the shift of the vibrational levels of the Morse oscillator. As a result, we find out that a redshift, which is observed experimentally for, e.g., iodine in a rare gas matrix, occurs only if the counter term is not included in the Hamiltonian. We then move away from the model bath and on to a realistic, experimentally relevant environment consisting of krypton atoms. We put the iodine molecule into a cluster of 17 krypton atoms and investigate the loss of coherence of the iodine vibration upon coupling to just a few normal coordinates of the bath. These modes with the same symmetry as the iodine vibration turn out to be sufficient to reproduce the expected qualitative dependence on bath temperature and initial state of the iodine molecule. With these few normal modes, a full quantum calculation yields values for coherence loss rates that are close to experimental results. Furthermore, a comparison to semiclassical calculations with more bath modes included confirms the importance of the few highly symmetric normal coordinates. Then, we apply the time-averaged hybrid formalism once more to calculate the vibrational spectrum of the iodine molecule in this now anharmonic krypton environment. Using a krypton matrix instead of a cluster geometry, we find the correct qualitative and also quite good quantitative agreement for the shift of the iodine potential. Finally, we will investigate a more fundamental question, namely, if SC-IVRs contain the spin effects due to the Pauli exclusion principle. To this end, we apply a number of SC-IVRs to the scattering of two electrons with initial states corresponding to either parallel or antiparallel spin. We compare the outcome to full quantum results and find that the difference is resolved by those methods that comprise multiple interfering trajectories

Herman-Kluk propagator is free from zero-point energy leakage

Semiclassical techniques constitute a promising route to approximate quantum dynamics based on classical trajectories starting from a quantum-mechanically correct distribution. One of their main drawbacks is the so-called zero-point energy (ZPE) leakage, that is artificial redistribution of energy from the modes with high frequency and thus high ZPE to that with low frequency and ZPE due to classical equipartition. Here, we show that an elaborate semiclassical formalism based on the Herman-Kluk propagator is free from the ZPE leakage despite utilizing purely classical propagation. This finding opens the road to correct dynamical simulations of systems with a multitude of degrees of freedom that cannot be treated fully quantum-mechanically due to the exponential increase of the numerical effort.Comment: 6 pages 2 figure

Simplified Approach to the Mixed Time-averaging Semiclassical Initial Value Representation for the Calculation of Dense Vibrational Spectra

We present and test an approximate method for the semiclassical calculation of vibrational spectra. The approach is based on the mixed time-averaging semiclassical initial value representation method, which is simplified to a form that contains a filter to remove contributions from approximately harmonic environmental degrees of freedom. This filter comes at no additional numerical cost, and it has no negative effect on the accuracy of peaks from the anharmonic system of interest. The method is successfully tested for a model Hamiltonian, and then applied to the study of the frequency shift of iodine in a krypton matrix. Using a hierarchic model with up to 108 normal modes included in the calculation, we show how the dynamical interaction between iodine and krypton yields results for the lowest excited iodine peaks that reproduce experimental findings to a high degree of accuracy

Deep learning in streamlining the conversion of natural language requirements into template-based architecture

In dieser Arbeit wird evaluiert, wie die kürzlichen Entwicklungen im Bereich des Machine Learning, insbesondere die Entwicklung von Large Language Models dazu genutzt werden kann, bei der Konvertierung von natürlichsprachlichen Anforderungen, in eine vorlagenbasierte Struktur unterstützen können. Es werden verschiedene Ansätze implementiert, getestet und evaluiert, sowie ein existierender Datensatz um Anforderungen in umstrukturierter Form ergänzt. Die untersuchten Ansätze erweisen sich als potenzielle Ansätze für zukünftige Arbeiten und sind in der Lage die Aufgabe im Ansatz zu bewältigen. Daher kann eine auf maschinellem Lernen basierende Umwandlung von Anforderungsstatements den Prozess der Umwandlung vorhandener Statements in eine normierte Struktur verbessern.This thesis evaluates how recent development in Machine Learning, especially Large Language Models could be used to help with the conversion of natural language based requirement statements into a template-based structure. To evaluate different approaches, they have been implemented, tested and evaluated, on an existing dataset, complemented by converted requirement statements. It seems that the tested approaches are feasible to be used and further investigated, as they were already able to provide a decent performance. Therefore, machine learning based conversion of requirement statements can improve the process of converting existing statements into a normed structure

Graph-based Trajectory Prediction with Cooperative Information

For automated driving, predicting the future trajectories of other road users in complex traffic situations is a hard problem. Modern neural networks use the past trajectories of traffic participants as well as map data to gather hints about the possible driver intention and likely maneuvers. With increasing connectivity between cars and other traffic actors, cooperative information is another source of data that can be used as inputs for trajectory prediction algorithms. Connected actors might transmit their intended path or even complete planned trajectories to other actors, which simplifies the prediction problem due to the imposed constraints. In this work, we outline the benefits of using this source of data for trajectory prediction and propose a graph-based neural network architecture that can leverage this additional data. We show that the network performance increases substantially if cooperative data is present. Also, our proposed training scheme improves the network's performance even for cases where no cooperative information is available. We also show that the network can deal with inaccurate cooperative data, which allows it to be used in real automated driving environments.Comment: Accepted for publication at the 26th IEEE International Conference on Intelligent Transportation Systems 202

Fast Long-Term Multi-Scenario Prediction for Maneuver Planning at Unsignalized Intersections

Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a vehicle merges or crosses before another vehicle with the right of way, is often handled implicitly in the prediction. However, an infrastructure-based maneuver planning can assign artificial priorities between cooperative vehicles, so it needs to evaluate many more potential scenarios. Additionally, the prediction horizon has to be long enough to assess the impact of a maneuver. We, therefore, present a novel long-term prediction approach handling the gap acceptance estimation and the velocity prediction in two separate stages. Thereby, the behavior of regular vehicles as well as priority assignments of cooperative vehicles can be considered. We train both stages on real-world traffic observations to achieve realistic prediction results. Our method has a competitive accuracy and is fast enough to predict a multitude of scenarios in a short time, making it suitable to be used in a maneuver planning framework

Genetic tools for the investigation of Roseobacter clade bacteria

<p>Abstract</p> <p>Background</p> <p>The <it>Roseobacter </it>clade represents one of the most abundant, metabolically versatile and ecologically important bacterial groups found in marine habitats. A detailed molecular investigation of the regulatory and metabolic networks of these organisms is currently limited for many strains by missing suitable genetic tools.</p> <p>Results</p> <p>Conjugation and electroporation methods for the efficient and stable genetic transformation of selected <it>Roseobacter </it>clade bacteria including <it>Dinoroseobacter shibae</it>, <it>Oceanibulbus indolifex</it>, <it>Phaeobacter gallaeciensis</it>, <it>Phaeobacter inhibens</it>, <it>Roseobacter denitrificans </it>and <it>Roseobacter litoralis </it>were tested. For this purpose an antibiotic resistance screening was performed and suitable genetic markers were selected. Based on these transformation protocols stably maintained plasmids were identified. A plasmid encoded oxygen-independent fluorescent system was established using the flavin mononucleotide-based fluorescent protein FbFP. Finally, a chromosomal gene knockout strategy was successfully employed for the inactivation of the anaerobic metabolism regulatory gene <it>dnr </it>from <it>D. shibae </it>DFL12^T.</p> <p>Conclusion</p> <p>A genetic toolbox for members of the <it>Roseobacter </it>clade was established. This provides a solid methodical basis for the detailed elucidation of gene regulatory and metabolic networks underlying the ecological success of this group of marine bacteria.</p

Questionable and unquestionable in the perturbation theory of non-Abelian models

We show, by explicit computation, that bare lattice perturbation theory in the two-dimensional O(n) nonlinear $\sigma$ models with superinstanton boundary conditions is divergent in the limit of an infinite number of points $|\Lambda|$. This is the analogue of David's statement that renormalized perturbation theory of these models is infrared divergent in the limit where the physical size of the box tends to infinity. We also give arguments which support the validity of the bare perturbative expansion of short-distance quantities obtained by taking the limit $|\Lambda|\to\infty$ term by term in the theory with more conventional boundary conditions such as Dirichlet, periodic, and free.Comment: One reference added to the published version, 28 pages, 3 figure

StellaTUM: current consensus and discussion on pancreatic stellate cell research

The field of pancreatic stellate cell (PSC) biology is very young, as the essential in-vitro tools to study these cells (ie, methods to isolate and culture PSC) were only developed as recently as in 1998. Nonetheless, there has been an exponential increase in research output in this field over the past decade, with numerous research groups around the world focusing their energies into elucidating the biology and function of these cells. It is now well established that PSC are responsible for producing the stromal reaction (fibrosis) of two major diseases of the pancreas—chronic pancreatitis and pancreatic cancer. Despite exponentially increasing data, the methods for studying PSC remain variable. Although within individual laboratories methods are consistent, different methodologies used by various research groups make it difficult to compare results and conclusions. This article is not a review article on the functions of PSC. Instead, members of the Pancreatic Star Alliance (http://www.pancreaticstaralliance.com) discuss here and consolidate current knowledge, to outline and delineate areas of consensus or otherwise (eg, with regard to methodological approaches) and, more importantly, to identify essential directions for future research