A quantum embedding theory in the screened Coulomb interaction: Combining configuration interaction with GW/BSE

We present a new quantum embedding theory called dynamical configuration interaction (DCI) that combines wave function and Green's function theories. DCI captures static correlation in a correlated subspace with configuration interaction and couples to high-energy, dynamic correlation outside the subspace with many-body perturbation theory based on Green's functions. In the correlated subspace, we use a wave function description to avoid embedding the two-particle vertex, which greatly simplifies the frequency structure of the embedding. DCI takes the strengths of both theories to balance static and dynamic correlation in a single, fully ab-initio embedding concept. We show that treating high-energy correlation up to the $GW$ and Bethe-Salpeter equation level is sufficient even for challenging multi-reference problems. Our theory treats ground and excited states on equal footing, and we compute the dissociation curve of N$_2$, vertical excitation energies of N$_2$ and C$_2$, and the ionization spectrum of benzene in excellent agreement with high level quantum chemistry methods and experiment

Tunable High-Field/ High-Frequency ESR and High-Field Magnetization on Single-Molecule Clusters

In this work, low dimensional iron group clusters have been studied by application of high magnetic fields. The magnetization has been probed with an MPMS as function of temperature and field. The combination with pulse field measurements up to 52\,T allowed determination of the magnetic exchange coupling parameters, and to probing the effective spin of the ground state. The main focus was on tunable high-field/high-frequency (tHF) ESR in static fields < 17 T and pulse field ESR up to 36 T. This magnetic resonance method has been used for the characterization of the local magnetic properties: The detailed analysis of the field dependence of dedicated spin states allowed to determine the magnetic anisotropy and g-factors. The results were analyzed in the framework of the appropriate effective spin Hamiltonians in terms of magnetization fits and ESR spectrum simulations

Tunable High-Field/ High-Frequency ESR and High-Field Magnetization on Single-Molecule Clusters

In this work, low dimensional iron group clusters have been studied by application of high magnetic fields. The magnetization has been probed with an MPMS as function of temperature and field. The combination with pulse field measurements up to 52\,T allowed determination of the magnetic exchange coupling parameters, and to probing the effective spin of the ground state. The main focus was on tunable high-field/high-frequency (tHF) ESR in static fields < 17 T and pulse field ESR up to 36 T. This magnetic resonance method has been used for the characterization of the local magnetic properties: The detailed analysis of the field dependence of dedicated spin states allowed to determine the magnetic anisotropy and g-factors. The results were analyzed in the framework of the appropriate effective spin Hamiltonians in terms of magnetization fits and ESR spectrum simulations

Fast evaluation of solid harmonic Gaussian integrals for local resolution-of-the-identity methods and range-separated hybrid functionals

An integral scheme for the efficient evaluation of two-center integrals over contracted solid harmonic Gaussian functions is presented. Integral expressions are derived for local operators that depend on the position vector of one of the two Gaussian centers. These expressions are then used to derive the formula for three-index overlap integrals where two of the three Gaussians are located at the same center. The efficient evaluation of the latter is essential for local resolution-of-the-identity techniques that employ an overlap metric. We compare the performance of our integral scheme to the widely used Cartesian Gaussian-based method of Obara and Saika (OS). Non-local interaction potentials such as standard Coulomb, modified Coulomb and Gaussian-type operators, that occur in range-separated hybrid functionals, are also included in the performance tests. The speed-up with respect to the OS scheme is up to three orders of magnitude for both, integrals and their derivatives. In particular, our method is increasingly efficient for large angular momenta and highly contracted basis sets.Comment: 18 pages, 2 figures; accepted manuscript. v2: supplementary material include

Determining user specific semantics of locations extracted from trajectory data

Knowledge about people's daily travel behavior is very relevant for transportation planning, but also for urban and regional planning in general. This information is typically collected through questionnaires or surveys. With the increasing availability of mobile devices capable of using Global Navigation Satellite Systems, it is possible to derive individual mobility behavior on a large scale and for a variety of different users. However, the challenge is to derive the relevant information from the mere GNSS trajectories; in this paper, the relevant information is semantic locations such as home, work place or leisure places. This paper presents an approach to first detect and cluster stop points as potential semantic locations of a user, which are then enriched with Points of Interest from OpenStreetMap and additional features, and finally a Viterbi optimization assigns the most probable semantics to these locations. Overall, this approach produces promising results for predicting user location semantics on a generalized level

Traffic Regulator Detection Using GPS Trajectories

This paper explores the idea of enriching maps with features predicted from GPS trajectories. More specifically, it proposes a method of classifying street intersections according to traffic regulators (traffic light, yield/priority-sign and right-of-way rule). Intersections are regulated locations and the observable movement of vehicles is affected by the underlying traffic rules. Movement patterns such as stop events or start-and-stop sequences are commonly observed at those locations due to traffic regulations. In this work, we test the idea of detecting traffic regulators by learning them in a supervised way from features derived from GPS trajectories. We explore and assess different settings of the feature vector being used to train a classifier that categorizes the intersections based on traffic regulators; also, we test several experimental setups. The results show that a Random Forest classifier with oversampling and Bagging booster enabled can predict the intersection regulators with 90.4% accuracy. We discuss future research directions and recommend next steps for improving the results of this research. © 2020, The Author(s)

Accelerating core-level GWGW calculations by combining the contour deformation approach with the analytic continuation of WW

In recent years, the $GW$ method has emerged as a reliable tool for computing core-level binding energies. The contour deformation (CD) technique has been established as an efficient, scalable, and numerically stable approach to compute the $GW$ self-energy for deep core excitations. However, core-level $GW$ calculations with CD face the challenge of higher scaling with respect to system size $N$ compared to the conventional quartic scaling in valence state algorithms. In this work, we present the CD-WAC method (CD with $W$ Analytic Continuation), which reduces the scaling of CD applied to the inner shells from $O(N^5)$ to $O(N^4)$ by employing an analytic continuation of the screened Coulomb interaction $W$. Our proposed method retains the numerical accuracy of CD for the computationally challenging deep core case, yielding mean absolute errors $<5$ meV for well-established benchmark sets, such as CORE65, for single-shot $GW$ calculations. More extensive testing for different $GW$ flavors prove the reliability of the method. We have confirmed the theoretical scaling by performing scaling experiments on large acene chains and amorphous carbon clusters, achieving speedups of up to 10x for structures of only 116 atoms. This improvement in computational efficiency paves the way for more accurate and efficient core-level $GW$ calculations on larger and more complex systems

Trajectory analysis at intersections for traffic rule identification

In this paper, we focus on trajectories at intersections regulated by various regulation types such as traffic lights, priority/yield signs, and right-of-way rules. We test some methods to detect and recognize movement patterns from GPS trajectories, in terms of their geometrical and spatio-temporal components. In particular, we first find out the main paths that vehicles follow at such locations. We then investigate the way that vehicles follow these geometric paths (how do they move along them). For these scopes, machine learning methods are used and the performance of some known methods for trajectory similarity measurement (DTW, Hausdorff, and Fréchet distance) and clustering (Affinity propagation and Agglomerative clustering) are compared based on clustering accuracy. Afterward, the movement behavior observed at six different intersections is analyzed by identifying certain movement patterns in the speed- and time-profiles of trajectories. We show that depending on the regulation type, different movement patterns are observed at intersections. This finding can be useful for intersection categorization according to traffic regulations. The practicality of automatically identifying traffic rules from GPS tracks is the enrichment of modern maps with additional navigation-related information (traffic signs, traffic lights, etc.)

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework

Four-center two-electron Coulomb integrals routinely appear in electronic structure algorithms. The resolution-of-the-identity (RI) is a popular technique to reduce the computational cost for the numerical evaluation of these integrals in localized basis-sets codes. Recently, Duchemin and Blase proposed a separable RI scheme [J. Chem. Phys. 150, 174120 (2019)], which preserves the accuracy of the standard global RI method with the Coulomb metric (RI-V) and permits the formulation of cubic-scaling random phase approximation (RPA) and $GW$ approaches. Here, we present the implementation of a separable RI scheme within an all-electron numeric atom-centered orbital framework. We present comprehensive benchmark results using the Thiel and the GW100 test set. Our benchmarks include atomization energies from Hartree-Fock, second-order M{\o}ller-Plesset (MP2), coupled-cluster singles and doubles, RPA and renormalized second-order perturbation theory as well as quasiparticle energies from $GW$. We found that the separable RI approach reproduces RI-free HF calculations within 9 meV and MP2 calculations within 1 meV. We have confirmed that the separable RI error is independent of the system size by including disordered carbon clusters up to 116 atoms in our benchmarksComment: 16 pages, 8 figure