We have examined the impact of intermolecular vibrational coupling effects of the O-H stretch modes, as obtained by the surface-specific velocity-velocity correlation function approach, on the simulated sum-frequency generation spectra of the water/air interface. Our study shows that the inclusion of intermolecular coupling effects within the first three water layers, i.e. from the water/air interface up to a distance of 6 Å towards the bulk, is essential to reproduce the experimental SFG spectra. In particular, we find that these intermolecular vibrational contributions to the SFG spectra of the water/air interface are dominated by the coupling between the SFG active interfacial and SFG inactive bulk water molecules. Moreover, we find that most of the intermolecular vibrational contributions to the spectra originate from the coupling between double-donor water molecules only, whereas the remaining contributions originate mainly from the coupling between single-donor and double-donor water molecules.

In this work, we investigated ternary chalcogenide semiconductors to identify promising p-type transparent conducting materials (TCMs). Highthroughput calculations were employed to find the compounds that satisfies our screening criteria. Our screening strategy was based on the size of band gaps, the values of hole effective masses, and p-type dopability. Our search led to the identification of seven promising compounds (IrSbS, Ba 2 GeSe 4 , Ba 2 SiSe 4 , Ba(BSe 3 ) 2 , VCu 3 S 4 , NbCu 3 Se 4 , and CuBS 2 ) as potential TCM candidates. In addition, branch point energy and optical absorption spectra calculations support our findings. Our results open a new direction for the design and development of ptype TCMs.

The accuracy of water models derived from ab initio molecular dynamics simulations by means on an improved force-matching scheme is assessed for various thermodynamic, transport, and structural properties. It is found that although the resulting force-matched water models are typically less accurate than fully empirical force fields in predicting thermodynamic properties, they are nevertheless much more accurate than generally appreciated in reproducing the structure of liquid water and in fact superseding most of the commonly used empirical water models. This development demonstrates the feasibility to routinely parametrize computationally efficient yet predictive potential energy functions based on accurate ab initio molecular dynamics simulations for a large variety of different systems. © 2016 Wiley Periodicals, Inc.

In this work, a high-throughput screening of binary and ternary pnictide- and halide-based compounds is performed to identify promising p-type transparent conductors. Our investigation profits from the emergence of open-access databases based on ab-initio results. The band gap, stability, hole effective mass, and p-type dopability are employed for the materials screening and the validity of these descriptors is discussed. Among the final candidates, BaSiN2 is the most promising compound.

We present the submatrix method, a highly parallelizable method for the approximate calculation of inverse p-th roots of large sparse symmetric matrices which are required in different scientific applications. We follow the idea of Approximate Computing, allowing imprecision in the final result in order to be able to utilize the sparsity of the input matrix and to allow massively parallel execution. For an n × n matrix, the proposed algorithm allows to distribute the calculations over n nodes with only little communication overhead. The approximate result matrix exhibits the same sparsity pattern as the input matrix, allowing for efficient reuse of allocated data structures.We evaluate the algorithm with respect to the error that it introduces into calculated results, as well as its performance and scalability. We demonstrate that the error is relatively limited for well-conditioned matrices and that results are still valuable for errorresilient applications like preconditioning even for ill-conditioned matrices. We discuss the execution time and scaling of the algorithm on a theoretical level and present a distributed implementation of the algorithm using MPI and OpenMP. We demonstrate the scalability of this implementation by running it on a high-performance compute cluster comprised of 1024 CPU cores, showing a speedup of 665× compared to single-threaded execution.