In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. We show that these metrics correlate well with the RMSD between two configurations if this RMSD is obtained from a global minimization over all translations, rotations and permutations of atomic indices. We introduce a Monte Carlo approach to obtain this global minimum of the RMSD between configurations.

Halogen bonding is a noncovalent interaction where an electrophilic cap on a halogen atom, the so-called σ-hole, attracts a nucleophilic site on an adjacent molecule. The polarizability of halogens relates to the strength of the σ-hole, and accordingly the halogen-halogen distance becomes shorter in the order of Cl, Br, and I. Fully fluoro-substituted aromatic molecules, on the contrary, are generally believed not to form halogen bonds due to the absence of a σ-hole. Here, we study atomic-scale in-plane F-F contacts with high-resolution force microscopy. Our ab initio calculations show that the attractive dispersion forces can overcome the electrostatic repulsion between the fluorine atoms, while the anisotropic distribution of the negative electrostatic potential leads the directional bond and even changes the gap. The coexistence of these two competing forces results in the formation of a "windmill" structure, containing three C-F···F bonds among neighboring molecules. While the σ-hole is absent, the scheme of the C-F···F bonding has a high similarity to halogen bonding.

Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings. C 2016 AIP Publishing LLC. [http://dx

60439By adding a non-linear core correction to the well established Dual Space Gaussian type pseudopotentials for the chemical elements up to the third period, we construct improved pseudopotentials for the Perdew Burke Ernzerhof (PBE) [J. Perdew, K. Burke, and M. Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996)] functional and demonstrate that they exhibit excellent accuracy. Our benchmarks for the G2-1 test set show average atomization energy errors of only half a kcal/mol. The pseudopotentials also remain highly reliable for high pressure phases of crystalline solids. When supplemented by empirical dispersion corrections [S. Grimme, J. Comput. Chem. 27, 1787 (2006); S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, J. Chem. Phys. 132, 154104 (2010)] the average error in the interaction energy between molecules is also about half a kcal/mol. The accuracy that can be obtained by these pseudopotentials in combination with a systematic basis set is well superior to the accuracy that can be obtained by commonly used medium size Gaussian basis sets in all-electron calculations.