We present an update on recently developed methodology and functionality in the computer program LOBSTER (Local Orbital Basis Suite Towards Electronic-Structure Reconstruction) for chemical-bonding analysis in periodic systems. LOBSTER is based on an analytic projection from projector-augmented wave (PAW) densityfunctional theory (DFT) computations [J. Comput. Chem. 2013, 34, 2557, reconstructing chemical information in terms of local, auxiliary atomic orbitals and thereby opening the output of PAW-based DFT codes to chemical interpretation. We demonstrate how LOBSTER has been improved by taking into account time reversal symmetry, thereby speeding up the DFT and LOBSTER calculations by a factor of 2.Over the recent years, the functionalities have also been continually expanded, including accurate projected densities of states (DOS), crystal orbital Hamilton population (COHP) analysis, atomic and orbital charges, gross populations, and the recently introduced ᵈ-dependent COHP. The software is offered free-of-charge for non-commercial research. File list (2)download file view on ChemRxiv Manuscript.pdf (3.50 MiB) download file view on ChemRxiv Supporting_Information.pdf (647.70 KiB)

Halogen bonds (XBs) are intriguing noncovalent interactions that are frequently being exploited for crystal engineering. Recently, similar bonding mechanisms have been proposed for adjacent main-group elements, and noncovalent "chalcogen bonds" and "pnictogen bonds" have been identified in crystal structures. A fundamental question, largely unresolved thus far, is how XBs and related contacts interact with each other in crystals; similar to hydrogen bonding, one might expect "cooperativity" (bonds amplifying each other), but evidence has been sparse. Here, we explore the crucial step from gas-phase oligomers to truly infinite chains by means of quantum chemical computations. A periodic density functional theory (DFT) framework allows us to address polymeric chains of molecules avoiding the dreaded "cluster effects" as well as the arbitrariness of defining a "large enough" cluster. We focus on three types of molecular chains that we cut from crystal structures; furthermore, we explore reasonable substitutional variants in silico. We find evidence of cooperativity in chains of halogen cyanides and also in similar chalcogen- and pnictogen-bonded systems; the bonds, in the most extreme cases, are amplified through cooperative effects by 79% (I···N), 90% (Te···N), and 103% (Sb···N). Two experimentally known organic crystals, albeit with similar atomic connectivity and XB characteristics, show signs of cooperativity in one case but not in another. Finally, no cooperativity is observed in alternating halogen/acetone and halogen/1,4-dioxane chains; in fact, these XBs weaken each other by up to 26% compared to the respective gas-phase dimers.

Quaternary tantalum-based oxynitrides ATa(O,N) 3 , with electronic band gaps between 1.8 and 2.4 eV, are promising materials for photochemical water-splitting. The tailoring of their surface properties is a critical aspect to obtain efficient hole extraction. We report on the origin of improved photoelectrochemical (PEC) water oxidation by means of acidic treatment for this class of compounds on the example of cubic CaTaO 2 N particles. We address the effect of acidic treatment by using complementary physical characterization techniques, such as X-ray photoelectron spectroscopy, electrochemical impedance spectroscopy, 1 H and 14 N solid-state nuclear magnetic resonance (NMR) spectroscopy, electron microscopy, and electronic band structure calculations at the density functional theory level. In combination with PEC measurements, solid-state NMR indicates that the restructured surface displays a meaningfully higher concentration of terminating OH groups. Subsequent deposition of a nickel borate (NiB i ) catalyst on the acid-treated surface yields a higher percentual upsurge of photocurrent in comparison to pristine CaTaO 2 N. Our results highlight the application of solid-state NMR spectroscopy for understanding the semiconductor−catalyst interface in photochemical devices.

Molecular compounds, organic and inorganic, crystallize in diverse and complex structures. They continue to inspire synthetic efforts and "crystal engineering", with implications ranging from fundamental questions to pharmaceutical research. The structural complexity of molecular solids is linked with diverse intermolecular interactions: hydrogen bonding with all its facets, halogen bonding, and other secondary bonding mechanisms of recent interest (and debate). Today, high-resolution diffraction experiments allow unprecedented insight into the structures of molecular crystals. Despite their usefulness, however, these experiments also face problems: hydrogen atoms are challenging to locate, and thermal effects may complicate matters. Moreover, even if the structure of a crystal is precisely known, this does not yet reveal the nature and strength of the intermolecular forces that hold it together. In this Account, we show that periodic plane-wave-based density functional theory (DFT) can be a useful, and sometimes unexpected, complement to molecular crystallography. Initially developed in the solid-state physics communities to treat inorganic solids, periodic DFT can be applied to molecular crystals just as well: theoretical structural optimizations "help out" by accurately localizing the elusive hydrogen atoms, reaching neutron-diffraction quality with much less expensive measurement equipment. In addition, phonon computations, again developed by physicists, can quantify the thermal motion of atoms and thus predict anisotropic displacement parameters and ORTEP ellipsoids "from scratch". But the synergy between experiment and theory goes much further than that. Once a structure has been accurately determined, computations give new and detailed insights into the aforementioned intermolecular interactions. For example, it has been debated whether short hydrogen bonds in solids have covalent character, and we have added a new twist to this discussion using an orbital-based theory that once more had been developed for inorganic solids. However, there is more to a crystal structure than a handful of short contacts between neighboring residues. We hence have used dimensionally resolved analyses to dissect crystalline networks in a systematic fashion, one spatial direction at a time. Initially applied to hydrogen bonding, these techniques can be seamlessly extended to halogen, chalcogen, and pnictogen bonding, quantifying bond strength and cooperativity in truly infinite networks. Finally, these methods promise to be useful for (bio)polymers, as we have recently exemplified for α-chitin. At the interface of increasingly accurate and popular DFT methods, ever-improving crystallographic expertise, and new challenging, chemical questions, we believe that combined experimental and theoretical studies of molecular crystals are just beginning to pick up speed.

In chemical crystallography, the thermal motion of scattering centres is commonly described by anisotropic displacement parameters (ADPs). Very recently, it has been shown that ADPs are not only accessible by diffraction experiments but also via theory: this emerging approach seems promising but must be thoroughly tested. In this study, we have performed specifically tailored X-ray diffraction (XRD) experiments in fine steps between 100 and 300 K which allow detailed comparison to ab initio data from dispersion-corrected density functional theory (DFT) combined with periodic lattice-dynamics. The compound chosen for this study, crystalline pentachloropyridine IJC 5 NCl 5 ), is well suited for this purpose: it represents a molecular crystal without H atoms, thus posing no challenge to XRD; its solid-state structure is controlled by dispersion and halogen-bonding interactions; and the ADPs associated with the peripheral Cl atoms show strong temperature dependence. Quality criteria in direct and in reciprocal space prove that ADPs are predicted with high confidence for the temperature range between 100 and 200 K, and that several economic dispersion corrections to DFT can be reliably employed for this purpose. Within the limits we have explored here, the ab initio prediction of ADPs appears to be a facile and complementary tool, especially in those cases where diffraction data cannot provide a straightforward model for thermal motion.7414 | CrystEngComm, 2015, 17, 7414-7422This journal is

The physics of heat conduction puts practical limits on many technological fields such as energy production, storage, and conversion. It is now widely appreciated that the phonon-gas model does not describe the full vibrational spectrum in amorphous materials, since this picture likely breaks down at higher frequencies. A two-channel heat conduction model, which uses harmonic vibrational states and lattice dynamics as a basis, has recently been shown to capture both crystal-like (phonon-gas channel) and amorphous-like (diffuson channel) heat conduction. While materials design principles for the phonon-gas channel are well established, similar understanding and control of the diffuson channel is lacking. In this work, in order to uncover design principles for the diffuson channel, we study structurally-complex crystalline Yb 14 (Mn,Mg)Sb 11 , a champion thermoelectric material above 800 K, experimentally using inelastic neutron scattering and computationally using the two-channel lattice dynamical approach. Our results show that the diffuson channel indeed dominates in Yb14MgSb 11 above 300 K. More importantly, we demonstrate a method for the rational design of amorphous-like heat conduction by considering the energetic proximity phonon modes and modifying them through chemical means. We show that increasing (decreasing) the mass on the Sb-site decreases (increases) the energy of these modes such that there is greater (smaller) overlap with Yb-dominated modes resulting in a higher (lower) thermal conductivity. This design strategy is exactly opposite of what is expected when the phonon-gas channel and/or common analytical models for the diffuson channel are considered, since in both cases an increase in atomic mass commonly leads to a decrease in thermal conductivity. This work demonstrates how two-channel lattice dynamics can not only quantitatively predict the relative importance of the phonon-gas and diffuson channels, but also lead to rational design strategies in materials where the diffuson channel is important. File list (2) download file view on ChemRxiv main.pdf (2.42 MiB) download file view on ChemRxiv SI.pdf (1.83 MiB)This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together. The Interplay of Chemical Heuristics and Machine Learning Data science (see Glossary), artificial intelligence, and machine learning are nowadays present in all fields of science and technology, including chemistry and materials science. The impact of these techniques is expected to be very large, leading to a new path towards scientific discovery (sometimes called the fourth paradigm in science) [1,2]. We will not review here all progress and future directions in the use of machine learning in materials science; we refer interested readers to recent reviews on this topic (e.g., [3-6]). Instead, we offer a personal perspective on how these new techniques, heavily relying on sophisticated algorithms and large data sets, compete, complement, challenge, and/or benefit from more traditional heuristic approaches.

An unexpected polymorph of the highly energetic phase CuN3 has been synthesized and crystallizes in the orthorhombic space group Cmcm with a=3.3635(7), b=10.669(2), c=5.5547(11) Å and V=199.34(7) Å(3). The layered structure resembles graphite with an interlayer distance of 2.777(1) Å (=1/2 c). Within a single layer, considering N3(-) as one structural unit, there are 10-membered almost hexagonal rings with a heterographene-like motif. Copper and nitrogen atoms are covalently bonded with Cu-N bonds lengths of 1.91 and 2.00 Å, and the N3(-) group is linear but with N-N 1.14 and 1.20 Å. Electronic-structure calculations and experimental thermochemistry show that the new polymorph termed β-CuN3 is more stable than the established α-CuN3 phase. Also, β-CuN3 is dynamically, and thus thermochemically, metastable according to the calculated phonon density of states. In addition, β-CuN3 exhibits negative thermal expansion within the graphene-like layer.