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Amorphous ice phases are key constituents of water's complex structural landscape. This study investigates the polyamorphic nature of water, focusing on the complexities within low-density amorphous ice (LDA), high-density amorphous ice, and the recently discovered medium-density amorphous ice (MDA). We use rotationally invariant, high-dimensional order parameters to capture a wide spectrum of local symmetries for the characterization of local oxygen environments. We train a neural network to classify these local environments and investigate the distinctiveness of MDA within the structural landscape of amorphous ice. Our results highlight the difficulty in accurately differentiating MDA from LDA due to structural similarities. Beyond water, our methodology can be applied to investigate the structural properties and phases of disordered materials. [ABSTRACT FROM AUTHOR]

Machine-learning (ML)-based interatomic potentials are increasingly popular in material modeling, enabling highly accurate simulations with thousands and millions of atoms. However, the performance of machine-learned potentials depends strongly on the choice of hyperparameters—that is, of those parameters that are set before the model encounters data. This problem is particularly acute where hyperparameters have no intuitive physical interpretation and where the corresponding optimization space is large. Here, we describe an openly available Python package that facilitates hyperparameter optimization across different ML potential fitting frameworks. We discuss methodological aspects relating to the optimization itself and to the selection of validation data, and we show example applications. We expect this package to become part of a wider computational framework to speed up the mainstream adaptation of ML potentials in the physical sciences. [ABSTRACT FROM AUTHOR]

Machine learning (ML) based interatomic potentials are emerging tools for material simulations, but require a trade-off between accuracy and speed. Here, we show how one can use one ML potential model to train another: we use an accurate, but more computationally expensive model to generate reference data (locations and labels) for a series of much faster potentials. Without the need for quantum-mechanical reference computations at the secondary stage, extensive reference datasets can be easily generated, and we find that this improves the quality of fast potentials with less flexible functional forms. We apply the technique to disordered silicon, including a simulation of vitrification and polycrystalline grain formation under pressure with a system size of a million atoms. Our work provides conceptual insight into the ML of interatomic potential models and suggests a route toward accelerated simulations of condensed-phase systems. [ABSTRACT FROM AUTHOR]

We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. We combine structural databases for amorphous carbon and graphene, which we extend substantially by adding suitable configurations, for example, for defects in graphene and other nanostructures. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional. Dispersion interactions, which are crucial to describe multilayer carbonaceous materials, are therefore implicitly included. We additionally account for long-range dispersion interactions using a semianalytical two-body term and show that an improved model can be obtained through an optimization of the many-body smooth overlap of atomic positions descriptor. We rigorously test the potential on lattice parameters, bond lengths, formation energies, and phonon dispersions of numerous carbon allotropes. We compare the formation energies of an extensive set of defect structures, surfaces, and surface reconstructions to DFT reference calculations. The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon [V. L. Deringer and G. Csányi, Phys. Rev. B 95, 094203 (2017)] with the high numerical accuracy necessary for crystalline graphene [Rowe et al., Phys. Rev. B 97, 054303 (2018)], thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon. [ABSTRACT FROM AUTHOR]

We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with graphene, graphite, and disordered carbon nanostructures, based on reference density functional theory data. Rather than treating the full Li–C system, we demonstrate how the energy and force differences arising from Li intercalation can be modeled and then added to a (prexisting and unmodified) GAP model of pure elemental carbon. Furthermore, we show the benefit of using an explicit pair potential fit to capture “effective” Li–Li interactions and to improve the performance of the GAP model. This provides proof-of-concept for modeling guest atoms in host frameworks with machine-learning based potentials and in the longer run is promising for carrying out detailed atomistic studies of battery materials. [ABSTRACT FROM AUTHOR]

Prompted by this observation, the reference dataset used to fit the GAP-20 model was recomputed, using DFT calculations with moreconsistent reciprocal-space sampling than was used in the original training data. (b) bcc atomization energy for updated GAP-20 model (GAP-20U); the better regularization of the model reduces the magnitude of the fluctuations, and the bcc atomization energy remains high. (a) bcc atomization energy for the original GAP-20 model, showing a bcc carbon minimum below the atomization energy of graphite. [Extracted from the article]

Oxidation of the phase-change material germanium telluride (GeTe) is an atomic-scale process of fundamental importance, as it is detrimental to the stability of GeTe-based data-storage devices. Here, we present comprehensive density-functional theory simulations of molecular and atomic oxygen in contact with GeTe(111) surfaces. Molecular O2 is predicted to readily adsorb on the Ge-terminated (111) surface; the pristine Te-terminated counterpart, by contrast, appears quite inert. The coverage-dependent adsorption of O atoms is then investigated, and based on these data, a surface phase diagram for GeTe(111)/O is constructed. These results afford a detailed, atom-resolved picture of the initial surface oxidation of GeTe, and they harmonize well with a previous X-ray photoelectron spectroscopy study on this very topic. [ABSTRACT FROM AUTHOR]

The discovery of materials is increasingly guided by quantum‐mechanical crystal‐structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data‐driven approaches can vastly accelerate the search for complex structures, combining a machine‐learning (ML) model for the potential‐energy surface with efficient, fragment‐based searching. We use the characteristic building units observed in Hittorf's and fibrous phosphorus to seed stochastic ("random") structure searches over hundreds of thousands of runs. Our study identifies a family of hierarchically structured allotropes based on a P8 cage as principal building unit, including one‐dimensional (1D) single and double helix structures, nanowires, and two‐dimensional (2D) phosphorene allotropes with square‐lattice and kagome topologies. These findings yield new insight into the intriguingly diverse structural chemistry of phosphorus, and they provide an example for how ML methods may, in the long run, be expected to accelerate the discovery of hierarchical nanostructures. [ABSTRACT FROM AUTHOR]

The discovery of materials is increasingly guided by quantum‐mechanical crystal‐structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data‐driven approaches can vastly accelerate the search for complex structures, combining a machine‐learning (ML) model for the potential‐energy surface with efficient, fragment‐based searching. We use the characteristic building units observed in Hittorf's and fibrous phosphorus to seed stochastic ("random") structure searches over hundreds of thousands of runs. Our study identifies a family of hierarchically structured allotropes based on a P8 cage as principal building unit, including one‐dimensional (1D) single and double helix structures, nanowires, and two‐dimensional (2D) phosphorene allotropes with square‐lattice and kagome topologies. These findings yield new insight into the intriguingly diverse structural chemistry of phosphorus, and they provide an example for how ML methods may, in the long run, be expected to accelerate the discovery of hierarchical nanostructures. [ABSTRACT FROM AUTHOR]

Understanding the thermal properties of disordered systems is of fundamental importance for condensed matter physics - and for practical applications as well. While quantities such as the thermal conductivity are usually well characterised experimentally, their microscopic origin is often largely unknown - hence the pressing need for molecular simulations. However, the time and length scales involved with thermal transport phenomena are typically well beyond the reach of ab initio calculations. On the other hand, many amorphous materials are characterised by a complex structure, which prevents the construction of classical interatomic potentials. One way to get past this deadlock is to harness machine-learning (ML) algorithms to build interatomic potentials: these can be nearly as computationally efficient as classical force fields while retaining much of the accuracy of first-principles calculations. Here, we discuss neural network potentials (NNPs) and Gaussian approximation potentials (GAPs), two popular ML frameworks. We review the work that has been devoted to investigate, via NNPs, the thermal properties of phase-change materials, systems widely used in non-volatile memories. In addition, we present recent results on the vibrational properties of amorphous carbon, studied via GAPs. In light of these results, we argue that ML-based potentials are among the best options available to further our understanding of the vibrational and thermal properties of complex amorphous solids. [ABSTRACT FROM AUTHOR]

Disordered nanoporous and “hard” carbons are widely used in batteries and supercapacitors, but their atomic structures are poorly determined. Here, we combine machine learning and DFT to obtain new atomistic insight into carbonaceous energy materials. We study structural models of porous and graphitic carbons, and Na intercalation as relevant for sodium-ion batteries. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nanocrystals with rationally tailored morphologies and properties are a thriving research field. The phase-change material (PCM) germanium telluride (GeTe) has recently been synthesized in a plethora of nanocrystalline forms and morphologies, but a mechanistic understanding of these experimental findings has been largely missing so far. Here, we present comprehensive dispersion-corrected density functional theory (DFT-D) simulations of the low-index crystal facets of GeTe and how they interact with relevant ligand molecules. These results enable a systematic understanding of experimental findings regarding GeTe nanocrystals and clarify the origin of the broad range of particle shapes observed. This study also affords semiquantitative signposts assisting future synthetic studies of nanocrystalline PCMs. [ABSTRACT FROM AUTHOR]

The thermal motion of atoms in crystals is quantified by anisotropic displacement parameters (ADPs). Here we show that dispersion-corrected periodic density-functional theory can be used to compute accurate ADPs for transition metal carbonyls, which serve as model systems for crystalline organometallic and coordination compounds. [ABSTRACT FROM AUTHOR]

Urea (CN2H4O) is a fundamental biomolecule whose derivatives are abundant throughout chemistry. Among the latter, rubidium ureate (RbCN2H3O) and its cesium analog (CsCN2H3O) have been described only very recently and form the first structurally characterized salts of deprotonated urea. Here, we report on a neutron diffraction study on the aforementioned alkaline-metal ureates, which affords the positions for all hydrogen atoms (including full anisotropic displacement tensors) and thus allows us to gain fundamental insights into the hydrogen-bonding networks in the title compounds. The structure refinements of the experimental neutron data proceeded successfully using starting parameters from ab initio simulations of atomic positions and anisotropic displacement parameters. Such joint experimental-theoretical refinement procedures promise significant practical potential in cases where complex solids (organic, organometallic, framework materials) are studied by powder diffraction. [ABSTRACT FROM AUTHOR]

The computer program LOBSTER (Local Orbital Basis Suite Towards Electronic-Structure Reconstruction) enables chemical-bonding analysis based on periodic plane-wave (PAW) density-functional theory (DFT) output and is applicable to a wide range of first-principles simulations in solid-state and materials chemistry. LOBSTER incorporates analytic projection routines described previously in this very journal [J. Comput. Chem. 2013, 34, 2557] and offers improved functionality. It calculates, among others, atom-projected densities of states (pDOS), projected crystal orbital Hamilton population (pCOHP) curves, and the recently introduced bond-weighted distribution function (BWDF). The software is offered free-of-charge for non-commercial research. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Chitin is an abundant biopolymer that stabilizes the exoskeleton of insects and gives structure to plants. Its macroscopic properties go back to an intricate network of hydrogen bonds that connect the polymer strands, and these intermolecular links have been under ongoing study. Here, we use atomistic simulations to explore hydrogen bonding in the most abundant form, α-chitin. The crystal structure exhibits disorder, and so discrete models are systematically derived as suitable approximants to the macroscopic material. These models then allow us to perform dispersion-corrected density-functional theory (DFT-D) simulations on the three-dimensional crystal network and on lower-dimensional fragments. Thereby, we rationalize the nature of hydrogen bonding and the role of crystallographic disorder for the stability of α-chitin, and complement previous, larger-scale molecular-dynamics (MD) simulations as well as recent fiber-diffraction experiments. Our results provide new, atomic-level insight into one of Nature's most abundant building materials, and the techniques and concepts are likely transferable to other biopolymers. [ABSTRACT FROM AUTHOR]

Synthesis and utilization of nanocrystals are highly active fields of current research, but they require a thorough understanding of the underlying crystal surfaces. In this Minireview, we span the arc from surfaces to free nanocrystals, and onward to their chemical synthesis, using as examples lead selenide (PbSe), tin telluride (SnTe), and their direct chemical relatives. Besides experimental insights, we highlight the increasingly influential role played by quantum-chemical simulations of surfaces and nanocrystals. What can theory do today, or possibly tomorrow; where are its limits? Answering these questions, and skillfully linking them to experiments, could open up new atomistically (that is, chemically) guided perspectives for nanosynthesis. [ABSTRACT FROM AUTHOR]

Die Synthese und die Nutzbarmachung von Nanokristallen sind hochaktuelle Forschungsfelder, doch setzen sie ein gründliches Verständnis der beteiligten Kristallflächen voraus. In diesem Kurzaufsatz schlagen wir ebendiesen Bogen von Oberflächen zu freien und weiter zu naβchemisch synthetisierten Nanokristallen, und zwar exemplarisch für Bleiselenid (PbSe), Zinntellurid (SnTe) und ihre direkten chemischen Verwandten. Wir diskutieren experimentelle Einblicke und darüber hinaus die - zunehmend einfluβreiche - quantenchemische Simulation von Oberflächen und Nanokristallen. Was kann die Theorie heute leisten, was möglicherweise morgen, was kann sie eben nicht? Die Beantwortung dieser Fragen und ihre geschickte Verknüpfung mit Experimenten könnten neue, atomistisch (und damit chemisch) geleitete Perspektiven in der Nanosynthese eröffnen. [ABSTRACT FROM AUTHOR]

Phase-change materials (PCMs) are widely used for data storage and in other functional devices. Despite their seemingly simple compositions, these materials exhibit intriguing microscopic complexity and a portfolio of interesting properties. In this Feature Article, it is shown that structural and electronic peculiarities on the atomic scale are key determinants for the technological success of PCMs. Particular emphasis is put on the interplay of different experimental and theoretical methods, on the bonding nature of crystalline and amorphous PCMs, and on the role of surfaces and nanostructures. Then, unconventional transport properties of the crystalline phases are highlighted, both with regard to electrical and heat conduction. Finally, perspectives and future directions are drawn: for finding new PCMs based on microscopic understanding, and also for new applications of these materials in emerging fields. [ABSTRACT FROM AUTHOR]

Amorphous red phosphorus (a‐P) is one of the remaining puzzling cases in the structural chemistry of the elements. Here, we elucidate the structure, stability, and chemical bonding in a‐P from first principles, combining machine‐learning and density‐functional theory (DFT) methods. We show that a‐P structures exist with a range of energies slightly higher than those of phosphorus nanorods, to which they are closely related, and that the stability of a‐P is linked to the degree of structural relaxation and medium‐range order. We thus complete the stability range of phosphorus allotropes [Angew. Chem. Int. Ed. 2014, 53, 11629] by now including the previously poorly understood amorphous phase, and we quantify the covalent and van der Waals interactions in all main phases of phosphorus. We also study the electronic densities of states, including those of hydrogenated a‐P. Beyond the present study, our structural models are expected to enable wider‐ranging first‐principles investigations—for example, of a‐P‐based battery materials. [ABSTRACT FROM AUTHOR]

Amorpher roter Phosphor (a‐P) stellt eine der verbleibenden Herausforderungen in der Strukturchemie der Elemente dar. In diesem Beitrag klären wir Struktur, Stabilität und chemische Bindung im a‐P auf Grundlage voraussetzungsfreier Rechnungen auf, indem wir maschinelles Lernen und dichtefunktionaltheoretische (DFT‐) Methoden kombinieren. Wir zeigen, dass die Energien von a‐P‐Strukturen leicht höher liegen als jene von Phosphor‐Nanostäben, mit denen sie eng verwandt sind, und dass die Stabilität von a‐P mit dem Grad an struktureller Relaxation und mittelreichweitiger Ordnung verknüpft ist. Wir vervollständigen damit die Stabilitätsreihe der Phosphorallotrope [Angew. Chem. Int. Ed. 2014, 53, 11629], indem wir die bislang unvollständig verstandene amorphe Phase hinzufügen, und wir quantifizieren die kovalenten und van‐der‐Waals‐Wechselwirkungen in allen wichtigen Phasen des Phosphors. Wir untersuchen darüberhinaus die elektronische Zustandsdichte, einschließlich jener des wasserstoffhaltigen a‐P. Über die vorliegende Arbeit hinaus erwarten wir, dass unsere Strukturmodelle weiterführende voraussetzungsfreie Untersuchungen ermöglichen werden – beispielsweise zu a‐P‐basierten Batteriematerialien. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) and layered structures gained a lot of attention in the recent years ("post-graphene era"). The chalcogen cyanides S(CN)2 and Se(CN)2 offer themselves as interesting model systems to study layered inorganic crystal structures; both are built up from cyanide molecules connected by chalcogen bonds (ChBs). Here, we investigate ChBs and their cooperativity directly within the layers of the S(CN)2 and Se(CN)2 crystal structures and, furthermore, in putative O(CN)2 and Te(CN)2 crystal structures derived therefrom. Moreover, we determine the energetic contributions of ChBs within the layers to the overall stabilization energy. To compare these structures not only energetically but also geometrically, we derive a direction-dependent root mean square of the Cartesian displacement, a possible tool for further computational investigations of layered compounds. The molecular chains connected by ChBs are highly cooperative but do not influence each other when combined to layers: the ChBs are nearly orthogonal in terms of energy when connected to the same chalcogen acceptor atom. Layers built up from ChBs account for 41% to 79% of the overall interaction energy in the crystal. This provides new, fundamental insight into the meaning of ChBs, and therefore directed intermolecular interactions, for the stability of crystal structures. [ABSTRACT FROM AUTHOR]

The creation of “maps” for solid-state materials has a long-standing history in condensed matter theory. Here, based on periodic density-functional theory (DFT) output, a heuristic numerical indicator is constructed to assess s–p orbital mixing in materials (or, depending on one׳s viewpoint, the tendency toward “sp 3 hybridization”). Other than before, this now intrinsically includes structural information and the microscopic effects associated with it. The new method provides useful insights to understand physical relationships in composition space and promises to help to identify hitherto unknown material candidates. [ABSTRACT FROM AUTHOR]

The thermal motion of atoms and functional groups is a key characteristic of any molecular crystal, and such motion derived from scattering experiments is conveniently visualised by means of thermal ellipsoids (the famous "ORTEP" drawings). Unfortunately, it is often impossible to obtain the underlying anisotropic displacement parameters (ADPs) for hydrogen atoms, due to their low X-ray scattering power, and sometimes no ADPs can be refined at all even for heavier atoms. In these cases, it would seem advantageous to estimate ADPs by first-principles techniques, and indeed such ORTEP plots have become available ab initio very recently. Here, we test this young method for a representative set of hydrogen-bonded molecular crystals: first, we study urea (CON2H4) as a well-known benchmark, then, its all-nitrogen analogue guanidine (CN3H5); finally, we move on to rubidium guanidinate (RbCN3H4) as a specimen with pronounced ionic interactions. For all three systems, ADPs have been obtained from density-functional theory (DFT) based phonon computations using the PHONOPY software. The results are compared with neutron-diffraction data as the experimental "benchmark" in this regard, and a critical discussion of experimental aspects is given. We observe excellent agreement between experiment and theory for the hydrogen-bonded systems urea and guanidine at low temperature, whereas high-temperature data for guanidine deviate visibly, and the more salt-like RbCN3H4 may suffer from a less-than-ideal description even at 12 K. Both are discussed in depth as there are possible solutions and directions for further research. Generally, the present results shine a favourable light on a future, more routine application of combined experimental/ theoretical approaches in chemical crystallography. [ABSTRACT FROM AUTHOR]

Despite its simple chemical constitution and unparalleled technological importance, the phase-change material germanium telluride (GeTe) still poses fundamental questions. In particular, the bonding mechanisms in amorphous GeTe have remained elusive to date, owing to the lack of suitable bond-analysis tools. Herein, we introduce a bonding indicator for amorphous structures, dubbed 'bond-weighted distribution function' (BWDF), and we apply this method to amorphous GeTe. The results underline a peculiar role of homopolar GeGe bonds, which locally stabilize tetrahedral fragments but not the global network. This atom-resolved (i.e., chemical) perspective has implications for the stability of amorphous 'zero bits' and thus for the technologically relevant resistance-drift phenomenon. [ABSTRACT FROM AUTHOR]

Ungeachtet seiner einfachen Zusammensetzung und enormen technischen Wichtigkeit wirft das Phasenwechselmaterial Germaniumtellurid (GeTe) auch heute noch grundlegende Fragen auf. Insbesondere die Bindungsmechanismen in amorphem GeTe haben sich bislang einer schlüssigen Erklärung entzogen, da geeignete bindungsanalytische Hilfsmittel fehlten. Hier führen wir einen neuen Bindungsindikator für amorphe Strukturen ein, den wir als “bindungsgewichtete Verteilungsfunktion” (BWDF) bezeichnen, und wenden diese Methode auf amorphes GeTe an. Die Ergebnisse belegen, daß homopolare Ge ‐ Ge ‐ Bindungen darin eine besondere Rolle spielen: sie stabilisieren einzelne tetraedrische Fragmente, nicht aber das globale Netzwerk. Diese atomaufgelöste (d. h. chemische) Perspektive hat Bedeutung für die Langzeitstabilität amorpher “Null ‐ Bits” und damit für das technisch wichtige Phänomen der Widerstandsdrift. Amorph – kein Problem! Wie man eine Kristallstruktur in lokale Fragmente und kovalente Bindungen zerlegt, haben Chemiker lange perfektioniert. Mit neuen Hilfsmitteln läßt sich diese chemische Sprache auf amorphe Festkörpermaterialien erweitern – hier auf das Phasenwechsel ‐ Speichermaterial Germaniumtellurid. [ABSTRACT FROM AUTHOR]

Halogen bonds (XBs) are intriguingnoncovalent interactions thatare frequently being exploited for crystal engineering. Recently,similar bonding mechanisms have been proposed for adjacent main-groupelements, and noncovalent “chalcogen bonds” and “pnictogenbonds” have been identified in crystal structures. A fundamentalquestion, largely unresolved thus far, is how XBs and related contactsinteract with each other in crystals; similar to hydrogen bonding,one might expect “cooperativity” (bonds amplifying eachother), but evidence has been sparse. Here, we explore the crucialstep from gas-phase oligomers to truly infinite chains by means ofquantum chemical computations. A periodic density functional theory(DFT) framework allows us to address polymeric chains of moleculesavoiding the dreaded “cluster effects” as well as thearbitrariness of defining a “large enough” cluster.We focus on three types of molecular chains that we cut from crystalstructures; furthermore, we explore reasonable substitutional variantsin silico. We find evidence of cooperativity in chains of halogencyanides and also in similar chalcogen- and pnictogen-bonded systems;the bonds, in the most extreme cases, are amplified through cooperativeeffects by 79% (I···N), 90% (Te···N),and 103% (Sb···N). Two experimentally known organiccrystals, 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-dioxanechains; in fact, these XBs weakeneach other by upto 26% compared to the respective gas-phase dimers. [ABSTRACT FROM AUTHOR]

Finite-temperature properties such as thermodynamic state functions can be obtained for a range of crystalline materials by combining density functional theory (DFT) with lattice-dynamics approaches. Despite the usefulness of such first-principles predictions, their results must be carefully checked for accuracy, especially in cases where the DFT description of the material itself is nontrivial. Here, we investigate a prototypical layered semiconductor, namely, germanium selenide (GeSe) by dispersion-corrected DET, lattice-dynamics computations, and a thermodynamic framework that relies on the quasiharmonic approximation (QHA). We study phonon band structures, their evolution under pressure, and finite-temperature thermodynamic state functions. Besides the layered orthorhombic structure, this analysis includes the high-temperature cubic (rocksalt-type) polymorph of GeSe, which is shown to exhibit imaginary vibrational modes but emerging dynamic stability under increasing external pressure. The effect of these imaginary vibrational modes on the QHA and hence on computed thermochemical properties is critically evaluated. First-principles thermodynamics correctly predict a high-temperature transition from the orthorhombic to the cubic structure, albeit the transition temperature is severely underestimated. This simple compound allows us to address important methodological questions regarding the QHA treatment of crystalline solids. [ABSTRACT FROM AUTHOR]

Mutually perpendicular hydrogen bonds, halogen bonds and stacking interactions are investigated in the seemingly simple crystal structure of bromomalonic aldehyde (C3H3O2Br). This combined experimental and theoretical study sheds new light on the role of electrostatic interactions in molecular crystalline networks and poses a caveat to look beyond what may be termed "chemical intuition". [ABSTRACT FROM AUTHOR]

A family of alkali-metal guanidinates (M = Na–Cs) has been reported recently, representing all-nitrogen analogues of alkali bicarbonates and containing the elusive guanidinate monoanion CN3H4−. Here, we describe the synthesis and characterisation of LiCN3H4 as the last representative of alkali guanidinates. Single crystals of LiCN3H4 were obtained using a solvothermal route in liquid ammonia, and the crystal structure was determined at 100 K using single-crystal X-ray diffraction (monoclinic, P21/c, Z = 4, a = 7.251(2) Å, b = 4.532(2) Å, c = 9.051(2) Å, β = 103.315(3)°). The structure of LiCN3H4 is reminiscent of the previously reported NaCN3H4 type in that both guanidinates contain tetrahedrally nitrogen-coordinated alkali-metal cations. The linking of these tetrahedra differs, however: they share corners in NaCN3H4 to form one-dimensionally infinite chains running through the crystal, whereas isolated, edge-sharing tetrahedra (Li2N6 motif) are found in the LiCN3H4 structure described here. Periodic density-functional theory (DFT) computations at the dispersion-corrected PBE + D2 level not only correctly reproduce the structural preferences, but also indicate that a NaCN3H4 polymorph should be viable adopting the lithium guanidinate type but much less easily so vice versa. [ABSTRACT FROM AUTHOR]

Quantum-chemical computations of solids benefit enormously from numerically efficient plane-wave (PW) basis sets, and together with the projector augmented-wave (PAW) method, the latter have risen to one of the predominant standards in computational solid-state sciences. Despite their advantages, plane waves lack local information, which makes the interpre-tation of local densities-of-states (DOS) difficult and precludes the direct use of atom-resolved chemical bonding indicators such as the crystal orbital overlap population (COOP) and the crystal orbital Hamilton population (COHP) techniques. Recently, a number of methods have been proposed to over-come this fundamental issue, built around the concept of basis-set projection onto a local auxiliary basis. In this work, we propose a novel computational technique toward this goal by transferring the PW/PAW wavefunctions to a properly cho- sen local basis using analytically derived expressions. In partic-ular, we describe a general approach to project both PW and PAW eigenstates onto given custom orbitals, which we then exemplify at the hand of contracted multiple-Ç Slater-type orbitals. The validity of the method presented here is illus-trated by applications to chemical textbook examples--dia-mond, gallium arsenide, the transition-metal titanium--as well as nanoscale allotropes of carbon: a nanotube and the C6o full-erene. Remarkably, the analytical approach not only recovers the total and projected electronic DOS with a high degree of confidence, but it also yields a realistic chemical-bonding pic-ture in the framework of the projected COHP method. [ABSTRACT FROM AUTHOR]

Germanium dioxide (Ge02) takes two forms at ambient pressure: a thermodynamically stable rutile-type structure and a high-temperature quartz-type polymorph. Here, we investigate the phase stability at finite temperatures by ab initio phonon and thermochemical computations. We use gradient-corrected den-sity-functional theory (PBE-GGA) and pay particular attention to the modeling of the "semicore" germanium 3d Orbitals (ascrib-ing them either to the core or to the valence region). The phase transition is predicted correctly in both cases, and computed heat capacities and entropies are in excellent agreement with thermochemical database values. Nonetheless, the computed formation energies of α-quartz-type GeO2 (and, consequently, the predicted transition temperatures) differ significantly depending on theoretical method. Remarkably, the simpler and cheaper computational approach produces seemingly better results, not worse. In our opinion, GeO2 is a nice test case that illustrates both possibilities and limitations of modern ab initio thermochemistry. [ABSTRACT FROM AUTHOR]

Germaniumtelluride (GeTe) is an iconic functional material, bothin itself and as the parent compound for a range of ternary phase-changedata-storage alloys. Long taken to be a “simple” ABcompound, crystalline GeTe is today known to contain a large numberof germanium vacancies which directly affect the material’smacroscopic properties. Here, we use atomistic simulations to elucidatelocal mechanisms behind the motion of Ge atoms (and thus, vacancydiffusion) in crystalline GeTe. Transition pathways are computed usingthe nudged elastic band (NEB) approach at the gradient-corrected levelof density-functional theory (GGA-DFT), both for the idealized rhombohedral(R3m) crystal and a number of defectiveconfigurations. Besides obvious structural arguments (i.e., beyonda simple rigid-sphere model), the diffusion barriers show a delicatedependence on the material’s electronic structure. The latteris controlled by vacancy formation, Sb adatoms, and charge injection,all of which is discussed in a unified framework. [ABSTRACT FROM AUTHOR]

Transition-metal borides show not only promising physical properties but also a rich variety of crystal structures. In this context, quantum-chemical tools can shed light on important facets of the chemistry within such intermetallic borides. Using density-functional theory (DFT), we analyze in detail two phases of significant structural-chemical importance: the recently synthesized Ti1+xOs2-xRuB2 and the isotypical Ti1+xOs3-xB2. Starting from the observation of different Ti/Os occupations in X-ray crystal structure analysis, we assess suitable computational models and rationalize how the interplay of Ti-Ti, Ti-Os, and Os-Os bonds drives the site preferences. Then, we move on to a systematic investigation of the metal-boron bonds which embed the characteristic, trigonal-planar B4 units within their metallic surroundings. Remarkably, the different Ti-B bonds in Ti1+xOs2-xRuB2 (and also in its ternary derivative) are of vastly different strength, and the strength of these bonds does not correlate with their length. The tools presented in this work are based on simple and insightful chemical arguments together with DFT, and may subsequently be transferred to other intermetallic phases-transition-metal borides and beyond. [ABSTRACT FROM AUTHOR]

Hydrogen bonding is among the most important interactionsin molecularcrystals, and examples are abundant. As a consequence of such interactions,many molecules crystallize in complex but intriguing structures, incontrast to the relatively simple packing principles of metallic orionic solids. In this work, we present a computational approach basedon plane-wave density-functional theory (DFT) and supercell techniques,aiming to understand and quantify hydrogen-bonded networks in thesolid state and in two-, one-, and zero-dimensional fragments derivedfrom the molecular crystal. With such methodology at hand, we investigateguanidine, a fitting example of a molecular crystal and an importantcompound for inorganic and organic chemistry alike. On the basis ofour computations, we discuss the initially proposed layered structureof guanidine and identify both stabilizing and destabilizing cooperativeinteractions in the three crystalline dimensions. [ABSTRACT FROM AUTHOR]

A ternary stannide of sodium and magnesium, Na2MgSn, was synthesized from the elements, and the crystal structure was determined by single-crystal X-ray diffraction. The compound crystallizes in the Li2CuAs structure type (hexagonal, P63/mmc, Z = 2, a = 5.0486(11) Å, c = 10.095(2) Å), and its structure is built up of two-dimensional honeycomb layers of 2∞[(MgSn)2-] stacked along the c-axis, with Na atoms as "space fillers". First-principles computations at various levels of density functional theory (DFT) verify that the most stable configuration is the one in which Na and Mg atoms occupy the 4f and 2b sites, respectively, and thus DFT provides a necessary complement to X-ray structural elucidation. Our computations also predict that Na2MgSn must be a semiconductor with a small band gap. In accord with these predictions, the electrical resistivity measured for a polycrystalline sample of Na2MgSn is 9.6-10.4 mΩ cm in the range of 90-635 K, and the Seebeck coefficient decreases from +390 μV K-1 (at 300 K) to +150 μV K-1 (at 430 K). [ABSTRACT FROM AUTHOR]

Organic molecules crystallize in manifold structures.The lastfew decades have seen the rise of high-resolution X-ray diffractiontechniques that make the structures of even the most complex crystalseasily accessible. Still, an intrinsic challenge lies in assigninghydrogen atoms’ positions from X-ray experiments alone. Quantumchemistry plays a fruitful, complementary role here, and so ab initiooptimization techniques for organic crystals are on the rise as well.In this context, we review and evaluate a popular ab initio strategybased on plane-wave density-functional computations, namely, selectivelyrelaxing H positions in an otherwise fixed cell. Our data show thatsuch-optimized C–H, N–H, O–H, and B–Hbond lengths coincide well with results from neutron diffractiontheexperimental technique that sets the “gold standard”for H positions in molecular crystals but which is far less easilyavailable. We have thus justified the use of a quantum-chemical aidewith a broad variety of possible applications. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods are of rapidly growing interest for materials modeling, and yet, the use of ML interatomic potentials for new systems is often more demanding than that of established density-functional theory (DFT) packages. Here, we describe computational methodology to combine the CASTEP first-principles simulation software with the on-the-fly fitting and evaluation of ML interatomic potential models. Our approach is based on regular checking against DFT reference data, which provides a direct measure of the accuracy of the evolving ML model. We discuss the general framework and the specific solutions implemented, and we present an example application to high-temperature molecular-dynamics simulations of carbon nanostructures. The code is freely available for academic research. [ABSTRACT FROM AUTHOR]

The covalent nature of short hydrogen bonds has been under debate for long. Here we show that the crystal orbital Hamilton population (COHP) bonding indicator gives new, complementary evidence of covalent hydrogen- - acceptor interactions in the molecular solid state. [ABSTRACT FROM AUTHOR]

The structure of amorphous silicon (a‐Si) is widely thought of as a fourfold‐connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling‐bond" and "floating‐bond" defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a‐Si. We study an ultra‐large‐scale, quantum‐accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect‐related statistics to be obtained. We combine structural descriptors and machine‐learned atomic energies to develop a classification of the different types of defects in a‐Si. The results suggest a revision of the established floating‐bond model by showing that fivefold‐bonded atoms in a‐Si exhibit a wide range of local environments–analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone. [ABSTRACT FROM AUTHOR]

The structure of amorphous silicon (a‐Si) is widely thought of as a fourfold‐connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling‐bond" and "floating‐bond" defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a‐Si. We study an ultra‐large‐scale, quantum‐accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect‐related statistics to be obtained. We combine structural descriptors and machine‐learned atomic energies to develop a classification of the different types of defects in a‐Si. The results suggest a revision of the established floating‐bond model by showing that fivefold‐bonded atoms in a‐Si exhibit a wide range of local environments–analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone. [ABSTRACT FROM AUTHOR]

Studies of supercooled liquid phase‐change materials are important for the development of phase‐change memory and neuromorphic computing devices. Herein, a machine‐learning (ML)‐based interatomic potential for Ge2Sb2Te5 (GST) to conduct large‐scale molecular dynamics simulations of liquid and supercooled liquid GST is used. A pronounced effect of the thermostat parameters on the simulation results is demonstrated, and it is shown how using a Langevin thermostat with optimized damping values can lead to excellent agreement with reference ab initio molecular dynamics (AIMD) simulations. Structural and dynamical analyses are presented, including the studies of radial and angular distributions, homopolar bonds, and the temperature‐dependent diffusivity. Herein, the usefulness of ML‐driven molecular dynamics for further studies of supercooled liquid GST, with length and timescales far exceeding those that are accessible to AIMD is demonstrated. [ABSTRACT FROM AUTHOR]

We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high sp³ fractions in excess of 85% observed experimentally are reproduced by means of computational simulation, and the deposition energy dependence of the film's characteristics is also accurately described. High confidence in the potential and direct access to the atomic interactions allow us to infer the microscopic growth mechanism in this material. While the widespread view is that ta-C grows by "subplantation," we show that the so-called "peening" model is actually the dominant mechanism responsible for the high sp³ content. We show that pressure waves lead to bond rearrangement away from the impact site of the incident ion, and high sp³ fractions arise from a delicate balance of transitions between three- and fourfold coordinated carbon atoms. These results open the door for a microscopic understanding of carbon nanostructure formation with an unprecedented level of predictive power. [ABSTRACT FROM AUTHOR]

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery. [ABSTRACT FROM AUTHOR]

We generate representative structural models of amorphous carbon (a-C) from constant-volume quenching from the liquid with subsequent relaxation of internal stresses in molecular dynamics simulations using empirical and machine-learning interatomic potentials. By varying volume and quench rate we generate structures with a range of density and amorphous morphologies. We find that all a-C samples show a universal relationship between hybridization, bulk modulus and density despite having distinctly different cohesive energies. Differences in cohesive energy are traced back to slight changes in the distribution of bond-angles that is likely linked to thermal stability of these structures. [ABSTRACT FROM AUTHOR]

Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB2 phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be "coarse-grained" in hybrid framework materials. [ABSTRACT FROM AUTHOR]

The layered crystal structure of Cr2Ge2Te6 shows ferromagnetic ordering at the two‐dimensional limit, which holds promise for spintronic applications. However, external voltage pulses can trigger amorphization of the material in nanoscale electronic devices, and it is unclear whether the loss of structural ordering leads to a change in magnetic properties. Here, it is demonstrated that Cr2Ge2Te6 preserves the spin‐polarized nature in the amorphous phase, but undergoes a magnetic transition to a spin glass state below 20 K. Quantum‐mechanical computations reveal the microscopic origin of this transition in spin configuration: it is due to strong distortions of the CrTeCr bonds, connecting chromium‐centered octahedra, and to the overall increase in disorder upon amorphization. The tunable magnetic properties of Cr2Ge2Te6 can be exploited for multifunctional, magnetic phase‐change devices that switch between crystalline and amorphous states. [ABSTRACT FROM AUTHOR]

We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory (DFT) potential-energy surface, such interatomic potentials enable materials simulations with close-to DFT accuracy but at much lower computational cost. We first determine the maximum accuracy that any finite-range potential can achieve in carbon structures; then, using a hierarchical set of two-, three-, and many-body structural descriptors, we construct a GAP model that can indeed reach the target accuracy. The potential yields accurate energetic and structural properties over a wide range of densities; it also correctly captures the structure of the liquid phases, at variance with a state-of-the-art empirical potential. Exemplary applications of the GAP model to surfaces of "diamondlike" tetrahedral amorphous carbon (ta-C) are presented, including an estimate of the amorphous material's surface energy and simulations of high-temperature surface reconstructions ("graphitization"). The presented interatomic potential appears to be promising for realistic and accurate simulations of nanoscale amorphous carbon structures. [ABSTRACT FROM AUTHOR]

Review: [focus on PbSe and SnSe; 59 refs. [ABSTRACT FROM AUTHOR]