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An abundance of oxide, halide and chalcogenide perovskites have been explored, demonstrating outstanding properties, while the emerging nitride perovskites are extremely rare due to their challenging synthesis requirements. By inverting the ion type in the perovskite structure, the corresponding antiperovskite structure is obtained. Among them, ternary antiperovskite nitrides X 3 AN (X=Ba, Sr, Ca, Mg; A=As, Sb) have recently been identified as exhibiting excellent optoelectronic properties. To explore the unrealized composition space of nitride perovskites, the ammonothermal method was applied, yielding three new layered quaternary imide-based defect-antiperovskites, namely AE 5 AsPn(NH) 2 (AE=Ca, Sr; Pn=Sb, Bi). These new compounds feature distorted square-pyramidal coordination around the imide-group (Ca 5 NH). Layers with Ca 2+ vacancies are found with an alternating As 3- and Pn 3- (Pn 3- =Sb 3- , Bi 3- ) coordination along the A-site, forming a two-dimensional (2D) structure. All three AE 5 AsPn(NH) 2 compounds show suitable direct band gaps within the visible light spectrum. Density functional theory calculations reveal favorable band dispersion, as well as transport and optical properties, especially along the out-of-plane direction, demonstrating their 3D character of electronic transport. The narrow tunable direct band gaps and favorable charge carrier properties make AE 5 AsPn(NH) 2 promising candidates for solar cell absorber materials., (© 2025 The Author(s). Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

In this work, we use a Bayesian optimization (BO) algorithm to sample the space of covalent organic framework (COF) components aimed at the design of COFs with a high hole conductivity. COFs are crystalline, often porous coordination polymers, where organic molecular units-called building blocks (BBs)-are connected by covalent bonds. Even though we limit ourselves here to a space of three-fold symmetric BBs forming two-dimensional COF sheets, their design space is still much too large to be sampled by traditional means through evaluating the properties of each element in this space from first principles. In order to ensure valid BBs, we use a molecular generation algorithm that, by construction, leads to rigid three-fold symmetric molecules. The BO approach then trains two distinct surrogate models for two conductivity properties, level alignment vs a reference electrode and reorganization free energy, which are combined in a fitness function as the objective that evaluates BBs' conductivities. These continuously improving surrogates allow the prediction of a material's properties at a low computational cost. It thus allows us to select promising candidates which, together with candidates that are very different from the molecules already sampled, form the updated training sets of the surrogate models. In the course of 20 such training steps, we find a number of promising candidates, some being only variations on already known motifs and others being completely novel. Finally, we subject the six best such candidates to a computational reverse synthesis analysis to gauge their real-world synthesizability., (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

We study the electronic coupling between an adsorbate and a metal surface by calculating tunneling matrix elements Had directly from first principles. For this, we employ a projection of the Kohn-Sham Hamiltonian upon a diabatic basis using a version of the popular projection-operator diabatization approach. An appropriate integration of couplings over the Brillouin zone allows the first calculation of a size-convergent Newns-Anderson chemisorption function, a coupling-weighted density of states measuring the line broadening of an adsorbate frontier state upon adsorption. This broadening corresponds to the experimentally observed lifetime of an electron in the state, which we confirm for core-excited Ar*(2p3/2-14s) atoms on a number of transition metal (TM) surfaces. Yet, beyond just lifetimes, the chemisorption function is highly interpretable and encodes rich information on orbital phase interactions on the surface. The model thus captures and elucidates key aspects of the electron transfer process. Finally, a decomposition into angular momentum components reveals the hitherto unresolved role of the hybridized d-character of the TM surface in the resonant electron transfer and elucidates the coupling of the adsorbate to the surface bands over the entire energy scale., (© 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

The determination of depth profiles across interfaces is of primary importance in many scientific and technological areas. Photoemission spectroscopy is in principle well suited for this purpose, yet a quantitative implementation for investigations of liquid-vapor interfaces is hindered by the lack of understanding of electron-scattering processes in liquids. Previous studies have shown, however, that core-level photoelectron angular distributions (PADs) are altered by depth-dependent elastic electron scattering and can, thus, reveal information on the depth distribution of species across the interface. Here, we explore this concept further and show that the experimental anisotropy parameter characterizing the PAD scales linearly with the average distance of atoms along the surface normal obtained by molecular dynamics simulations. This behavior can be accounted for in the low-collision-number regime. We also show that results for different atomic species can be compared on the same length scale. We demonstrate that atoms separated by about 1 Å along the surface normal can be clearly distinguished with this method, achieving excellent depth resolution.

Smart, responsive materials are required in various advanced applications ranging from anti-counterfeiting to autonomous sensing. Colloidal crystals are a versatile material class for optically based sensing applications owing to their photonic stopband. A careful combination of materials synthesis and colloidal mesostructure rendered such systems helpful in responding to stimuli such as gases, humidity, or temperature. Here, an approach is demonstrated to simultaneously and independently measure the time and temperature solely based on the inherent material properties of complex colloidal crystal mixtures. An array of colloidal crystals, each featuring unique film formation kinetics, is fabricated. Combined with machine learning-enabled image analysis, the colloidal crystal arrays can autonomously record isothermal heating events - readout proceeds by acquiring photographs of the applied sensor using a standard smartphone camera. The concept shows how the progressing use of machine learning in materials science has the potential to allow non-classical forms of data acquisition and evaluation. This can provide novel insights into multiparameter systems and simplify applications of novel materials., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

With a view on adding to their use in trace gas sensing, we perform a combined experimental and theoretical study of the change of the conductivity of a metal organic framework (iron (1,2,3)-triazolate, Fe(ta) 2 ) with the uptake of chemically inert gases. To align our first-principles calculations with experimental measurements, we perform an ensemble average over different microscopic arrangements of the gas molecules in the pores of the metal-organic framework (MOF). Up to the experimentally reachable limit of gas uptake, we find a good agreement between both approaches. Thus, we can employ theory to further interpret our experimental results in terms of changes to the parameters of the Bardeen-Shockley band theory, electron-phonon coupling (in the form of the deformation potential), bulk modulus, and carrier effective mass. We find the first of these to be most strongly influenced through the gas uptake. Furthermore, we find the changes to the deformation potential to strongly depend on the individual microscopic arrangements of molecules in the pores of the MOF. This hints at a possible synthetic engineering of the material, e.g., by closing off certain pores, for a stronger, more interpretable electric response upon gas sorption.

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations to account for a surrounding liquid electrolyte on the level of a continuous polarizable medium. Originating in molecular chemistry with finite solutes, implicit solvation techniques are now increasingly used in the context of first-principles modeling of electrochemistry and electrocatalysis at extended (often metallic) electrodes. The prevalent ansatz to model the latter electrodes and the reactive surface chemistry at them through slabs in periodic boundary condition supercells brings its specific challenges. Foremost this concerns the difficulty of describing the entire double layer forming at the electrified solid-liquid interface (SLI) within supercell sizes tractable by commonly employed density functional theory (DFT). We review liquid solvation methodology from this specific application angle, highlighting in particular its use in the widespread ab initio thermodynamics approach to surface catalysis. Notably, implicit solvation can be employed to mimic a polarization of the electrode's electronic density under the applied potential and the concomitant capacitive charging of the entire double layer beyond the limitations of the employed DFT supercell. Most critical for continuing advances of this effective methodology for the SLI context is the lack of pertinent (experimental or high-level theoretical) reference data needed for parametrization.

The characterization of liquid-vapor interfaces at the molecular level is an important underpinning for a basic understanding of fundamental heterogeneous processes in many areas, such as atmospheric science. Here we use X-ray photoelectron spectroscopy to study the adsorption of a model surfactant, octanoic acid, at the water-gas interface. In particular, we examine the information contained in photoelectron angular distributions and show that information about the relative depth of molecules and functional groups within molecules can be obtained from these measurements. Focusing on the relative location of carboxylate (COO - ) and carboxylic acid (COOH) groups at different solution pH, the former is found to be immersed deeper into the liquid-vapor interface, which is confirmed by classical molecular dynamics simulations. These results help establish photoelectron angular distributions as a sensitive tool for the characterization of molecules at the liquid-vapor interface.

The structure and chemical composition are the key parameters influencing the properties of organic thin films deposited on inorganic substrates. Such films often display structures that substantially differ from the bulk, and the substrate has a relevant influence on their polymorphism. In this work, we illuminate the role of the substrate by studying its influence on para -benzoquinone on two different substrates, Ag(111) and graphene. We employ a combination of first-principles calculations and machine learning to identify the energetically most favorable structures on both substrates and study their electronic properties. Our results indicate that for the first layer, similar structures are favorable for both substrates. For the second layer, we find two significantly different structures. Interestingly, graphene favors the one with less, while Ag favors the one with more electronic coupling. We explain this switch in stability as an effect of the different charge transfer on the two substrates., Competing Interests: The authors declare no competing financial interest., (© 2022 The Authors. Published by American Chemical Society.)

Objective: To examine and better understand expectations and facilitators of satisfaction amongst patients presenting to an ambulatory urology clinic at an academic medical center., Methods: Patients completed an anonymous survey regarding expectations for their clinic visit. Patients were included in the investigation if they were aged 18-89 years and had the ability to complete informed consent. Chi-square analysis was then used to analyze the collected data., Results: A total of five hundred patients were enrolled in the study. Patients were predominantly white males and were older than 60 years of age. Most patients had at least a college education and drew an annual household income between $40,000-$99,999. Most enrollees were return patients (74.8%). Most expected to be seen within 3-7 days of referral and expected 16-30 minutes with their provider. Patients noted they would not be equally satisfied seeing a physician vs advanced practice provider on their initial visit but would on a return visit. About half (52%) of the cohort stated they would be dissatisfied with their clinic experience if their expectations were not met. Significance was found between variables including age, race, gender and type of visit and their survey responses., Conclusion: Patient satisfaction remains an important measure for the quality and safety of patient care. This investigation highlighted patient prioritization of time to be seen after referral and the provider that cares for them at both initial and follow-up visits. Future research is needed to enhance stakeholder understanding of precisely how expectations impact overall satisfaction., Competing Interests: CONFLICT OF INTEREST This information will be kept confidential. The Editor will discuss the method of disclosure of any potential conflict of interest with the corresponding author on an individual basis., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The multipole-expansion (MPE) model is an implicit solvation model used to efficiently incorporate solvent effects in quantum chemistry. Even within the recent direct approach, the multipole basis used in MPE to express the dielectric response still solves the electrostatic problem inefficiently or not at all for solutes larger than approximately ten non-hydrogen atoms. In existing MPE parametrizations, the resulting systematic underestimation of the electrostatic solute-solvent interaction is presently compensated for by a systematic overestimation of nonelectrostatic attractive interactions. Even though the MPE model can thus reproduce experimental free energies of solvation of small molecules remarkably well, the inherent error cancellation makes it hard to assign physical meaning to the individual free-energy terms in the model, raising concerns about transferability. Here we resolve this issue by solving the electrostatic problem piecewise in 3D regions centered around all non-hydrogen nuclei of the solute, ensuring reliable convergence of the multipole series. The resulting method thus allows for a much improved reproduction of the dielectric response of a medium to a solute. Employing a reduced nonelectrostatic model with a single free parameter, in addition to the density isovalue defining the solvation cavity, our method yields free energies of solvation of neutral, anionic, and cationic solutes in water in good agreement with experiment.

The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.

We address a long-standing ambiguity in the DFT-based projection-operator diabatization method for charge transfer couplings in donor-acceptor systems. It has long been known that the original method yields diabats which are not strictly fragment-localized due to mixing arising from basis-set orthogonalization. We demonstrate that this can contribute to a severe underestimation of coupling strengths and a spurious dependence on the choice of the basis set. As a remedy, we reformulate the method within a simple tight-binding model to generate diabats with increased localization, yielding a proper basis set convergence and improved performance for the general Hab11 benchmark set. Orthogonality of diabats is ensured either through symmetric Löwdin or asymmetric Gram-Schmid procedures, the latter of which offers to extend these improvements to asymmetric systems such as adsorbates on surfaces.

Zero strain insertion, high cycling stability, and a stable charge/discharge plateau are promising properties rendering Lithium Titanium Oxide (LTO) a possible candidate for an anode material in solid state Li ion batteries. However, the use of pristine LTO in batteries is rather limited due to its electronically insulating nature. In contrast, reduced LTO shows an electronic conductivity several orders of magnitude higher. Studying bulk reduced LTO, we could show recently that the formation of polaronic states can play a major role in explaining this improved conductivity. In this work, we extend our study toward the lithium-terminated LTO (111) surface. We investigate the formation of polarons by applying Hubbard-corrected density functional theory. Analyzing their relative stabilities reveals that positions with Li ions close by have the highest stability among the different localization patterns.

The treatment of electrostatic interactions is a key ingredient in the force field-based simulation of condensed phase systems. Most approaches used fixed, site-specific point charges. Yet, it is now clear that many applications of force fields (FFs) demand more sophisticated treatments, prompting the implementation of charge equilibration methods in polarizable FFs to allow the redistribution of charge within the system. One approach allowing both, charge redistribution and site-specific polarization, while at the same time solving methodological shortcomings of earlier methods, is the first-principles-derived atom-condensed Kohn-Sham density functional theory method approximated to the second order (ACKS2). In this work, we present two fragment approaches to ACKS2, termed f-ACKS2 and a self-consistent version, scf-ACKS2, that treat condensed phase systems as a collection of electronically polarizable molecular fragments. The fragmentation approach to ACKS2 not only leads to a more transferable and less system-specific collection of electronic response parameters but also opens up the method to large condensed phase systems. We validate the accuracies of f-ACKS2 and scf-ACKS2 by comparing polarization energies and induced dipole moments for a number of charged hydrocarbon dimers against DFT reference calculations. Finally, we also apply both fragmented ACKS2 variants to calculate the polarization energy for electron-hole pair separation along a chain of anthracene molecules and find excellent agreement with reference DFT calculations.

The metal-organic framework [Fe(ta) 2 ] (Hta = 1 H -1,2,3-triazole) containing Fe(II) ions and 1,2,3-triazolate ligands shows a reversible phase transition while retaining the cubic crystal symmetry and space group Fd 3 m (no. 227). The phase transition between room temperature (RT-[Fe(ta) 2 ]; a = 16.6315(2) Å, V = 4600.39(8) Å 3 ) and high temperature (HT-[Fe(ta) 2 ]; a = 17.7566(4) Å, V = 5598.6(1) Å 3 ) phases occurs at a temperature above 290 °C, whereas the phase transition between HT- and RT-[Fe(ta) 2 ] starts at a temperature below 210 °C. Both [Fe(ta) 2 ] polymorphs have identical bond topologies, but they differ by a large increase of the unit cell's volume of 22% for HT-[Fe(ta) 2 ]. The compounds are characterized by powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analyses. Additionally, Mössbauer spectroscopy, magnetic studies, and the electronic structure of both phases are discussed in detail with respect to the spin-crossover transition from the low-spin (RT-[Fe(ta) 2 ]) to the high-spin phase (HT-[Fe(ta) 2 ]).

Purpose: Latissimus dorsi (LD) breast reconstruction is of proven efficacy. Advantages of thoracodorsal nerve transection are potential prevention of muscle spasticity/movement; disadvantages are possible long-term muscle atrophy and volume loss. This study's purpose is to provide data that would support or refute nerve transection., Methods: A retrospective study of all LD breast reconstruction patients from 2011 to 2017 was done. Total number of flaps was identified, as was thoracodorsal nerve transection. Outcomes were noted for symptomatic muscle spasticity/involuntary movement, and complications inclusive of hematoma, seroma, and capsular contracture., Results: A total of 125 patients had 170 flaps. Eighty-one flaps had nerve transection; 89 did not. These cohorts had no differences in comorbidities, indications of surgery (cancer vs prophylactic), irradiation, delayed/immediate reconstruction, and use of expanders. Symptomatic muscle movement/spasticity was not significantly different: 3 (3.7%) of 78 in transection and 5 (5.6%) of 84 in nontransection (P = 0.55, χ). Incidence of seroma in the transection group was notably higher (18/81; 22% vs 12%) but not statistically significant (P = 0.09, χ). No differences existed in all other outcomes., Conclusions: Symptomatic spasticity or involuntary muscle movement occurs in a small number of patients with LD breast reconstruction and is not affected by thoracodorsal nerve transection. Movement after transection is likely due to aberrant nerve innervation and reinnervation. The absence of movement without transection is due to disruption of muscle position and origin after transfer. Seroma formation may be affected by increased axillary dissection required for nerve transection. These data do not support nerve transection, and therefore, it is not recommended.

Lithium titanium oxide Li 4 Ti 5 O 12 is an intriguing anode material promising particularly long-life batteries, due to its remarkable phase stability during (dis)charging of the cell. However, its usage is limited by its low intrinsic electronic conductivity. Introducing oxygen vacancies can be one method for overcoming this drawback, possibly by altering the charge carrier transport mechanism. We use Hubbard corrected density functional theory to show that polaronic states in combination with a possible hopping mechanism can play a crucial role in the experimentally observed increase in electronic conductivity. To gauge polaronic charge mobility, we compute the relative stabilities of different localization patterns and estimate polaron hopping barrier heights.

Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD), denoted OE62. Molecular equilibrium geometries are reported at the Perdew-Burke-Ernzerhof (PBE) level of density functional theory (DFT) including van der Waals corrections for all 62 k molecules. For these geometries, OE62 supplies total energies and orbital eigenvalues at the PBE and the PBE hybrid (PBE0) functional level of DFT for all 62 k molecules in vacuum as well as at the PBE0 level for a subset of 30,876 molecules in (implicit) water. For 5,239 molecules in vacuum, the dataset provides quasiparticle energies computed with many-body perturbation theory in the G 0 W 0 approximation with a PBE0 starting point (denoted GW5000 in analogy to the GW100 benchmark set (M. van Setten et al. J. Chem. Theory Comput. 12, 5076 (2016))).

In this work, we present general and robust transferable principles for the construction of quantum-mechanically treated clusters in a solid-state embedding (SSE) approach, beyond the still prevalent trial and error approach. Thereby, we probe the quality of different cluster shapes on the accuracy of chemisorption energies of small molecules and small polaron formation energies at the rutile TiO 2 (110) surface as test cases. Our analyses show that at least the binding energies and electronic structures in the form of the density of states tend to be quite robust already for small, nonoptimal cluster shapes. In contrast to that, the description of polaron formation can be dramatically influenced by the employed cluster geometry possibly leading to an erroneous energetic ordering or even to a wrong prediction of the polaronic states themselves. Our findings show that this is mainly caused by an inaccurate description of the Hartree potential at boundary and surrounding atoms, which are insufficiently compensated by the embedding environment. This stresses the importance of the cluster size and shape for the accuracy of general-purpose SSE models that do not have to be refitted for each new chemical question. Based on these observations, we derive some general design criteria for solid state embedded clusters.

The last 20 years of force field development have shown that even well parametrized classical models need to at least approximate the dielectric response of molecular systems-based, e.g., on atomic polarizabilities-in order to correctly render their structural and dynamic properties. Yet, despite great advances most approaches tend to be based on ad hoc assumptions and often insufficiently capture the dielectric response of the system to external perturbations, such as, e.g., charge carriers in semiconducting materials. A possible remedy was recently introduced with the atom-condensed Kohn-Sham density-functional theory approximated to second order (ACKS2), which is fully derived from first principles. Unfortunately, specifically its reliance on first-principles derived parameters so far precluded the widespread adoption of ACKS2. Opening up ACKS2 for general use, we here present a reformulation of the method in terms of Gaussian basis functions, which allows us to determine many of the ACKS2 parameters analytically. Two sets of parameters depending on exchange-correlation interactions are still calculated numerically, but we show that they could be straightforwardly parametrized owing to the smoothness of the new basis. Our approach exhibits three crucial benefits for future applications in force fields: i ) efficiency, ii ) accuracy, and iii ) transferability. We numerically validate our Gaussian augmented ACKS2 model for a set of small hydrocarbons which shows a very good agreement with density-functional theory reference calculations. To further demonstrate the method's accuracy and transferability for realistic systems, we calculate polarization responses and energies of anthracene and tetracene, two major building blocks in organic semiconductors.

Metal-organic frameworks (MOFs) are known for their vast design space of possible structures, covering a wide range of often porous crystal structures and physical properties. Electrical conductivity, though, was-until very recently-not a feature usually associated with MOFs. On the other hand, well defined porous media such as MOFs, showing some measure of conductivity, could find uses in a huge number of fields ranging from electrochemistry to electronics and sensing. In this work, we therefore investigate the different aspects contributing to the bad conductivity in MOFs. Using Bardeen-Shockley deformation potential theory, we devise an approach that allows us to gauge all factors influencing the conductivity, including the availability of free charge carriers and their mobility. The latter itself is determined by the effective masses of the charge carriers, the material's elastic constants, and the deformation potential constants, which measure an effective electron-phonon coupling. Based on these parameters, we study charge carrier mobility in metal (1,2,3)-triazolate MOF crystals, M(ta) 2 , where the metal is either iron, zinc, or ruthenium. Thereby, Zn(ta) 2 was experimentally shown to have little to no conductivity, while Fe(ta) 2 is one of the best currently known MOF semiconductors. Disregarding the fact that all three investigated MOFs show near-zero carrier densities due to their large bandgaps, our calculations reproduce the trends between Zn(ta) 2 and Fe(ta) 2 . In contrast to that we find the Ru(ta) 2 MOF, which to date has not been synthesized experimentally, to yield even better performance than iron triazolate. In summary, assuming, fox example, light doping to counter the large bandgap, our analysis of the factors influencing conductivity in MOFs allows us not only to confirm experimental trends but also to predict new, as yet unknown semiconducting MOF crystals.

We implemented the popular Hubbard density-functional theory + U (DFT+U) method in its spherically averaged form in the all-electron, full-potential DFT code FHI-aims. There, electronic states are expressed on a basis of highly localized numeric atomic orbitals (NAO), which straightforwardly lend themselves as projector functions for the DFT+U correction, yielding the necessary occupations of the correlated Hubbard subspace at no additional cost. We establish the efficacy of our implementation on the prototypical bulk NiO and obtain the well-known band gap opening effect of DFT+U. As a more stringent, real world test system, we then study polaron formation at the rutile TiO 2 (110) surface, where our results are in line with both experimental data as well as hybrid functional calculations. At this TiO 2 test system, yet in the bulk, we analyze some of the intricacies of using the DFT+U correction in a localized, numeric atomic orbital basis set. Specifically, we find that multiple localized radial basis functions of the same angular momentum can lead to highly erroneous predictions of ground-state properties. We also demonstrate a number of remedies to this problem. Finally, we highlight the critical influence of the exact choice of projector functions on DFT+U results using a number of projector functions of different spatial extent and composed of linear combinations of NAO basis functions. All of our efforts serve to highlight that, contrary to its deceptive ease of use, the DFT+U is far from the "black-box" approach it is sometimes made out to be.

Modern materials discovery and design studies often rely on the computational screening of large databases. Complementing experimental databases, virtual databases are thereby increasingly established through the first-principles calculation of computationally inexpensive, but for a given application, decisive microscopic quantities of the system. These so-called descriptors are calculated for vast numbers of candidate materials. In general, the sheer volume of datapoints generated in such studies precludes an in depth human analysis. To this end, smart visualization techniques, based e.g., on so-called chemical space networks (CSN), have been developed to extract general design rules connecting structural modifications to changes in the target functionality. In this work, we generate and visualize the CSN of possible crystalline organic semiconductors based on an in-house database of > 64,000 molecular crystals that we extracted from the exhaustive Cambridge Structural Database and for which we computed prominent charge-mobility descriptors. Our CSN thereby links clusters of molecular crystals based on the chemical similarity of the scaffolds of their molecular building blocks and thus groups communities of similar molecules. Including each cluster's median descriptor values, the CSN visualization not only reproduces known trends of good organic semiconductors but also allows us to extract general design rules for organic molecular scaffolds. Finally, the local environment of each scaffold in our visualization shows how thoroughly its local chemical space has already been explored synthetically. Of special interest here are those clusters with promising descriptor values, yet with little or no connections in the sampled chemical space, as these offer the most room for scaffold optimization.

In computer simulations of solvation effects on chemical reactions, continuum modeling techniques regain popularity as a way to efficiently circumvent an otherwise costly sampling of solvent degrees of freedom. As effective techniques, such implicit solvation models always depend on a number of parameters that need to be determined earlier. In the past, the focus lay mostly on an accurate parametrization of water models. Yet, non-aqueous solvents have recently attracted increasing attention, in particular, for the design of battery materials. To this end, we present a systematic parametrization protocol for the Self-Consistent Continuum Solvation (SCCS) model resulting in optimized parameters for 67 non-aqueous solvents. Our parametrization is based on a collection of ≈6000 experimentally measured partition coefficients, which we collected in the Solv@TUM database presented here. The accuracy of our optimized SCCS model is comparable to the well-known universal continuum solvation model (SMx) family of methods, while relying on only a single fit parameter and thereby largely reducing statistical noise. Furthermore, slightly modifying the non-electrostatic terms of the model, we present the SCCS-P solvation model as a more accurate alternative, in particular, for aromatic solutes. Finally, we show that SCCS parameters can, to a good degree of accuracy, also be predicted for solvents outside the database using merely the dielectric bulk permittivity of the solvent of choice.

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations, Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.

With the advent of efficient electronic structure methods, effective continuum solvation methods have emerged as a way to, at least partially, include solvent effects into simulations without the need for expensive sampling over solvent degrees of freedom. The multipole moment expansion (MPE) model, while based on ideas initially put forward almost 100 years ago, has recently been updated for the needs of modern electronic structure calculations. Indeed, for an all-electron code relying on localized basis sets and-more importantly-a multipole moment expansion of the electrostatic potential, the MPE method presents a particularly cheap way of solving the macroscopic Poisson equation to determine the electrostatic response of a medium surrounding a solute. In addition to our implementation of the MPE model in the FHI-aims electronic structure theory code [ Blum , V. ; Comput. Phys. Commun. 2009 , 180 , 2175 - 2196 , DOI: 10.1016/j.cpc.2009.06.022 ], we describe novel algorithms for determining equidistributed points on the solvation cavity-defined as a charge density isosurface-and the determination of cavity surface and volume from just this collection of points and their local density gradients. We demonstrate the efficacy of our model on an analytically solvable test case, against high-accuracy finite-element calculations for a set of ≈140000 2D test cases, and finally against experimental solvation free energies of a number of neutral and singly charged molecular test sets [ Andreussi , O. ; J. Chem. Phys. 2012 , 136 , 064102 , DOI: 10.1063/1.3676407 ; Marenich , A. V. ; Minnesota Solvation Database , Version 2012; University of Minnesota : Minneapolis, MN, USA , 2012 . ]. In all test cases we find that our MPE approach compares very well with given references at computational overheads < 20% and sometimes much smaller compared to a plain self-consistency cycle.

The booming field of molecular electronics has fostered a surge of computational research on electronic properties of organic molecular solids. In particular, with respect to a microscopic understanding of transport and loss mechanisms, theoretical studies assume an ever-increasing role. Owing to the tremendous diversity of organic molecular materials, a great number of computational methods have been put forward to suit every possible charge transport regime, material, and need for accuracy. With this review article we aim at providing a compendium of the available methods, their theoretical foundations, and their ranges of validity. We illustrate these through applications found in the literature. The focus is on methods available for organic molecular crystals, but mention is made wherever techniques are suitable for use in other related materials such as disordered or polymeric systems.

Implicit solvation calculations based on a Stern-layer corrected size-modified Poisson-Boltzmann (SMPB) model are an effective approach to capture electrolytic effects in first-principles electronic structure calculations. For a given salt solution, they require a range of ion-specific parameters, which describe the size of the dissolved ions as well as thickness and shape of the Stern layer. Out of this defined parameter space, we show that the Stern layer thickness expressed in terms of the solute's electron density and the resulting ionic cavity volume completely determine ion effects on the stability of neutral solutes. Using the efficient SMPB functionality of the full-potential density-functional theory package FHI-aims, we derive optimized such Stern layer parameters for neutral solutes in various aqueous monovalent electrolytes. The parametrization protocol relies on fitting to reference Setschenow coefficients that describe solvation free energy changes with ionic strength at low to medium concentrations. The availability of such data for NaCl solutions yields a highly predictive SMPB model that allows to recover the measured Setschenow coefficients with an accuracy that is comparable to prevalent quantitative regression models. Correspondingly derived SMPB parameters for other salts suffer from a much scarcer experimental data base but lead to Stern layer properties that follow a physically reasonable trend with ionic hydration numbers.

The reaction of small cationic tantalum clusters (Ta n + , n = 4-8) with molecular oxygen is studied under multi-collision conditions in the gas phase, and the reaction kinetics are analyzed in order to elucidate underlying mechanisms. Reaction pathways as well as relevant apparent rate constants are reported. Two principal pathways in the consecutive oxidation reaction are present in this size regime: solely oxidative degradation (loss of a TaO fragment) in the beginning followed by parallel intact oxidation of certain intermediate species. Selected product structures and energies are subsequently determined via density functional theory calculations. The branching between oxidative fragmentation and intact oxidation is related to the corresponding relaxation dynamics.

First-principles screening approaches exploiting energy trends in surface adsorption represent an unparalleled success story in recent computational catalysis research. Here we argue that our still limited understanding of the structure of active sites is one of the major bottlenecks towards an ever extended and reliable use of such computational screening for catalyst discovery. For low-index transition metal surfaces, the prevalently chosen high-symmetry (terrace and step) sites offered by the nominal bulk-truncated crystal lattice might be justified. For more complex surfaces and composite catalyst materials, computational screening studies will need to actively embrace a considerable uncertainty with respect to what truly are the active sites. By systematically exploring the space of possible active site motifs, such studies might eventually contribute towards a targeted design of optimized sites in future catalysts.

We explicitly calculate the free-energy barrier for the initial proton abstraction in the water splitting reaction at rutile TiO&#95;{2}(110) through ab initio molecular dynamics. Combining solid-state embedding, an energy based reaction coordinate and state-of-the-art free-energy reconstruction techniques renders the calculation tractable at the hybrid density-functional theory level. The obtained free-energy barrier of approximately 0.2 eV, depending slightly on the orientation of the first acceptor water molecule, suggests a hindered reaction on the pristine rutile surface.

Coupled-cluster theory with single, double, and perturbative triple excitations (CCSD(T)) is widely considered to be the "gold standard" of ab initio quantum chemistry. Using the domain-based pair natural orbital local correlation concept (DLPNO-CCSD(T)), these calculations can be performed on systems with hundreds of atoms at an accuracy of ∼99.9% of the canonical CCSD(T) method. This allows for ab initio calculations providing reference adsorption energetics at solid surfaces with an accuracy approaching 1 kcal/mol. This is an invaluable asset, not least for the assessment of density functional theory (DFT) as the prevalent approach for large-scale production calculations in energy or catalysis applications. Here we use DLPNO-CCSD(T) with embedded cluster models to compute entire adsorbate potential energy surfaces for the binding of a set of prototypical closed-shell molecules (H 2 O, NH 3 , CH 4 , CH 3 OH, CO 2 ) to the rutile TiO 2 (110) surface. The DLPNO-CCSD(T) calculations show excellent agreement with available experimental data, even for the "infamous" challenge of correctly predicting the CO 2 adsorption geometry. The numerical efficiency of the approach is within 1 order of magnitude of hybrid-level DFT calculations, hence blurring the borders between reference and production technique.