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Abstract In this first-principles investigation, we explore the polymorphic features of pseudo-cubic alloys, focusing on the impact of mixing organic and inorganic cations on their structural and electronic properties, configurational disorder, and thermodynamic stability. Employing an automated cluster expansion within the generalized quasichemical approximation (GQCA), our results reveal how the effective radius of the organic cation (r MA = 2.15 Å, r FA = 2.53 Å) and its dipole moment (μ MA = 2.15 D, μ FA = 0.25 D), influences Glazer’s rotations in the A1−x Cs x PbI3 (A = MA, FA) sublattice, with MA-based alloy presenting a higher critical temperature (527 K) and being stable for x > 0.60 above 200 K, while its FA analog has a lower critical temperature (427.7 K) and is stable for x

Abstract Materials with photoswitchable electronic properties and conductance values that can be reversibly changed over many orders of magnitude are highly desirable. Metal-organic framework (MOF) films functionalized with photoresponsive spiropyran molecules demonstrated the general possibility to switch the conduction by light with potentially large on-off-ratios. However, the fabrication of MOF materials in a trial-and-error approach is cumbersome and would benefit significantly from in silico molecular design. Based on the previous proof-of-principle investigation, here, we design photoswitchable MOFs which incorporate spiropyran photoswitches at controlled positions with defined intermolecular distances and orientations. Using multiscale modelling and automated workflow protocols, four MOF candidates are characterized and their potential for photoswitching the conductivity is explored. Using ab initio calculations of the electronic coupling between the molecules in the MOF, we show that lattice distances and vibrational flexibility tremendously modulate the possible conduction photoswitching between spiropyran- and merocyanine-based MOFs upon light absorption, resulting in average on-off ratios higher than 530 and 4200 for p- and n-conduction switching, respectively. Further functionalization of the photoswitches with electron-donating/-withdrawing groups is demonstrated to shift the energy levels of the frontier orbitals, permitting a guided design of new spiropyran-based photoswitches towards controlled modification between electron and hole conduction in a MOF.

Abstract HOMO and LUMO energies are critical molecular properties that typically require high accuracy computations for practical applicability. Until now, a comprehensive dataset containing sufficiently accurate HOMO and LUMO energies has been unavailable. In this study, we introduce a new dataset of HOMO/LUMO energies for QM9 compounds, calculated using the GW method. The GW method offers adequate HOMO/LUMO prediction accuracy for diverse applications, exhibiting mean unsigned errors of 100 meV in the GW100 benchmark dataset. This database may serve as a benchmark of HOMO/LUMO prediction, delta-learning, and transfer learning, particularly for larger molecules where GW is the most accurate but still numerically feasible method. We anticipate that this dataset will enable the development of more accurate machine learning models for predicting molecular properties.

Doping is widely adopted to make organic semiconductors more conductive, yet the impact of molecular electronic properties on doping performance is still not fully understood. Armleder et al. compute host-dopant interactions and show that a short-range overscreening effect strongly affects conductivity.

Simulations have become increasingly important to understand and design organic optoelectronic devices, such as organic light emitting diodes (OLEDs) and to optimize their performance by selecting appropriate materials and layer arrangements. To achieve accurate device simulations, it is crucial to consider the interplay between material properties, device architecture, and operating conditions and to incorporate physical processes such as charge injection, transport, recombination, and exciton decay. Simulations can provide insights into device bottlenecks and streamline optimization cycles, eliminating the need for physical prototyping and rationalizing OLED design. In this study, we investigated three heuristic OLED architectures with a 3D kinetic Monte Carlo (kMC) model and compared their quantum efficiency at different operation voltages. Our investigation focused on examining the effects of various layer arrangements on charge and exciton dynamics in OLED devices and establishing design principles for achieving high efficiency, which are consistent with experimental observations. Notably, we find that increasing the thickness of the emissive layer (EML) led to higher luminance efficiency, and that an emitter concentration of approximately 5% results in optimal performance. By using this model, it is possible to rapidly study the influence of many device parameters and explore a broad range of parameter and architecture space within a reasonable time-frame.

Direct laser writing is an effective technique for fabrication of complex 3D polymer networks using ultrashort laser pulses but to date it is difficult to obtain a time-resolved microscopic picture of the printing process in operando. Here, the use molecular dynamics simulation to model direct laser writing and investigate the effect of writing condition and aspect ratio on the mechanical properties of the printed polymer network.

The need for reduced dimensions of future devices pushes the limits of essential Si-based components and so alternative materials, such as carbon nanotubes or graphene, are being investigated as alternatives, but with new materials come new challenges. Here, the authors experimentally and theoretically investigate the on-currents for all-carbon transistors finding that contact spacing and length plays an important role in device performance.

Establishing a fundamental understanding of the nature of materials via computational simulation approaches requires knowledge from different areas, including physics, materials science, chemistry, mechanical engineering, mathematics, and computer science. Accurate modeling of the characteristics of a particular system usually involves multiple scales and therefore requires the combination of methods from various fields into custom-tailored simulation workflows. The typical approach to developing patch-work solutions on a case-to-case basis requires extensive expertise in scripting, command-line execution, and knowledge of all methods and tools involved for data preparation, data transfer between modules, module execution, and analysis. Therefore multiscale simulations involving state-of-the-art methods suffer from limited scalability, reproducibility, and flexibility. In this work, we present the workflow framework SimStack that enables rapid prototyping of simulation workflows involving modules from various sources. In this platform, multiscale- and multimodule workflows for execution on remote computational resources are crafted via drag and drop, minimizing the required expertise and effort for workflow setup. By hiding the complexity of high-performance computations on remote resources and maximizing reproducibility, SimStack enables users from academia and industry to combine cutting-edge models into custom-tailored, scalable simulation solutions.

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation.This overarching goal is achieved by working towards a number of key objectives:Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories.Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack.Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula.Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers.Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI.Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

The arrangement of organic semiconductor molecules in a material can be modulated using different supramolecular approaches, including the metal–organic framework (MOF) approach. These arrangements result in different frameworks topologies and structures. Fabrication of materials comprising optimized assemblies and functional molecules enables efficient tailoring of material properties, including electronic responses. Since semiconducting properties are sensitive to subtle changes in the nanostructure of the material, the exploitation of MOFs has promising potential in the development of new materials with designed structure and function. Based on decade-long method development, virtual design strategies have become ever more important, and such design methods profit from the availability of automated tools. Such tools enable screening of huge libraries of organic molecules in in silico models of the structure of three-dimensional nanoscale assemblies as the prerequisite to predict their functionality. In this report, we present and demonstrate the application of an automated workflow tool developed for MOFs of the primitive cubic (PCU) topology. We use pentacene-based ditopic linkers of a varied chemical composition and pillar linkers of different molecular sizes to automatically generate PCU MOFs, sample their structural dynamics at finite temperature, and predict electronic coupling matrix elements in vibrationally averaged assemblies. We demonstrate the change of the intermolecular ordering in the resulting MOFs and its impact on the semiconducting properties. This development lays the basis of an extendable framework to automatically model a wide variety of MOFs and characterize their function with respect to properties, such as conduction properties, absorption, and interaction with light. The developed workflow protocol and tools are available at https://github.com/KIT-Workflows/PCU-MOF.

Organic light emitting diodes (OLED) play an important role in commercial displays and are promising candidates for energy-efficient lighting applications. Although they have been continuously developed since their discovery in 1987, some unresolved challenges remain. The performance of OLEDs is determined by a multifaceted interplay of materials and device architectures. A commonly used technique to overcome the charge injection barrier from the electrodes to the organic layers, are doped injection layers. The optimization of doped injection layers is critical for high-efficiency OLED devices, but has been driven mainly by chemical intuition and experimental experience, slowing down the progress in this field. Therefore, computer-aided methods for material and device modeling are promising tools to accelerate the device development process. In this work, we studied the effect of doped hole injection layers on the injection barrier in dependence on material and layer properties by using a parametric kinetic Monte Carlo model. We were able to quantitatively elucidate the influence of doping concentration, material properties, and layer thickness on the injection barrier and device conductivity, leading to the conclusion that our kMC model is suitable for virtual device design.

Accurate prediction of the ionization potential and electron affinity energies of small molecules are important for many applications. Density functional theory (DFT) is computationally inexpensive, but can be very inaccurate for frontier orbital energies or ionization energies. The GW method is sufficiently accurate for many relevant applications, but much more expensive than DFT. Here we study how we can learn to predict orbital energies with GW accuracy using machine learning (ML) on molecular graphs and fingerprints using an interpretable delta-learning approach. ML models presented here can be used to predict quasiparticle energies of small organic molecules even beyond the size of the molecules used for training. We furthermore analyze the learned DFT-to-GW corrections by mapping them to specific localized fragments of the molecules, in order to develop an intuitive interpretation of the learned corrections, and thus to better understand DFT errors.

Organic semiconductors (OSC) are key components in applications such as organic photovoltaics, organic sensors, transistors and organic light emitting diodes (OLED). OSC devices, especially OLEDs, often consist of multiple layers comprising one or more species of organic molecules. The unique properties of each molecular species and their interaction determine charge transport in OSCs—a key factor for device performance. The small charge carrier mobility of OSCs compared to inorganic semiconductors remains a major limitation of OSC device performance. Virtual design can support experimental R&D towards accelerated R&D of OSC compounds with improved charge transport. Here we benchmark a de novo multiscale workflow to compute the charge carrier mobility solely on the basis of the molecular structure: We generate virtual models of OSC thin films with atomistic resolution, compute the electronic structure of molecules in the thin films using a quantum embedding procedure and simulate charge transport with kinetic Monte-Carlo protocol. We show that for 15 common amorphous OSC the computed zero-field and field-dependent mobility are in good agreement with experimental data, proving this approach to be an effective virtual design tool for OSC materials and devices.

Though conductivity doping in organic semiconductors has been widely studied in organic electronics, a clear mechanistic picture that explains the phenomenon is still lacking. Here, the authors report a theoretical approach to elucidate the role of disorder compensation in doped organic materials.

Abstract Recent progress in the improvement of organic solar cells lead to a power conversion efficiency to over 16%. One of the key factors for this improvement is a more favorable energy level alignment between donor and acceptor materials, which demonstrates that the properties of interfaces between donor and acceptor regions are of paramount importance. Recent investigations showed a significant dependence of the energy levels of organic semiconductors upon admixture of different materials, but its origin is presently not well understood. Here, we use multiscale simulation protocols to investigate the molecular origin of the mixing induced energy level shifts and show that electrostatic properties, in particular higher-order multipole moments and polarizability determine the strength of the effect. The findings of this study may guide future material-design efforts in order to improve device performance by systematic modification of molecular properties.

In molecular solids, photoluminescence of dye molecules is often suppressed owing to excitonic coupling with adjacent chromophores. Here the authors use a computational method to predict the optimal alignment of naphthalenediimide linkers in metal–organic frameworks to afford J-aggregates, and demonstrate the same by fabricating highly photoluminescent thin film.

Molecular simulations such as Molecular Dynamics (MD) and Monte Carlo (MC) have gained increasing importance in the explanation of various physicochemical and biochemical phenomena in soft matter and help elucidate processes that often cannot be understood by experimental techniques alone. While there is a large number of computational studies and developments in MD, MC simulations are less widely used, but they offer a powerful alternative approach to explore the potential energy surface of complex systems in a way that is not feasible for atomistic MD, which still remains fundamentally constrained by the femtosecond timestep, limiting investigations of many essential processes. This paper provides a review of the current developments of a MC based code, SIMONA, which is an efficient and versatile tool to perform large-scale conformational sampling of different kinds of (macro)molecules. We provide an overview of the approach, and an application to soft-matter problems, such as protocols for protein and polymer folding, physical vapor deposition of functional organic molecules and complex oligomer modeling. SIMONA offers solutions to different levels of programming expertise (basic, expert and developer level) through the usage of a designed Graphical Interface pre-processor, a convenient coding environment using XML and the development of new algorithms using Python/C++. We believe that the development of versatile codes which can be used in different fields, along with related protocols and data analysis, paves the way for wider use of MC methods. SIMONA is available for download under http://int.kit.edu/nanosim/simona.

A critical barrier in the treatment of endosomal and lysosomal diseases is the lack of understanding of the in vivo functions of the putative causative genes. We addressed this by investigating a key pair of endocytic adaptor proteins, PH domain-containing endocytic trafficking adaptor 1 and 2 (PHETA1/2; also known as FAM109A/B, Ses1/2, IPIP27A/B), which interact with the protein product of OCRL, the causative gene for Lowe syndrome. Here, we conducted the first study of PHETA1/2 in vivo, utilizing the zebrafish system. We found that impairment of both zebrafish orthologs, pheta1 and pheta2, disrupted endocytosis and ciliogenesis in renal tissues. In addition, pheta1/2 mutant animals exhibited reduced jaw size and delayed chondrocyte differentiation, indicating a role in craniofacial development. Deficiency of pheta1/2 resulted in dysregulation of cathepsin K, which led to an increased abundance of type II collagen in craniofacial cartilages, a marker of immature cartilage extracellular matrix. Cathepsin K inhibition rescued the craniofacial phenotypes in the pheta1/2 double mutants. The abnormal renal and craniofacial phenotypes in the pheta1/2 mutant animals were consistent with the clinical presentation of a patient with a de novo arginine (R) to cysteine (C) variant (R6C) of PHETA1. Expressing the patient-specific variant in zebrafish exacerbated craniofacial deficits, suggesting that the R6C allele acts in a dominant-negative manner. Together, these results provide insights into the in vivo roles of PHETA1/2 and suggest that the R6C variant is contributory to the pathogenesis of disease in the patient. This article has an associated First Person interview with the first author of the paper.

Exciton diffusion length and directionality are important parameters in artificial photosynthetic devices. Here, the authors present a way to make crystalline chromophore assemblies with bespoke architecture, fabricating one exhibiting anisotropic exciton transport properties.

One major limitation for the vascularization of bone substitutes used for filling is the presence of mineral blocks. The newly-formed blood vessels are stopped or have to circumvent the mineral blocks, resulting in inefficient delivery of oxygen and nutrients to the implant. This leads to necrosis within the implant and to poor engraftment of the bone substitute. The aim of the present study is to provide a bone substitute currently used in the clinic with suitably guided vascularization properties. This therapeutic hybrid bone filling, containing a mineral and a polymeric component, is fortified with pro-angiogenic smart nano-therapeutics that allow the release of angiogenic molecules. Our data showed that the improved vasculature within the implant promoted new bone formation and that the newly-formed bone swapped the mineral blocks of the bone substitutes much more efficiently than in non-functionalized bone substitutes. Therefore, we demonstrated that our therapeutic bone substitute is an advanced therapeutical medicinal product, with great potential to recuperate and guide vascularization that is stopped by mineral blocks, and can improve the regeneration of critical-sized bone defects. We have also elucidated the mechanism to understand how the newly-formed vessels can no longer encounter mineral blocks and pursue their course of vasculature, giving our advanced therapeutical bone filling great potential to be used in many applications, by combining filling and nano-regenerative medicine that currently fall short because of problems related to the lack of oxygen and nutrients.

Metal organic frameworks have emerged as an important new class of materials with many applications, such as sensing, gas separation, drug delivery. In many cases, their performance is limited by structural defects, including vacancies and domain boundaries. In the case of MOF thin films, surface roughness can also have a pronounced influence on MOF-based device properties. Presently, there is little systematic knowledge about optimal growth conditions with regard to optimal morphologies for specific applications. In this work, we simulate the layer-by-layer (LbL) growth of the HKUST-1 MOF as a function of temperature and reactant concentration using a coarse-grained model that permits detailed insights into the growth mechanism. This model helps to understand the morphological features of HKUST-1 grown under different conditions and can be used to predict and optimize the temperature for the purpose of controlling the crystal quality and yield. It was found that reactant concentration affects the mass deposition rate, while its effect on the crystallinity of the generated HKUST-1 film is less pronounced. In addition, the effect of temperature on the surface roughness of the film can be divided into three regimes. Temperatures in the range from 10 to 129 °C allow better control of surface roughness and film thickness, while film growth in the range of 129 to 182 °C is characterized by a lower mass deposition rate per cycle and rougher surfaces. Finally, for T larger than 182 °C, the film grows slower, but in a smooth fashion. Furthermore, the potential effect of temperature on the crystallinity of LbL-grown HKUST-1 was quantified. To obtain high crystallinity, the operating temperature should preferably not exceed 57 °C, with an optimum around 28 °C, which agrees with experimental observations.

We have studied the electronic properties and the charge carrier mobility of the organic semiconductor tris(1-oxo-1H-phenalen-9-olate)aluminium(III) (Al(Op)3) both experimentally and theoretically. We experimentally estimated the HOMO and LUMO energy levels to be −5.93 and −3.26 eV, respectively, which were close to the corresponding calculated values. Al(Op)3 was successfully evaporated onto quartz substrates and was clearly identified in the absorption spectra of both the solution and the thin film. A structured steady state fluorescence emission was detected in solution, whereas a broad, red-shifted emission was observed in the thin film. This indicates the formation of excimers in the solid state, which is crucial for the transport properties. The incorporation of Al(Op)3 into organic thin film transistors (TFTs) was performed in order to measure the charge carrier mobility. The experimental setup detected no electron mobility, while a hole mobility between 0.6 × 10−6 and 2.1 × 10−6 cm2·V−1·s−1 was measured. Theoretical simulations, on the other hand, predicted an electron mobility of 9.5 × 10−6 cm2·V−1·s−1 and a hole mobility of 1.4 × 10−4 cm2·V−1·s−1. The theoretical simulation for the hole mobility predicted an approximately one order of magnitude higher hole mobility than was observed in the experiment, which is considered to be in good agreement. The result for the electron mobility was, on the other hand, unexpected, as both the calculated electron mobility and chemical common sense (based on the capability of extended aromatic structures to efficiently accept and delocalize additional electrons) suggest more robust electron charge transport properties. This discrepancy is explained by the excimer formation, whose inclusion in the multiscale simulation workflow is expected to bring the theoretical simulation and experiment into agreement.

The creation of magnetic storage devices by decoration of a graphene sheet by magnetic transition-metal adatoms, utilizing the high in-plane versus out-of-plane magnetic anisotropy energy (MAE), has recently been proposed. This concept is extended in our density-functional-based modeling study by incorporating the influence of the graphene edge on the MAE. We consider triangular graphene flakes with both armchair and zigzag edges in which a single ruthenium adatom is placed at symmetrically inequivalent positions. Depending on the edge-type, the graphene edge was found to influence the MAE in opposite ways: for the armchair flake the MAE increases close to the edge, while the opposite is true for the zigzag edge. Additionally, in-plane pinning of the magnetization direction perpendicular to the edge itself is observed for the first time.

E(rns) is an essential virion glycoprotein with RNase activity that suppresses host cellular innate immune responses upon being partially secreted from the infected cells. Its unusual C-terminus plays multiple roles, as the amphiphilic helix acts as a membrane anchor, as a signal peptidase cleavage site, and as a retention/secretion signal. We analyzed the structure and membrane binding properties of this sequence to gain a better understanding of the underlying mechanisms. CD spectroscopy in different setups, as well as Monte Carlo and molecular dynamics simulations confirmed the helical folding and showed that the helix is accommodated in the amphiphilic region of the lipid bilayer with a slight tilt rather than lying parallel to the surface. This model was confirmed by NMR analyses that also identified a central stretch of 15 residues within the helix that is fully shielded from the aqueous layer, which is C-terminally followed by a putative hairpin structure. These findings explain the strong membrane binding of the protein and provide clues to establishing the E(rns) membrane contact, processing and secretion.

Hydrophobins are amphiphilic proteins able to self-assemble at water-air interphases and are only found in filamentous fungi. In Aspergillus nidulans two hydrophobins, RodA and DewA, have been characterized, which both localize on the conidiospore surface and contribute to its hydrophobicity. RodA is the constituent protein of very regularly arranged rodlets, 10 nm in diameter. Here we analyzed four more hydrophobins, DewB-E, in A. nidulans and found that all six hydrophobins contribute to the hydrophobic surface of the conidiospores but only deletion of rodA caused loss of the rodlet structure. Analysis of the rodlets in the dewB-E deletion strains with atomic force microscopy revealed that the rodlets appeared less robust. Expression of DewA and DewB driven from the rodA promoter and secreted with the RodA secretion signal in a strain lacking RodA, restored partly the hydrophobicity. DewA and B were able to form rodlets to some extent but never reached the rodlet structure of RodA. The rodlet-lacking rodA-deletion strain opens the possibility to systematically study rodlet formation of other natural or synthetic hydrophobins.

The molecular chaperone GRP78/BiP is a key regulator of protein folding in the endoplasmic reticulum, and it plays a pivotal role in cancer cell survival and chemoresistance. Inhibition of its function has therefore been an important strategy for inhibiting tumor cell growth in cancer therapy. Previous efforts to achieve this goal have used peptides that bind to GRP78/BiP conjugated to pro-drugs or cell-death-inducing sequences. Here, we describe a peptide that induces prostate tumor cell death without the need of any conjugating sequences. This peptide is a sequence derived from the cochaperone Bag-1. We have shown that this sequence interacts with and inhibits the refolding activity of GRP78/BiP. Furthermore, we have demonstrated that it modulates the unfolded protein response in ER stress resulting in PARP and caspase-4 cleavage. Prostate cancer cells stably expressing this peptide showed reduced growth and increased apoptosis in in vivo xenograft tumor models. Amino acid substitutions that destroyed binding of the Bag-1 peptide to GRP78/BiP or downregulation of the expression of GRP78 compromised the inhibitory effect of this peptide. This sequence therefore represents a candidate lead peptide for anti-tumor therapy.