DFTB+, a software package for efficient approximate density functional theory based atomistic simulations

Engineering Trifluoromethyl Groups in Porous Coordination Polymers to Enhance Stability and Regulate Pore Window for Hexane Isomers Separation

Effective separation of hexane (C6) isomers is critical for a variety of industrial applications but conventional distillation methods are energy-intensive. Adsorptive separations based on porous coordination polymers (PCPs) offer a promising alternative due to their exceptional porosity and tunable properties. However, there is still an urgent need to develop PCPs with high stability and separation performance. This study investigates how substituting a methyl (-CH3) group with a trifluoromethyl (-CF3) group can regulate pores and hydrophobicity in PCPs. This precise adjustment aims to enhance stability and improve the kinetic separation performance of hydrophobic C6 isomers by considering the size and hydrophobicity of the trifluoromethyl group. Two isostructural PCPs with pcu topology, PCP-CH3 and PCP-CF3, were synthesized to vary pore diameters and hydrophobicity based on the presence of -CH3 or -CF3 groups. PCP-CF3 showed greater stability in water compared to PCP-CH3. While PCP-CH3 had high adsorption capacities, it lacked selectivity, whereas PCP-CF3 demonstrated improved selectivity, particularly in excluding dibranched isomers. Dynamic column separation experiments revealed that PCP-CF3 could selectively adsorb linear and monobranched isomers over dibranched isomers at room temperature. These findings highlight the potential of fluorine-modified PCPs for efficient isomer separation and underscore the importance of stability improvement strategies.

Large-Area Transfer of Nanometer-Thin C60 Films

Fullerenes, with well-defined molecular structures and high scalability, hold promise as fundamental building blocks for creating a variety of carbon materials. The fabrication and transfer of large-area films with precisely controlled thicknesses and morphologies on desired surfaces are crucial for designing and developing fullerene-based materials and devices. In this work, we present strategies for solid-state transferring C60 molecular nanometer-thin films, with dimensions of centimeters in lateral size and thicknesses controlled in the range of 1-20 nm, onto various substrates. Furthermore, we have successfully fabricated centimeter-wide graphene/C60/graphene heterostructures through layer-by-layer stacking of C60 and graphene films. This transfer methodology is versatile, allowing for the complete transfer of chemically modified C60 films, including oxygenated C60 films and C60Pdn organometallic polymer films. Additionally, direct solid-state transfer of C60 and C60Pdn films onto electrode surfaces has enabled their electrocatalytic performance for the hydrogen evolution reaction to be probed directly. This thin-film transfer strategy allows precise manipulation of large-area, ultrathin C60 films on various substrates, providing a platform for fullerene chemistry and the experimental synthesis of artificial carbon structures.

Understanding the synergistic interaction between a 1,3,4-thiadiazole derivative and amphotericin B using spectroscopic and theoretical studies

We present a comprehensive spectroscopic study supported by theoretical quantum chemical calculations conducted on a molecular system (4-(5-methyl-1,3,4-thiadiazol-2-yl)benzene-1,3-diol (C1) and the antibiotic Amphotericin B (AmB)) that exhibits highly synergistic properties. We previously reported the strong synergism of this molecular system and now wish to present related stationary measurements of UV-Vis absorption, fluorescence, and fluorescence anisotropy in a polar, aprotic solvent (DMSO and a PBS buffer), followed by time-resolved fluorescence intensity and anisotropy decay studies using different ratios of the selected 1,3,4-thiadiazole derivative to Amphotericin B. Absorption spectra measured for the system revealed discrepancies in terms of the shapes of absorption bands, particularly in PBS. Fluorescence emission spectra revealed that the addition of C1 molecules triggered significant changes in the emission spectra of the system. Measurements of the fluorescence lifetimes and fluorescence anisotropy supported by synchronous spectra clearly showed evidence of disaggregation. The AmB molecular aggregates indicated interaction of C1 with the antibiotic at points responsible for the formation of dimer structures. The spectroscopic results were further corroborated, analyzed, and interpreted using the methods of quantum mechanical modelling. Analyses based on the density functional tight-binding and time-dependent density functional theory confirmed that molecular interactions between "small" molecules and AmB lead to a significant increase in the clinical efficacy of the antibiotic.

Photoinduced Electron-Nuclear Dynamics of Fullerene and Its Monolayer Networks in Solvated Environments

The recently synthesized monolayer fullerene network in a quasi-hexagonal phase (qHP-C60) exhibits superior electron mobility and optoelectronic properties compared to molecular fullerene (C60), making it highly promising for a variety of applications. However, the microscopic carrier dynamics of qHP-C60 remain unclear, particularly in realistic environments, which are of significant importance for applications in optoelectronic devices. Unfortunately, traditional ab initio methods are prohibitive for capturing the real-time carrier dynamics of such large systems due to their high computational cost. In this work, we present the first real-time electron-nuclear dynamics study of qHP-C60 using velocity-gauge density functional tight binding, which enables us to perform several picoseconds of excited-state electron-nuclear dynamics simulations for nanoscale systems with periodic boundary conditions. When applied to C60, qHP-C60, and their solvated counterparts, we demonstrate that water/moisture significantly increases the electron-hole recombination time in C60 but has little impact on qHP-C60. Our excited-state electron-nuclear dynamics calculations show that qHP-C60 is extremely unique and enable exploration of time-resolved dynamics for understanding excited-state processes of large systems in complex, solvated environments.

Susceptibility formulation of density matrix perturbation theory

Density matrix perturbation theory based on recursive Fermi-operator expansions provides a computationally efficient framework for time-independent response calculations in quantum chemistry and materials science. From a perturbation in the Hamiltonian, we can calculate the first-order perturbation in the density matrix, which then gives us the linear response in the expectation values for some chosen set of observables. We present an alternative, dual formulation, where we instead calculate the static susceptibility of an observable, which then gives us the linear response in the expectation values for any number of different Hamiltonian perturbations. We show how the calculation of the susceptibility can be performed with the same expansion schemes used in recursive density matrix perturbation theory, including generalizations to fractional occupation numbers and self-consistent linear response calculations, i.e., similar to density functional perturbation theory. As with recursive density matrix perturbation theory, the dual susceptibility formulation is well suited for numerically thresholded sparse matrix algebra, which has linear scaling complexity for sufficiently large sparse systems. Similarly, the recursive computation of the susceptibility also seamlessly integrates with the computational framework of deep neural networks used in artificial intelligence (AI) applications. This integration enables the calculation of quantum response properties that can leverage cutting-edge AI-hardware, such as NVIDIA Tensor Cores or Google Tensor Processing Units. We demonstrate performance for recursive susceptibility calculations using NVIDIA Graphics Processing Units and Tensor Cores.

Canonical coupled cluster binding benchmark for nanoscale noncovalent complexes at the hundred-atom scale

In this study, we introduce two datasets for nanoscale noncovalent binding, featuring complexes at the hundred-atom scale, benchmarked using coupled cluster with single, double, and perturbative triple [CCSD(T)] excitations extrapolated to the complete basis set (CBS) limit. The first dataset, L14, comprises 14 complexes with canonical CCSD(T)/CBS benchmarks, extending the applicability of CCSD(T)/CBS binding benchmarks to systems as large as 113 atoms. The second dataset, vL11, consists of 11 even larger complexes, evaluated using the local CCSD(T)/CBS method with stringent thresholds, covering systems up to 174 atoms. We compare binding energies obtained from local CCSD(T) and fixed-node diffusion Monte Carlo (FN-DMC), which have previously shown discrepancies exceeding the chemical accuracy threshold of 1 kcal/mol in large complexes, with the new canonical CCSD(T)/CBS results. While local CCSD(T)/CBS agrees with canonical CCSD(T)/CBS within binding uncertainties, FN-DMC consistently underestimates binding energies in π-π complexes by over 1 kcal/mol. Potential sources of error in canonical CCSD(T)/CBS are discussed, and we argue that the observed discrepancies are unlikely to originate from CCSD(T) itself. Instead, the fixed-node approximation in FN-DMC warrants further investigation to elucidate these binding discrepancies. Using these datasets as reference, we evaluate the performance of various electronic structure methods, semi-empirical approaches, and machine learning potentials for nanoscale complexes. Based on computational accuracy and stability across system sizes, we recommend MP2+aiD(CCD), PBE0+D4, and ωB97X-3c as reliable methods for investigating noncovalent interactions in nanoscale complexes, maintaining their promising performance observed in smaller systems.

Insights into Carvone: Fatty Acid Hydrophobic NADES for Alkane Solubilization

The urge to adopt cleaner technologies drives the search for novel and sustainable materials such as Hydrophobic Natural Deep Eutectic Solvents (HNADESs), a new class of green solvents characterized by their low toxicity, biodegradability, and tunable properties, aiming to be applied in various fields for handling non-polar substances. In this work, the solubilization of hydrocarbons in type V HNADESs (non-ionic organic molecules) formed by mixing carvone, a natural monoterpenoid, with organic acids (hexanoic to decanoic acids) is examined by applying both experimental and theoretical approaches. The synthesis and physicochemical characterization of different HNADESs allowed us to tailor their properties, aiming for optimal interactions with desired hydrocarbons. The solubilization of hydrocarbons in CAR:C10AC (1:1) HNADES is evaluated in terms of HNADES content, temperature, and the structure of the hydrocarbon itself (C6, C10, and C14 being the selected ones). To gain deeper insights into the underlying mechanisms of interactions between the solvents and the alkanes, a comprehensive multiscale computational study was carried out to analyze the nature of the interactions, the changes upon formation of HNADESs and hydrocarbon solubilization in the fluid's nanostructure, and the possible toxicological effects of the solvents. The findings hold the potential to significantly impact the realm of hydrocarbon exploration and utilization.

Equipartitioning of Molecular Degrees of Freedom in MD Simulations of Gaseous Systems via an Advanced Thermostatization Strategy

This work introduces a dedicated thermostatization strategy for molecular dynamics simulations of gaseous systems. The proposed thermostat is based on the stochastic canonical velocity rescaling approach by Bussi and co-workers and is capable of ensuring an equal distribution of the kinetic energy among the translational, rotational, and vibrational degrees of freedom. The outlined framework ensures the correct treatment of the kinetic energy in gaseous systems, which is typically not the case in standard approaches due to the limited number of collisions between particles associated with a large free mean path. Additionally, an efficient strategy to effectively correct for intramolecular contributions to the virial in quantum mechanical simulations is presented. The equipartitioning thermostat was successfully tested by the determination of pV diagrams for carbon dioxide and methane at the density functional tight binding level of theory. The results unequivocally demonstrate that the equipartitioning thermostat can effectively achieve an equal distribution of the kinetic energy among the different degrees of freedom, thereby ensuring correct pressure in gaseous systems. Furthermore, RDF calculations show the capability of the proposed method to accurately depict the structure of gaseous systems, as well as enable an adequate treatment of gas molecules under confinement, as exemplified by an MD simulation of (CO2)50@MOF-5.

Exploring the electronic and mechanical properties of the recently synthesized nitrogen-doped amorphous monolayer carbon

The recent synthesis of nitrogen-doped amorphous monolayer carbon (NAMC) opens new possibilities for multifunctional materials. In this study, we have investigated the nitrogen doping limits and their effects on NAMC's structural and electronic properties using density functional-based tight-binding simulations. Our results show that NAMC remains stable up to 35% nitrogen doping, beyond which the lattice becomes unstable. The formation energies of NAMC are higher than those of nitrogen-doped graphene for all the cases we have investigated. Both undoped MAC and NAMC exhibit metallic behavior, although only MAC features a Dirac-like cone. MAC has an estimated Young's modulus value of about 410 GPa, while NAMC's modulus can vary around 416 GPa depending on nitrogen content. MAC displays optical activity in the ultraviolet range, whereas NAMC features light absorption within the infrared and visible ranges, suggesting potential for distinct optoelectronic applications. Their structural thermal stabilities were addressed through molecular dynamics simulations. MAC melts at approximately 4900 K, while NAMC loses its structural integrity for temperatures ranging from 300 K to 3300 K, lower than graphene. These results point to potential NAMC applications in flexible electronics and optoelectronics.

Mapping the Extended Ground State Reactivity Landscape of a Photoswitchable Molecule at a Single Molecular Level

Photoswitchable molecules with structural flexibility can exhibit a complex ground state potential energy landscape due to the accessibility of multiple metastable states at merely low energy barriers. However, conventional bulk analytical techniques are limited in their ability to probe these metastable ground states and their relative energies. This is partially due to the difficulty of inducing changes in small molecules in their ground state, as they do not respond to external stimuli, such as mechanical force, unless they are incorporated into larger polymer networks. This hinders the understanding of ground-state reactivity and the associated dynamics. In this study, we leverage the "perturb-probe" capability of the single molecular break junction technique to explore the ground state 6π electrocyclization of a dithienylethene (DTE) derivative, a process traditionally achieved through electro- or photochromism. Our findings reveal that this reaction can also be triggered by mechanical force and an oriented electric field at the single-molecule level via ground state dynamics. We demonstrated that external perturbations could control the ground state reaction dynamics and steer the reaction trajectories away from constraints imposed by typical excited state dynamics. This strategy will thus offer access to a whole new dimension of single molecular electromechanical conversions and extend our knowledge of the ground state potential energy surface available to molecules under external force fields.

Advanced Charge Extended Hückel (CEH) Model and a Consistent Adaptive Minimal Basis Set for the Elements Z = 1-103

The Charge Extended Hückel (CEH) model, initially introduced for adaptive atomic orbital (AO) basis set construction (J. Chem. Phys. 2023, 159, 164108), has been significantly revised to enhance accuracy and robustness, particularly in challenging electronic situations. This revision includes an extension toward f-elements, covering actinoids with their f-electrons in the valence space. We present a novel noniterative approximation for the electrostatic contribution to the effective Fock matrix, which substantially improves performance in polar or charged systems. Additionally, the training data set for elements Z = 1-103 has been expanded to encompass even more chemically diverse reference molecules as well as dipole moments and shell populations in addition to atomic charges. It includes a greater variety of "mindless" molecules (MLMs) as well as more complex electronic structures through open-shell and highly charged species. The revised method achieves mean absolute errors for atomic charges q of approximately 0.02 e- for randomly selected (mostly organic) molecules and 0.09 e- for MLMs, outperforming both classical charge models and established tight-binding methods. Furthermore, the revised CEH model has been validated through density functional theory calculations with the updated adaptive q-vSZP AO basis set on common thermochemical databases. Consistent with the extension of the CEH model, q-vSZP has also been variationally optimized and tested for elements Z = 58-71 and 87-103. The original versions of both CEH and q-vSZP are now considered deprecated.

Heavy-boundary mode patterning and dynamics of topological phonons in polymer chains and supramolecular lattices on surfaces

In topological band theory, phonon boundary modes consequence of a topologically non-trivial  band structure feature desirable properties for atomically-precise technologies, such as robustness against defects, waveguiding, and one-way transport. These topological phonon boundary modes remain to be studied both theoretically and experimentally in synthetic materials, such as polymers and supramolecular assemblies at the atomistic level under thermal fluctuations. Here we show by means of molecular simulations, that surface-confined Su-Schrieffer-Heeger (SSH) phonon analogue models express robust topological phonon boundary modes at heavy boundaries and under thermal fluctuations. The resulting bulk-heavy boundary correspondence enables patterning of boundary modes in polymer chains and weakly-interacting supramolecular lattices. Moreover, we show that upon excitation of a single molecule, propagation along heavy-boundary modes differs from free boundary modes. Our work is an entry to topological vibrations in supramolecular systems, and may find applications in the patterning of phonon circuits and realization of Hall effect phonon analogues at the molecular scale.

Non-adiabatic Couplings in Surface Hopping with Tight Binding Density Functional Theory: The Case of Molecular Motors

Nonadiabatic molecular dynamics (NAMD) has become an essential computational technique for studying the photophysical relaxation of molecular systems after light absorption. These phenomena require approximations that go beyond the Born-Oppenheimer approximation, and the accuracy of the results heavily depends on the electronic structure theory employed. Sophisticated electronic methods, however, make these techniques computationally expensive, even for medium size systems. Consequently, simulations are often performed on simplified models to interpret the experimental results. In this context, a variety of techniques have been developed to perform NAMD using approximate methods, particularly density functional tight binding (DFTB). Despite the use of these techniques on large systems, where ab initio methods are computationally prohibitive, a comprehensive validation has been lacking. In this work, we present a new implementation of trajectory surface hopping combined with DFTB, utilizing nonadiabatic coupling vectors. We selected the methaniminium cation and furan systems for validation, providing an exhaustive comparison with the higher-level electronic structure methods. As a case study, we simulated a system from the class of molecular motors, which has been extensively studied experimentally but remains challenging to simulate with ab initio methods due to its inherent complexity. Our approach effectively captures the key photophysical mechanism of dihedral rotation after the absorption of light. Additionally, we successfully reproduced the transition from the bright to dark states observed in the time-dependent fluorescence experiments, providing valuable insights into this critical part of the photophysical behavior in molecular motors.

Quantum Simulations of Radiation Damage in a Molecular Polyethylene Analog

An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. Ensembles of quantum simulations of radiation damage in a polyethylene analog are performed using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products are computed. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out-gassing products, though these correlations decrease with increasing atom recoil energy. The results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. Ultimately, the work provides a computational framework for determining the coupling between nonequilibrium chemistry in polymers and potential changes to macro-scale properties that can aid in the interpretation of future radiation damage experiments on plastic materials.

Benchmark Investigation of SCC-DFTB Against Standard DFT to Model Phononic Properties in Two-Dimensional MOFs for Thermoelectric Applications

Despite the importance of modeling lattice thermal conductivity in predicting thermoelectric (TE) properties, computational data on heat transport, especially from first-principles, in 2D metal-organic frameworks (MOFs) remain limited due to the high computational cost. To address this, we provide a benchmark of the performance of semiempirical self-consistent-charge density functional tight-binding (SCC-DFTB) methods against density functional theory (DFT) for monolayer, serrated, AA-stacked and/or AB-stacked Zn3C6O6, Cd3C6O6, Zn-NH-MOF, and Ni3(HITP)2 MOFs. Harmonic lattice dynamics calculations, including partial atomic contributions to phonon dispersions, are evaluated with both SCC-DFTB and DFT, whereas anharmonic transport (i.e., thermal conductivity) is evaluated with SCC-DFTB only. Our findings further suggest that unlike the other stacking geometries modeled, serrated Zn3C6O6, serrated Zn-NH-MOF, and wavy serrated Ni3(HITP)2 represent stable geometries. While Zn3C6O6 and Zn-NH-MOF exhibit a higher power factor than Ni3(HITP)2 (as found in our previous work), Zn-NH-MOF shows lower thermal conductivity, resulting in the highest thermoelectric figure of merit (ZT) among the studied MOFs.

Rationalization of the light-induced electron injection mechanism in a model 1D ZnO nanowire-dye complex: insights from real-time TD-DFTB simulations

Zinc oxide nanowires (ZnO NWs) possess a unique one-dimensional (1D) morphology that offers a direct pathway for charge transport. In this article, we present the first application of the real-time time-dependent density functional tight-binding (real-time TD-DFTB) method for a model hybrid system consisting of a catechol molecule adsorbed on a ZnO nanowire. The rationalization of the photoinduced electron injection to the 1D nanostructure is attained through quantum dynamics simulations, stressing the role of charge transfer in the new optical transitions upon dye adsorption. We provide a momentum-resolved picture of the photoexcitation dynamics, highlighting the charge accumulation in certain k-points, which could improve our understanding of these ultrafast processes. Finally, in the context of dye-sensitized solar cells (DSSCs) based on ZnO NW arrays, we provide a method to calculate the photoresponse obtaining similar results to experiments. This work paves the way towards the fast and accurate theoretical design of 1D optoelectronic nanodevices.

Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high-throughput to advanced models

Cocrystals are assemblies of more than one type of molecule stabilized through noncovalent interactions. They are promising materials for improved drug formulation in which the stability, solubility, or biocompatibility of the active pharmaceutical ingredient (API) is improved by including a coformer. In this work, a range of density functional theory (DFT) and density functional tight binding (DFTB) models are systematically compared for their ability to predict the lattice enthalpy of a broad range of existing pharmaceutically relevant cocrystals. These range from cocrystals containing model compounds 4,4'-bipyridine and oxalic acid to those with the well benchmarked APIs of aspirin and paracetamol, all tested with a large set of alternative coformers. For simple cocrystals, there is a general consensus in lattice enthalpy calculated by the different DFT models. For the cocrystals with API coformers the cocrystals, enthalpy predictions depend strongly on the DFT model. The significantly lighter DFTB models predict unrealistic values of lattice enthalpy even for simple cocrystals.

Structure of a biohybrid photosystem I-platinum nanoparticle solar fuel catalyst

Biohybrid solar fuel catalysts leverage natural light-driven enzymes to produce valuable fuel products. One useful biological platform for such a system is photosystem I, a pigment-protein complex that captures sunlight and converts it into chemical energy with near unity quantum efficiency, which generates low potential reducing equivalents for metabolism. Realizing and understanding the molecular basis for an approach that utilizes those electrons and stores solar energy as a fuel is therefore appealing. Here, we report the 2.27-Å global resolution cryo-EM structure of a photosystem I complex with bound platinum nanoparticles that catalyzes light-driven H2 production. The platinum nanoparticle binding sites and possible stabilizing interactions are described. Overall, the investigation reveals a direct structural look at a photon-to-fuels photosynthetic biohybrid system.

Aniline Derivatives Containing 1-Substituted 1,2,3-Triazole System as Potential Drug Candidates: Pharmacokinetic Profile Prediction, Lipophilicity Analysis Using Experimental and In Silico Studies

Background: The triazole ring is an attractive structural unit in medicinal chemistry, and chemical compounds containing this type of system in their structure exhibit a wide spectrum of biological activity. They are used in the development of new pharmaceuticals. One of the basic parameters considered in the initial phase of designing potential drugs is lipophilicity, which affects the bioavailability and pharmacokinetics of drugs. Methods: The study aimed to assess the lipophilicity of fifteen new triazole derivatives of aniline using reversed phase thin layer chromatography (RP-TLC) and free web servers. Based on in silico methods, the drug similarity and pharmacokinetic profile (ADMET) of synthesized molecules were assessed. Results: A relationship was observed between the structure of the title compound, including the position of substitution in the aniline ring, and the experimental values of lipophilicity parameters (logPTLC). Most of the algorithms used to determine theoretical logP values showed less sensitivity to structural differences of the tested molecules. All obtained derivatives satisfy the drug similarity rules formulated by Lipinski, Ghose and Veber. Moreover, in silico analysis of the ADME profile showed favorable values of parameters related to absorption.

Precise control of silver nanoplate dimensions and optical properties via pH, EDTA and AMP mediated synthesis

We present a seed-mediated synthesis method for producing silver nanoplates (AgNPTs) with customizable size and thickness, ensuring high yield and precise optical properties. This approach leverages ethylenediaminetetraacetic acid (EDTA) as a key component in the synthesis process, utilizing small single-crystal silver seeds. The interaction between Ag+ ions and EDTA at varying pH levels dynamically regulates silver complexation and reduction kinetics during seed overgrowth, leading to the formation of truncated nanoplates with superior optical responses. By adjusting the pH within the range of 8-10.5, we can manipulate the growth of the nanoplates, enabling a flexible optical response ranging from 519 to 1006 nm due to changes in their size and thickness. Additionally, nanoplate overgrowth extends plasmon resonance up to approximately 2000 nm. The incorporation of Adenosine 5' monophosphate (AMP) not only enhances nanoplate stability but also allows for precise thickness adjustment independent of growth kinetics. This method provides a systematic approach to tailor nanoplate morphology and optical properties with unprecedented precision. The role of EDTA is attributed to its complexation ability with Ag+ and its assistance in facet evolution, supported by density functional theory (DFT) simulations of surface energies modified by EDTA adsorption. Furthermore, DFT calculations confirm that AMP can further modify the surface energies of different facets, enabling precise thickness control.

Bending-induced enhanced spatial separation of dopants and long-lived conventional nanoribbon p-n junctions

The spatial separation of dopants is crucial in extending the lifetime of nanoribbon p-n junctions, which is traditionally realized via van der Waals heterostructures at a high cost. In this study, we employ atomistic quantum mechanical simulations to demonstrate that a simple in-plane bending deformation can lead to an enhanced doping preference in conventional nanoribbons. Dopants with larger atomic sizes than those of host atoms tend to reside on the tensile side close to the outermost edge of the bent nanoribbons, while dopants with smaller atomic sizes than those of host atoms tend to reside on the compressive side close to the innermost edge of the bent nanoribbons. We also show that this doping preference induces an enhanced spatial separation of n-type and p-type dopants with different atomic sizes. As conventional nanoribbons are easier to synthesize and cost-effective, our results provide a pathway for modulating dopant distribution and designing long-lived nanoribbon p-n junctions via inhomogeneous strain engineering.

Interfacial properties of a ZnO/PTFE composite from density functional tight-binding simulations

Metal-oxide-reinforced plastic nanocomposites are widely used in high-tech industries, but the reinforcement mechanism of the metal oxide is not fully understood. Here we investigate the interfacial properties of a zinc-oxide-reinforced amorphous polytetrafluoroethylene (a-PTFE) composite as a prototype for such composites using superlattice modeling and density functional tight-binding molecular dynamics simulations. To study the ZnO/a-PTFE composites, the superlattice supercells are built using a ZnO (112̄0) surface supercell and a-PTFE layer with an experimental density of 1.8 g cm-3 and various thicknesses. Our calculations demonstrate that the binding energy between ZnO and a-PTFE is negative, indicating their attractive binding, and electron accumulation occurs in the middle space between ZnO and a-PTFE, as well as around ZnO, evidencing that the newly formed interfacial chemical bonds are partially covalent. We further reveal that the tensile stress and elastic moduli of the ZnO/a-PTFE superlattice increases with increasing ZnO fraction, with values placed between those of ZnO and a-PTFE, which confirms the enhancement of the mechanical strength of the composites by incorporating ZnO into the a-PTFE matrix. This work provides a design guideline for developing high-performance metal-oxide-reinforced plastic composites.

The dynamic nature of electrostatic disorder in organic mixed ionic and electronic conductors

Charge dynamics in disordered media is described invariably assuming that the energy landscape for hopping site energy is stationary. Within the same framework, the correlation between low electronic disorder and high charge mobility is considered extremely robust, despite the emergence of materials with mixed ionic and electronic conductivity (OMIECs) that display high mobility coexisting with large disorder. We show in this work that the disorder of OMIEC polymers is highly dynamical, i.e. the on-site energy for charge transport fluctuates with a characteristic time comparable with that of electron transport. Under these conditions, the disorder of the "frozen" system is not relevant for the charge carrier, whose dynamics are instead controlled by the underlying dynamics of the material. Deep traps exist but have a finite lifetime. The combination of classical simulations and quantum chemical calculations on the nanosecond timescale seems ideal to disclose and characterise the phenomenon.

Double-pentagon silicon chains in a quasi-1D Si/Ag(001) surface alloy

Silicon surface alloys and silicide nanolayers are highly important as contact materials in integrated circuit devices. Here we demonstrate that the submonolayer Si/Ag(001) surface reconstruction, reported to exhibit interesting topological properties, comprises a quasi-one-dimensional Si-Ag surface alloy based on chains of planar double-pentagon Si moieties. This geometry is determined using a combination of density functional theory calculations, scanning tunnelling microscopy, and grazing incidence x-ray diffraction simulations, and yields an electronic structure in excellent agreement with photoemission measurements. This work provides further evidence of pentagonal geometries in 2D materials and heterostructures and elucidates the importance of surface alloying in stabilizing their formation.

GrapheNet: a deep learning framework for predicting the physical and electronic properties of nanographenes using images

In this work we introduce GrapheNet, a deep learning framework based on an Inception-Resnet architecture using image-like encoding of structural features for the prediction of the properties of nanographenes. The model is validated on datasets of computed structure/property data on graphene oxide and defected graphene nanoflakes. By exploiting the planarity of quasi-bidimensional systems and through encoding structures into images, and leveraging the flexibility and power of deep learning in image processing, Graphenet achieves significant accuracy in predicting the physicochemical properties of nanographenes. This approach is able to efficiently encode structures composed of hundreds of atoms, scaling efficiently with the size of the model and enabling the prediction of the properties of large systems, which contrasts with the limitations of current atomistic-level representations for deep learning applications. The approach proposed based on image encoding exhibit a significant numerical accuracy and outperforms the computational efficiency of current representations of materials at the atomistic level, with significant advantages especially in the representation of nanostructures and large planar systems.

Switching of electrochemical selectivity due to plasmonic field-induced dissociation

Electrochemical reactivity is known to be dictated by the structure and composition of the electrocatalyst-electrolyte interface. Here, we show that optically generated electric fields at this interface can influence electrochemical reactivity insofar as to completely switch reaction selectivity. We study an electrocatalyst composed of gold-copper alloy nanoparticles known to be active toward the reduction of CO2 to CO. However, under the action of highly localized electric fields generated by plasmonic excitation of the gold-copper alloy nanoparticles, water splitting becomes favored at the expense of CO2 reduction. Real-time time-dependent density functional tight binding calculations indicate that optically generated electric fields promote transient-hole-transfer-driven dissociation of the O─H bond of water preferentially over transient-electron-driven dissociation of the C─O bond of CO2. These results highlight the potential of optically generated electric fields for modulating pathways, switching reactivity on/off, and even directing outcomes.

Probing the range of applicability of structure- and energy-adjusted QM/MM link bonds III: QM/MM MD simulations of solid-state systems at the example of layered carbon structures

The previously introduced workflow to achieve an energetically and structurally optimized description of frontier bonds in quantum mechanical/molecular mechanics (QM/MM)-type applications was extended into the regime of computational material sciences at the example of a layered carbon model systems. Optimized QM/MM link bond parameters at HSEsol/6-311G(d,p) and self-consistent density functional tight binding (SCC-DFTB) were derived for graphitic systems, enabling detailed investigation of specific structure motifs occurring in graphene-derived structures v i a quantum-chemical calculations. Exemplary molecular dynamics (MD) simulations in the isochoric-isothermic (NVT) ensemble were carried out to study the intercalation of lithium and the properties of the Stone-Thrower-Wales defect. The diffusivity of lithium as well as hydrogen and proton adsorption on a defective graphene surface served as additional example. The results of the QM/MM MD simulations provide detailed insight into the applicability of the employed link-bond strategy when studying intercalation and adsorption properties of graphitic materials.

A Molecular View of Methane Activation on Ni(111) through Enhanced Sampling and Machine Learning

A combination of machine learned interatomic potentials (MLIPs) and enhanced sampling simulations is used to investigate the activation of methane on a Ni(111) surface. The work entails the development and iterative refinement of MLIPs, initially trained on a data set constructed via ab initio molecular dynamics simulations, supplemented by adaptive biasing forces, to enrich the sampling of catalytically relevant configurations. Our results reveal that upon incorporation of collective variables that capture the behavior of the reactant molecule, as well as additional frames that describe the dynamic response of the catalytic surface, it is possible to enhance considerably the accuracy of predicted energies and forces. By employing enhanced sampling schemes in the refinement of the MLIP, we systematically explore the potential energy surface, leading to a refined MLIP capable of predicting density functional theory-level energies and forces and replicating key geometric characteristics of the catalytic system. The resulting free energy landscapes at several temperatures provide a detailed view of the thermodynamics and dynamics of methane activation. Specifically, as methane approaches and dissociates on the catalytic surface, the process involves the dynamic interplay of CH4 and the Ni catalyst that includes both enthalpic and entropic contributions. The progression toward the transition state involves a CH4 moiety that is increasingly restrained in its ability to rotate or translate, while the stage following the transition state is characterized by a notable rise of the Ni atom that interacts with the cleaved C-H bond. This leads to an increase in the mobility of the adsorbed species, a feature that becomes more pronounced at higher temperatures.

Equivariant Neural Networks Utilizing Molecular Clusters for Accurate Molecular Crystal Lattice Energy Predictions

Equivariant neural networks have emerged as prominent models in advancing the construction of interatomic potentials due to their remarkable data efficiency and generalization capabilities for out-of-distribution data. Here, we expand the utility of these networks to the prediction of crystal structures consisting of organic molecules. Traditional methods for computing crystal structure properties, such as plane-wave quantum chemical methods based on density functional theory (DFT), are prohibitively resource-intensive, often necessitating compromises in accuracy and the choice of exchange-correlation functional. We present an approach that leverages the efficiency, and transferability of equivariant neural networks, specifically Allegro, to predict molecular crystal structure energies at a reduced computational cost. Our neural network is trained on molecular clusters using a highly accurate Gaussian-type orbital (GTO)-based method as the target level of theory, eliminating the need for costly periodic DFT calculations, while providing access to all families of exchange-corelation functionals and post-Hartree-Fock methods. The trained model exhibits remarkable accuracy in predicting lattice energies, aligning closely with those computed by plane-wave based DFT methods, thus representing significant cost reductions. Furthermore, the Allegro network was seamlessly integrated with the USPEX framework, accelerating the discovery of low-energy crystal structures during crystal structure prediction.

AI-assisted models to predict chemotherapy drugs modified with C60 fullerene derivatives

Employing quantitative structure-activity relationship (QSAR)/ quantitative structure-property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug-fullerene complexes (i.e., drug-pristine C60 fullerene and drug-carboxyfullerene C60-COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson's hard-soft acid-base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum chemistry at the density functional-based tight binding DFTB3 level. The results indicate that drug-fullerene complexes interact more with CXCR7 than isolated drugs. Specific binding sites were identified, with varying locations for each drug complex. Predictive models, developed using multiple linear regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used to compare results obtained by DFTB3 with a conventional density functional theory approach. These findings promise to enhance breast cancer chemotherapy by leveraging fullerene-based drug nanocarriers.

Atomic-level structure of the amorphous drug atuliflapon via NMR crystallography

We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, via chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify a number of specific conformations and interactions that stabilize the amorphous structure. These include preferred hydrogen-bonding motifs with water and with other drug molecules, as well as conformations of the cyclohexane and pyrazole rings that stabilize structure by indirectly allowing for optimization of hydrogen bonding.

Theoretical Investigation on the Reversible Photoswitch Mechanism of Benzylidene-Oxazolone System

The design and application of molecular photoswitches have attracted much attention. Herein, we performed a detailed computational study on the photoswitch benzylidene-oxazolone system based on static electronic structure calculations and on-the-fly excited-state dynamic simulations. For the Z and E isomer, we located six and four minimum energy conical intersections (MECIs) between the first excited state (S1) and the ground state (S0), respectively. Among them, the relaxation pathway driven by ring-puckering motion is the most competitive channel with the photoisomeization process, leading to the low photoisomerization quantum yield. In the dynamic simulations, about 88 % and 66 % trajectories decay from S1 to S0 for Z and E isomer, respectively, within the total simulation time of ~2 ps. The photoisomeization quantum yields obtained in our study (0.20 for Z→E and 0.12 for E→Z) agree well with the experimental measured values (0.25 and 0.11), even though the number of trajectories is limited to 50. Our study sheds light on the complexity of the benzylidene-oxazolone system 's deactivation process and the competitive mechanisms among different reaction channels, which provides theoretical guidance for further design and development of benzylidene-oxazolone based molecular photoswitches.

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Mechanism of activation and autophosphorylation of a histidine kinase

Histidine kinases (HK) are one of the main prokaryotic signaling systems. Two structurally conserved catalytic domains inside the HK enable autokinase, phosphotransfer, and phosphatase activities. Here, we focus on a detailed mechanistic understanding of the functional cycle of the WalK HK by a multi-scale simulation approach, consisting of classical as well as hybrid QM/MM molecular dynamics simulation. Strikingly, a conformational transition induced solely in DHp leads to the correct activated conformation in CA crucial for autophosphorylation. This finding explains how variable sensor domains induce the transition from inactive to active state. The subsequent autophosphorylation inside DHp proceeds via a penta-coordinated transition state to a protonated phosphohistidine intermediate. This intermediate is consequently deprotonated by a suitable nearby base. The reaction energetics are controlled by the final proton acceptor and presence of a magnesium cation. The slow rates of the process result from the high energy barrier of the conformational transition between inactive and active states. The phosphorylation step exhibits a lower barrier and down-the-hill energetics. Thus, our work suggests a detailed mechanistic model for HK autophosphorylation.

Dispersion-Driven Cooperativity in Alkyl Perylene Diimide Oligomers: Insights from Density Functional Theory

The cooperative mechanism is of paramount importance in the synthesis of supramolecular polymers with desired characteristics, including molecular mass, polydispersity, and morphology. It is primarily driven by the presence of intermolecular interactions, which encompass strong hydrogen bonding, metal-ligand interactions, and dipole-dipole interactions. In this study, we utilize density functional theory and energy decomposition analysis to investigate the cooperative behavior of perylene diimide (PDI) oligomers with alkyl chains at their imide positions, which lack the previously mentioned interactions. Our systematic examination reveals that dispersion interactions originating from the alkyl side-chain substituents play an important role in promoting cooperativity within these PDIs. This influence becomes even more pronounced for alkyl chain lengths beyond hexyl groups. The energy decomposition analysis reveals that the delicate balance between dispersion energy and Pauli repulsion energy is the key driver of cooperative behavior in PDIs. Additionally, we have developed a mathematical model capable of predicting the saturated binding energies for PDI oligomers of varying sizes and alkyl chain lengths. Overall, our findings emphasize the previously undervalued significance of dispersion forces in cooperative supramolecular polymerization, enhancing our overall understanding of the cooperative mechanism.

Kekulé Counts, Clar Numbers, and ZZ Polynomials for All Isomers of (5,6)-Fullerenes C52-C70

We report an extensive tabulation of several important topological invariants for all the isomers of carbon (5,6)-fullerenes Cn with n = 52-70. The topological invariants (including Kekulé count, Clar count, and Clar number) are computed and reported in the form of the corresponding Zhang-Zhang (ZZ) polynomials. The ZZ polynomials appear to be distinct for each isomer cage, providing a unique label that allows for differentiation between various isomers. Several chemical applications of the computed invariants are reported. The results suggest rather weak correlation between the Kekulé count, Clar count, Clar number invariants, and isomer stability, calling into doubt the predictive power of these topological invariants in discriminating the most stable isomer of a given fullerene. The only exception is the Clar count/Kekulé count ratio, which seems to be the most important diagnostic discovered from our analysis. Stronger correlations are detected between Pauling bond orders computed from Kekulé structures (or Clar covers) and the corresponding equilibrium bond lengths determined from the optimized DFTB geometries of all 30,579 isomers of C20-C70.

Designing building blocks of covalent organic frameworks through on-the-fly batch-based Bayesian optimization

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.

Multipodal Au-C grafting of calix[4]arene molecules on gold nanorods

The interface robustness and spatial arrangement of functional molecules on metallic nanomaterials play a key part in the potential applications of functional nano-objects. The design of mechanically stable and electronically coupled attachments with the underlying metal is essential to bring specific desirable properties to the resulting hybrid materials. In this context, rigid multipodal platforms constitute a unique opportunity for the controllable grafting of functionality. Herein, we provide for the first time an in-depth description of the interface between gold nanorods and a chemically-grafted multipodal platform based on diazonium salts. Thanks to Raman and X-ray photoelectron spectroscopies and theoretical modeling, we deliver insights on the structural and electronic properties of the hybrid material. More importantly, it allows for the accurate assignment of Raman bands. The combination of experimental and theoretical results establishes the formation of four carbon-gold anchors for the calix[4]arene macrocycle leading to the exceptional stability of the functionalized nano-objects. Our results lay the foundations for the future design of robust and versatile platforms.

i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations

Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach extensive length and time scales. The i-PI package facilitates integrating the latest developments in this field with advanced modeling techniques thanks to a modular software architecture based on inter-process communication through a socket interface. The choice of Python for implementation facilitates rapid prototyping but can add computational overhead. In this new release, we carefully benchmarked and optimized i-PI for several common simulation scenarios, making such overhead negligible when i-PI is used to model systems up to tens of thousands of atoms using widely adopted machine learning interatomic potentials, such as Behler-Parinello, DeePMD, and MACE neural networks. We also present the implementation of several new features, including an efficient algorithm to model bosonic and fermionic exchange, a framework for uncertainty quantification to be used in conjunction with machine-learning potentials, a communication infrastructure that allows for deeper integration with electronic-driven simulations, and an approach to simulate coupled photon-nuclear dynamics in optical or plasmonic cavities.

Density Functional Tight-Binding Models for Band Structures of Transition-Metal Alloys and Surfaces across the d-Block

First-principles electronic structure simulations are an invaluable tool for understanding chemical bonding and reactions. While machine-learning models such as interatomic potentials significantly accelerate the exploration of potential energy surfaces, electronic structure information is generally lost. Particularly in the field of heterogeneous catalysis, simulated electron band structures provide fundamental insights into catalytic reactivity. This ab initio knowledge is preserved in semiempirical methods such as density functional tight binding (DFTB), which extend the accessible computational length and time scales beyond first-principles approaches. In this paper we present Shell-Optimized Atomic Confinement (SOAC) DFTB electronic-part-only parametrizations for bulk and surface band structures of all d-block transition metals that enable efficient predictions of electronic descriptors for large structures or high-throughput studies on complex systems outside the computational reach of density functional theory.

A first-principles alternative to empirical solvent parameters

The use of solvents is ubiquitous in chemistry. Empirical parameters, such as the Kamlet-Taft parameters and Gutmann donor/acceptor numbers, have long been used to predict and quantify the effects solvents have on chemical phenomena. Collectively however, such parameters are unsatisfactory, since each describes ultimately the same non-covalent solute-solvent and solute-solute interactions in completely disparate ways. Here we hypothesise that empirical solvent parameters are essentially proxy measures of the electrostatic terms that dominate solvent-solute interactions. On the basis of this hypothesis, we develop a new fundamental descriptor of these interactions, , and show that it is a self-consistent, probe-free, first principles alternative to established empirical solvent parameters.

Dynamic interchange between two protonation states is characteristic of active sites of cholinesterases

Cholinesterases are well-known and widely studied enzymes crucial to human health and involved in neurology, Alzheimer's, and lipid metabolism. The protonation pattern of active sites of cholinesterases influences all the chemical processes within, including reaction, covalent inhibition by nerve agents, and reactivation. Despite its significance, our comprehension of the fine structure of cholinesterases remains limited. In this study, we employed enhanced-sampling quantum-mechanical/molecular-mechanical calculations to show that cholinesterases predominantly operate as dynamic mixtures of two protonation states. The proton transfer between two non-catalytic glutamate residues follows the Grotthuss mechanism facilitated by a mediator water molecule. We show that this uncovered complexity of active sites presents a challenge for classical molecular dynamics simulations and calls for special treatment. The calculated proton transfer barrier of 1.65 kcal/mol initiates a discussion on the potential existence of two coupled low-barrier hydrogen bonds in the inhibited form of butyrylcholinesterase. These findings expand our understanding of structural features expressed by highly evolved enzymes and guide future advances in cholinesterase-related protein and drug design studies.

Impact of 2-hydroxypropyl-β-cyclodextrin inclusion complex formation on dopamine receptor-ligand interaction - A case study

The octanol-water distribution coefficient (logP), used as a measure of lipophilicity, plays a major role in the drug design and discovery processes. While average logP values remain unchanged in approved oral drugs since 1983, current medicinal chemistry trends towards increasingly lipophilic compounds that require adapted analytical workflows and drug delivery systems. Solubility enhancers like cyclodextrins (CDs), especially 2-hydroxypropyl-β-CD (2-HP-β-CD), have been studied in vitro and in vivo investigating their ADMET (adsorption, distribution, metabolism, excretion and toxicity)-related properties. However, data is scarce regarding the applicability of CD inclusion complexes (ICs) in vitro compared to pure compounds. In this study, dopamine receptor (DR) ligands were used as a case study, utilizing a combined in silico/in vitro workflow. Media-dependent solubility and IC stoichiometry were investigated using HPLC. NMR was used to observe IC formation-caused chemical shift deviations while in silico approaches utilizing basin hopping global minimization were used to propose putative IC binding modes. A cell-based in vitro homogeneous time-resolved fluorescence (HTRF) assay was used to quantify ligand binding affinity at the DR subtype 2 (D2R). While all ligands showed increased solubility using 2-HP-β-CD, they differed regarding IC stoichiometry and receptor binding affinity. This case study shows that IC-formation was ligand-dependent and sometimes altering in vitro binding. Therefore, IC complex formation can't be recommended as a general means of improving compound solubility for in vitro studies as they may alter ligand binding.

Host-Guest Interactions of Ruthenium(II) Arene Complexes with Cucurbit[7/8]uril

Cucurbit[n]urils (CB[n]s) have been recognized for their chemical and thermal stability, and their ability to bind many neutral and cationic guest molecules makes them excellent hosts in a range of supramolecular applications. In drug delivery, CB[n]s can enhance drug solubility, improve chemical and physical drug stability, and allow for triggered and controlled release. This study aimed to investigate the ability of CB[7] and CB[8] as molecular hosts to bind ruthenium(II) arene complexes that are current anticancer lead structures in the area of metallodrugs. Both, experimental and computational methods, led to insights into the binding preferences and geometries of [RuII(cym)Cl2]2 (1; cym = η6-p-cymene), [RuII(cym)(dmb)Cl2]) (2; cym = η6-p-cymene; dmb = 1,3-dimethylbenzimidazol-2-ylidene), and [RuII(cym)(pta)Cl2] (3, RAPTA-C; cym = η6-p-cymene; pta = 1,3,5-triaza-7-phospha-adamantane) with CB[7] and CB[8]. Competition experiments by mass spectrometry revealed clear preferences of 2 for CB[8] and 3 for CB[7]. Based on a comparison of the associated interaction energies from quantum chemical calculations as well as experimental data, 3@CB[7] clearly prefers a binding mode, where the pta ligand is located inside the cavity of the host, and the metal ion interacts with two of the carbonyl groups on the rim of CB[7]. In contrast, complex 2 binds in two different orientations with interaction energies similar to those of both CB[n]s, with the cym ligand being either inside or outside of the cavity. These findings suggest that ruthenium(II) arene complexes are able to form stable host-guest interactions with CB[n]s, which can be exploited as drug delivery vehicles in further metallodrug development to improve their chemical stability.

Laser pulse induced second- and third-harmonic generation of gold nanorods with real-time time-dependent density functional tight binding (RT-TDDFTB) method

In this study, we investigate second- and third-harmonic generation processes in Au nanorod systems using the real-time time-dependent density functional tight binding method. Our study focuses on the computation of nonlinear signals based on the time dependent dipole response induced by linearly polarized laser pulses interacting with nanoparticles. We systematically explore the influence of various laser parameters, including pump intensity, duration, frequency, and polarization directions, on harmonic generation. We demonstrate all the results using Au nanorod dimer systems arranged in end-to-end configurations, and disrupting the spatial symmetry of regular single nanorod systems is crucial for second-harmonic generation processes. Furthermore, we study the impact of nanorod lengths, which lead to variable plasmon energies, on harmonic generation, and estimates of polarizabilities and hyper-polarizabilities are provided.

Nonadiabatic Exciton Dynamics and Energy Gradients in the Framework of FMO-LC-TDDFTB

We introduce a novel methodology for simulating the excited-state dynamics of extensive molecular aggregates in the framework of the long-range corrected time-dependent density-functional tight-binding fragment molecular orbital method (FMO-LC-TDDFTB) combined with the mean-field Ehrenfest method. The electronic structure of the system is described in a quasi-diabatic basis composed of locally excited and charge-transfer states of all fragments. In order to carry out nonadiabatic molecular dynamics simulations, we derive and implement the excited-state gradients of the locally excited and charge-transfer states. Subsequently, the accuracy of the analytical excited-state gradients is evaluated. The applicability to the simulation of exciton transport in organic semiconductors is illustrated on a large cluster of anthracene molecules. Additionally, nonadiabatic molecular dynamics simulations of a model system of benzothieno-benzothiophene molecules highlight the method's utility in studying charge-transfer dynamics in organic materials. Our new methodology will facilitate the investigation of excitonic transfer in extensive biological systems, nanomaterials, and other complex molecular systems consisting of thousands of atoms.

Efficient Parameterization of Density Functional Tight-Binding for 5f-Elements: A Th-O Case Study

Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th-O system, which was subsequently applied to the study of ThO2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO2. Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.

Exploring fracture of H-BN and graphene by neural network force fields

Extreme mechanical processes such as strong lattice distortion and bond breakage during fracture often lead to catastrophic failure of materials and structures. Understanding the nucleation and growth of cracks is challenged by their multiscale characteristics spanning from atomic-level structures at the crack tip to the structural features where the load is applied. Atomistic simulations offer 'first-principles' tools to resolve the progressive microstructural changes at crack fronts and are widely used to explore the underlying processes of mechanical energy dissipation, crack path selection, and dynamic instabilities (e.g. kinking, branching). Empirical force fields developed based on atomic-level structural descriptors based on atomic positions and the bond orders do not yield satisfying predictions of fracture, especially for the nonlinear, anisotropic stress-strain relations and the energy densities of edges. High-fidelity force fields thus should include the tensorial nature of strain and the energetics of bond-breaking and (re)formation events during fracture, which, unfortunately, have not been taken into account in either the state-of-the-art empirical or machine-learning force fields. Based on data generated by density functional theory calculations, we report a neural network-based force field for fracture (NN-F3) constructed by using the end-to-end symmetry preserving framework of deep potential-smooth edition (DeepPot-SE). The workflow combines pre-sampling of the space of strain states and active-learning techniques to explore the transition states at critical bonding distances. The capability of NN-F3is demonstrated by studying the rupture of hexagonal boron nitride (h-BN) and twisted bilayer graphene as model problems. The simulation results elucidate the roughening physics of fracture defined by the lattice asymmetry in h-BN, explaining recent experimental findings, and predict the interaction between cross-layer cracks in twisted graphene bilayers, which leads to a toughening effect.