Types Of Atoms Research Articles

Structural strategy for advancing nonlinear optical effects in 1D-[MX2]∞ chains: internal distortion and atomic types.

This work aimed to alleviate the limitations of existing mid-infrared nonlinear-optics (MIR-NLO) crystals by conducting theoretical research on 1D-[MX2]∞ (1D = one-dimensional; M = metallic element; X = anionic element) structures in relation to NLO. An analysis was conducted on the electronic structure and optical properties of six selenides (BaZnGeSe4, KxBa1-x/2Ga2Se4, KxBa1-x/2GayIn2-ySe4, KxBa1-x/2In2Se4, BaZnSiSe4, SrZnSiSe4) and three arsenides (Cs2SnAs2, Rb2SnAs2, K2SnAs2) using first principles. Afterwards, the inherent characteristics of these 1D-[MX2]∞ chains were further examined. The calculation results suggest that the presence of internal distortion (non-centrosymmetric) and high electronegativity in M elements can greatly enhance the NLO capability of 1D-[MX2]∞ structures. Furthermore, the first-ever prediction of K2SnAs2's NLO ability has been made. Ultimately, the establishment of a theoretical structure (K2BaSn2As4) provides guidance for the subsequent creation of high-performance MIR-NLO crystals.

Visualization of drug release in a chemo-immunotherapy nanoplatform via ratiometric 19F magnetic resonance imaging.

Visualization of drug release in vivo is crucial for improving therapeutic efficacy and preventing inappropriate medication dosing, yet, challenging. Herein, we report a pH-activated chemo-immunotherapy nanoplatform with visualization of drug release in vivo by ratiometric 19F magnetic resonance imaging (19F MRI). This nanoplatform consists of ultra-small histamine-modified perfluoro-15-crown-5-ether (PFCE) nanodroplets loaded with doxorubicin (Dox), which are packaged in trifluoromethyl-containing metal-organic assemblies via coordination-driven self-assembly. The chemical shifts of two types of 19F atoms in the nanoplatform are significantly different in 19F nuclear magnetic resonance (NMR) spectra, which facilitates the implementation of ratiometric 19F MRI without any signal crosstalk. In an acidic tumor microenvironment, this nanoplatform gradually degrades, which results in a sustained drug release with a real-time change in the ratiometric 19F MRI signal. Therefore, a linear correlation between the Dox release profile and ratiometric 19F MRI signal is established to visualize Dox release. Moreover, the pH-triggered disassembly of the nanoplatform leads to cell pyroptosis, which evokes immunogenic cell death (ICD), resulting in the regression of the primary tumor and inhibition of distal tumor growth. This study provides the proof-of-concept application of ratiometric 19F MRI to visualize drug release in vivo.

Monitoring water harvesting in metal-organic frameworks, one water molecule at a time.

Metal-organic frameworks (MOFs) have gained prominence as potential materials for atmospheric water harvesting, a vital solution for arid regions and areas experiencing severe water shortages. However, the molecular factors influencing the performance of MOFs in capturing water from the air remain elusive. Among all MOFs, Ni2X2BTDD (X = F, Cl, Br) stands out as a promising water harvester due to its ability to adsorb substantial amounts of water at low relative humidity (RH). Here, we use advanced molecular dynamics simulations carried out with the state-of-the-art MB-pol data-driven many-body potential to monitor water adsorption in the three Ni2X2BTDD variants as a function of RH. Our simulations reveal that the type of halide atom in the three Ni2X2BTDD frameworks significantly influences the corresponding molecular mechanisms of water adsorption: while water molecules form strong hydrogen bonds with the fluoride atoms in Ni2F2BTDD, they tend to form hydrogen bonds with the nitrogen atoms of the triazolate linkers in Ni2Cl2BTDD and Ni2Br2BTDD. Importantly, the large size of the bromide atoms reduces the void volume in the Ni2Br2BTDD pores, which enable water molecules to initiate an extended hydrogen-bond network at lower RH. These findings not only underscore the prospect for precisely tuning structural and chemical modifications of the frameworks to optimize their interaction with water, but also highlight the predictive power of simulations with the MB-pol data-driven many-body potential. By providing a realistic description of water under different thermodynamic conditions and environments, these simulations yield unique, molecular-level insights that can guide the design and optimization of energy-efficient water harvesting materials.

Controllable electronic properties, contact barriers and contact types in a TaSe2/WSe2 metal-semiconductor heterostructure.

Two-dimensional (2D) metallic TaSe2 and semiconducting WSe2 materials have been successfully fabricated in experiments and are considered as promising contact and channel materials, respectively, for the design of next-generation electronic devices. Herein, we design a metal-semiconductor (M-S) heterostructure combining metallic TaSe2 and semiconducting WSe2 materials and investigate the atomic structure, electronic properties and controllable contact types of the combined TaSe2/WSe2 M-S heterostructure using first-principles calculations. Our results reveal that the TaSe2/WSe2 M-S heterostructure can adopt four different stable stacking configurations, all of which exhibit enhanced elastic constants compared to the constituent monolayers. Furthermore, the TaSe2/WSe2 M-S heterostructure exhibits p-type Schottky contact (SC) with Schottky barriers ranging from 0.36 to 0.49 eV, depending on the stacking configurations. The TaSe2/WSe2 M-S heterostructure can be considered as a promising M-S contact for next-generation electronic Schottky devices owing to its small tunneling resistivity of about 2.14 × 10-9 Ω cm2. More interestingly, the TaSe2/WSe2 M-S heterostructure exhibits tunable contact types and contact barriers under the application of an electric field. A negative electric field induces a transition from Schottky contact type to ohmic contact (OC) type. On the other hand, a positive electric field leads to a transformation from p-type SC to n-type SC. Our findings provide valuable insights into the practical applications of the TaSe2/WSe2 M-S heterostructure towards next-generation electronic devices.

Structure and properties of the FLiNaK – LaF3 melt obtained with neural network potential

Accurate and efficient prediction of the thermochemical properties of the melts applicable in molten salt reactors could be made if the proper machine learning model is used. In this paper, the neural network potential for simulation of 85 % (LiF – NaF – KF)eut. – 15 % LaF3 molten mixture was developed based on ab initio data. In spite of multiple atomic types, the model of a moderate size showed small root mean squared errors in energy and forces of 0.5 meV/atom and 39 meV/Å, respectively. Then the neural network potential was employed to calculate local structure, density, self-diffusion coefficients, heat capacity, thermal conductivity, and thermal diffusivity for a range of temperatures. We found that the addition of LaF3 to the eutectic mixture of alkali fluorides results in a reduction the melt ability to store and transfer heat. The strong effect observed here is the reduction in heat capacity by 20–30 %. The analysis of the local structure details reveals the existence of [LaF6], [LaF7] and [LaF8] groupings, with the most probable being [LaF7] and the La – F separation averaged over the ensemble of 2.35 Å.

Deciphering the structure and potassium-ion transport mechanism of potassium borate glass.

Potassium borate glass has great potential as an ion transport material. The ion transport rate is closely related to the microstructure of the glass. However, the disorder and variations in boron and oxygen atom types in the glass structure pose challenges in the analysis of this complex glass structure. In this work, the structure of potassium borate glass was unveiled through the neutron diffraction method and ab initio molecular dynamics (AIMD) simulations. The B-O, K-O, and O-O atomic interactions, bond lengths, coordination numbers, cavity distribution, ring structure distributions and other detailed information in the microstructure of potassium borate glass were obtained. By comparing the structure and properties of potassium borate glass with those of crystals of similar components, it is found that the bond lengths of 3B-BO (BO, bridging oxygen), 4B-BO and 3B-NBO (NBO, non-bridging oxygen) are longer than those of corresponding crystals, so the structure of the boron-oxygen network is looser and the density is smaller than that of similar crystals. Moreover, we found a rule that in both borate glass and crystal, the increase of NBO shortened the length of the B-O bond, and the increase of 4B increased the length of the B-O bond. The key structures affecting the transport rate of K+ were NBO, chain structure units and cavities. This work will provide reference data for designing and developing electrically conductive amorphous materials with faster potassium-ion transport rates.

NICE-FF: A non-empirical, intermolecular, consistent, and extensible force field for nucleic acids and beyond.

A new non-empirical abinitio intermolecular force field (NICE-FF in buffered 14-7 potential form) has been developed for nucleic acids and beyond based on the dimer interaction energies (IEs) calculated at the spin component scaled-MI-second order Møller-Plesset perturbation theory. A fully automatic framework has been implemented for this purpose, capable of generating well-polished computational grids, performing the necessary abinitio calculations, conducting machine learning (ML) assisted force field (FF) parametrization, and extending existing FF parameters by incorporating new atom types. For the ML-assisted parametrization of NICE-FF, interaction energies of ∼18 000 dimer geometries (with IE < 0) were used, and the best fit gave a mean square deviation of about 0.46 kcal/mol. During this parametrization, atom types apparent in four deoxyribonucleic acid (DNA) bases have been first trained using the generated DNA base datasets. Both uracil and hypoxanthine, which contain the same atom types found in DNA bases, have been considered as test molecules. Three new atom types have been added to the DNA atom types by using IE datasets of both pyrazinamide and 9-methylhypoxanthine. Finally, the last test molecule, theophylline, has been selected, which contains already-fitted atom-type parameters. The performance of NICE-FF has been investigated on the S22 dataset, and it has been found that NICE-FF outperforms the well-known FFs by generating the most consistent IEs with the high-level abinitio ones. Moreover, NICE-FF has been integrated into our in-house developed crystal structure prediction (CSP) tool [called FFCASP (Fast and Flexible CrystAl Structure Predictor)], aiming to find the experimental crystal structures of all considered molecules. CSPs, which were performed up to 4 formula units (Z), resulted in NICE-FF being able to locate almost all the known experimental crystal structures with sufficiently low RMSD20 values to provide good starting points for density functional theory optimizations.

Enhancing oxygen reduction and evolution reactions on multi-metal N-doped graphene catalysts through catalytic activity and selectivity tuning

Metal-nitrogen-carbon (M-N-C) catalysts exhibit promising electrocatalytic properties for oxygen reduction and evolution reactions (ORR/OER) and are potential substitutes for precious metal catalysts. However, the underlying reaction mechanisms are still debated. To investigate the connection between atomic microstructures and catalytic performance, this study employs density functional theory (DFT) calculations to analyze 10 different M-N-C catalysts with varying metal atom types and ratios (Fe, Co, and Ni) on three active centers. The activities of all active sites are computed, showing notable variations in ORR/OER performance among different sites. For homonuclear M-N-C catalysts, the ORR overpotentials of Fe, Co, and Ni are 0.69, 0.26, and 1.03 V, respectively. These values change to 0.79, 0.31, and 1.00 V, respectively, for the heteronuclear M-N-C catalysts with trimetallic active centers when the reaction occurs at the Fe, Co, and Ni site. Similar trends are observed for the OER of both homonuclear and heteronuclear M-N-C catalysts in molecular models. Among Fe, Co, and Ni atoms, the Co site exhibits the most favorable ORR/OER bifunctional activity. This study provides valuable insights into selecting catalytic active sites and understanding the structure-performance relationship of M-N-C structures at a microscopic level.

Rational modulation of Fe single-atom electronic structure in a Fe-N2B4 configuration for preferential 1O2 generation in Fenton-like reactions

The important role of optimizing the coordination environment of single-atom catalysts (SACs) for selective production of singlet oxygen (1O2) in Fenton-like reactions is revealed. Herein, we introduce electron-depletion boron atoms to manipulate the coordination number and atom types of Fe site simultaneously and construct a six-coordination Fe-N2B4 catalyst for peroxymonosulfte (PMS) activation. Particularly, it achieves 98.68% 1O2 generation selectivity superior to unregulated Fe-N4 catalyst (64.57%), exhibiting an exceptional bisphenol A (BPA) degradation performance with a reaction rate constant of 0.249 min−1. Experimental and theoretical results unveil that the tailored electronic structure of Fe not only enhances the adsorption selectivity of terminal oxygen atoms in PMS and alters the reaction pathway preference, but also facilitates the electron donation from PMS and lowers the energy barrier for 1O2 generation. This work provides a universal strategy for rational and precise modulation of SACs for specific reactive species conversion in environment remediation.

A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database

Knowledge about the 3-dimensional structure, orientation and interaction of chemical compounds is important in many areas of science and technology. X-ray crystallography is one of the experimental techniques capable of providing a large amount of structural information for a given compound, and it is widely used for characterisation of organic and metal-organic molecules. The method provides precise 3D coordinates of atoms inside crystals, however, it does not directly deliver information about certain chemical characteristics such as bond orders, delocalization, charges, lone electron pairs or lone electrons. These aspects of a molecular model have to be derived from crystallographic data using refined information about interatomic distances and atom types as well as employing general chemical knowledge. This publication describes a curated automatic pipeline for the derivation of chemical attributes of molecules from crystallographic models. The method is applied to build a catalogue of chemical entities in an open-access crystallographic database, the Crystallography Open Database (COD). The catalogue of such chemical entities is provided openly as a derived database. The content of this catalogue and the problems arising in the fully automated pipeline are discussed, along with the possibilities to introduce manual data curation into the process.

Structural building-blocks of disordered Cu-Zr alloys

The atomic structures of Cu-Zr alloys have been extensively studied due to the unusually high glass forming ability of this system. In this work, we investigate the nearest-neighbor atomic structures of several simulated disordered Cu-Zr compositions at three temperatures: 1450 K (liquid), 700 K (undercooled liquid), and 350 K (disordered solid). The results show that over 90 % of the local structures in these systems can be described by one of 12 “building-block” atomic cluster geometries, regardless of composition or temperature, and that these structures all exhibit icosahedral or quasi-icosahedral geometries. Most of the building-blocks are unique in their combination of representative Voronoi polyhedron geometry, predominant central atom type, and occupation probability for each site in the cluster. In addition to the 12 common building-blocks, 35 “outlier” structures unique to their simulated compositions and temperatures were observed. The outliers were primarily non-icosahedral. While the populations of the common building-blocks do not exhibit clear trends with temperature or composition, the overall population of outliers increased with decreasing temperature, suggestive of their role in accommodating the lack of translational order in the disordered solid.

Boosting Graph Neural Networks with Molecular Mechanics: A Case Study of Sigma Profile Prediction.

Sigma profiles are quantum-chemistry-derived molecular descriptors that encode the polarity of molecules. They have shown great performance when used as a feature in machine learning applications. To accelerate the development of these models and the construction of large sigma profile databases, this work proposes a graph convolutional network (GCN) architecture to predict sigma profiles from molecule structures. To do so, the usage of molecular mechanics (force field atom types) is explored as a computationally inexpensive node-level featurization technique to encode the local and global chemical environments of atoms in molecules. The GCN models developed in this work accurately predict the sigma profiles of assorted organic and inorganic compounds. The best GCN model here reported, obtained using Merck molecular force field (MMFF) atom types, displayed training and testing set coefficients of determination of 0.98 and 0.96, respectively, which are superior to previous methodologies reported in the literature. This performance boost is shown to be due to both the usage of a convolutional architecture and node-level features based on force field atom types. Finally, to demonstrate their practical applicability, we used GCN-predicted sigma profiles as the input to machine learning models previously developed in the literature that predict boiling temperatures and aqueous solubilities. Using the predicted sigma profiles as input, these models were able to compute both physicochemical properties using significantly less computational resources and displayed only a slight decrease in performance when compared with sigma profiles obtained from quantum chemistry methods.

Properties of Local Electronic Structures.

The simulation of intrinsic contributions to molecular properties holds the potential to allow for chemistry to be directly inferred from changes to electronic structures at the atomic level. In the present study, we demonstrate how such local properties can be readily derived from suitable molecular orbitals to yield effective fingerprints of various types of atoms in organic molecules. In contrast, corresponding inferences from schemes that instead make use of individual atomic orbitals for this purpose are generally found to fail in expressing much uniqueness in atomic environments. By further studying the extent to which entire chemical reactions may be decomposed into meaningful and continuously evolving atomic contributions, schemes based on molecular rather than atomic orbitals are once again found to be the more consistent, even allowing for intricate differences between seemingly uniform nucleophilic substitutions to be probed.

MetalDock: An Open Access Docking Tool for Easy and Reproducible Docking of Metal Complexes.

Despite the proven potential of metal complexes as therapeutics, the lack of computational tools available for the high-throughput screening of their interactions with proteins is a limiting factor toward clinical developments. To address this challenge, we introduce MetalDock, an easy-to-use, open access docking software for docking metal complexes to proteins. Our tool integrates the AutoDock docking engine with three well-known quantum software packages to automate the docking of metal-organic complexes to proteins. We used a Monte Carlo sampling scheme to obtain the missing Lennard-Jones parameters for 12 metal atom types and demonstrated that these parameters generalize exceptionally well. Our results show that the poses obtained by MetalDock are highly accurate, as they predict the binding geometries experimentally determined by crystal structures with high spatial reproducibility. Three different case studies are presented that demonstrate the versatility of MetalDock for the docking of diverse metal-organic compounds to different biomacromolecules, including nucleic acids.

Síntesis y análisis estructural de la ludwigita Co&lt;sub&gt;2&lt;/sub&gt;InBO&lt;sub&gt;5&lt;/sub&gt;

We report the study on the synthesis of indium-doped cobalt ludwigite (Co2InBO5). The Match program, applied to the XRD powder standard, determined that the compound consists of two phases: phase I, ludwigite Co2InBO5 and phase II, the indium oxide In2O3 impurity. The Rietveld refinement applied to the XRD powder standard determined that the percentage by weight of phase I was 98.18% and the percentage by weight of phase II was 1.82%. The atomic ratio between Co and in corresponded to Co/In=2/1, for a goodness of fit s = 1.64 and Chi2 ≅ 2.70. With these adjustment values, the chemical content of the experimental compound was adjusted to its theoretical chemical stoichiometric content. In the unit cell, the different types of atoms occupied the Wyckoff positions 2b, 2c, 4g, and 4h. Atoms of the same or different types can occupy the same Wyckoff position, but a group of them occupy equivalent positions forming a crystallographic orbit. The refined position of each of the different types of atoms, in the unit cell, showed that the B ions, Co and In, form bonds with oxygen generating structural units in the form of triangles (BO3) and in the form of octahedrons (CoO6, InO6). These structural units form low- dimensional substructures such as three-legged ladders 313, 424 and zigzag-shaped planes. Both, the three-legged ladders and the zigzag-shaped planes are formed by the (Co/In)O6 octahedrons, and extend along the crystallographic axis c. These substructures are responsible for their extraordinary physical properties.

Liquid separation modeling while selecting contact devices for separation in a centrifugal field

This paper proposes the use of mathematical modeling of technological processes as a tool for increasing the efficiency of equipment and solving environmental problems, which contributes to the sustainable development of enterprises in the chemical industry. The results of modeling the process of separating liquids of different densities in a centrifugal field are presented to study the influence of the type of atomizers on the flow rate and the number of contact devices on the separation efficiency. The work shows the importance of using numerical modeling in design to substantiate scientifically decisions made and confirm the effectiveness of developed structures. The results are important to intensify heat and mass transfer processes and to reduce the negative impact of production on the environment.

Understanding complexity of hydrogen-bonded liquids through Voronoi entropy

In this paper we study the short order of liquids using Voronoi entropy (SVor), computed using Voronoi Polyhedra (VP) tessellations. Voronoi tessellations partition a plane with n seeds into polyhedrons, in which each polyhedron has exactly one seed and every location within a given polyhedron is nearest to its generating seed, rather than to any other seed. The distribution of these VP is used to determine the Voronoi entropy (SVor), which shares similarities with the Shannon measure of information (H). Such studies, however, have been largely restricted to two-dimensional analysis. In this study SVor is used to quantify the orderliness of the three-dimensional structures of hydrogen-bonded systems (water, methanol, ethanol, and 1-propanol), as well as binary mixtures (methanol-ethanol, methanol-1-propanol, and ethanol-1-propanol) through molecular dynamics simulations. SVor is calculated for the VP considering all atom types and oxygen atoms to exclusively study the hydrogen bonds. The SVor for all atoms shows that the smaller molecules have a more ordered structure than the larger ones whereas the SVor calculated considering only oxygen atoms decreases as the alkyl chain length of the molecules increases indicating the smaller molecules have more ways to form hydrogen bonds than the larger ones. SVor calculated for binary mixtures for varying concentrations shows a similar trend.

Precise Confinement and Position Distribution of Atomic Cu and Zn in ZSM-5 for CO2 Hydrogenation to Methanol

CuZn-based catalysts are widely used in CO2 hydrogenation, which may effectively convert CO2 to methanol and alleviate CO2 emission issues. The precise design of a model catalyst with a clear atomic structure is crucial in studying the relationship between structure and catalytic activity. In this work, a one-pot strategy was used to synthesize CuZn@ZSM-5 catalysts with approximately two Cu atoms and one Zn atom per unit cell. Atomic Cu and Zn species are confirmed to be located in the [54.6.102] and [62.104] tilings, respectively, by using magic-angle spinning nuclear magnetic resonance spectroscopy (MAS NMR), synchrotron X-ray powder diffraction (SXRD) and high-signal-to-noise-ratio annular dark field scanning transmission electron microscopy (High SNR ADF-STEM). Catalytic hydrogenation of CO2 to methanol was used as a model reaction to investigate the activity of the catalyst with confined active species. Compared to the Cu@ZSM-5, Zn@ZSM-5 and their mixture, the CuZn@ZSM-5 catalyst with a close Cu–Zn distance of 4.5 Å achieves a comparable methanol space–time yield (STY) of 92.0 mgmethanol·gcatal−1·h−1 at 533 K and 4 MPa with high stability. This method is able to confine one to three metal atoms in the zeolite channel and avoid migration and agglomeration of the atoms during the reaction, which maintains the stability of the catalyst and provides an efficient way for adjustment of the type and number of metal atoms along with the distances between them in zeolites.

Can Machine Learning Predict the Reaction Paths in Catalytic CO2 Reduction on Small Cu/Ni Clusters?

In this paper, we explore the catalytic CO2 reduction process on 13-atom bimetallic nanoclusters with icosahedron geometry. As copper and nickel atoms may be positioned in different locations and either separated into groups or uniformly distributed, the possible permutations lead to many unnecessary simulations. Thus, we have developed a machine learning model aimed at predicting the energy of a specific group of bimetallic (CuNi) clusters and their interactions with CO2 reduction intermediates. The training data for the algorithm have been provided from DFT simulations and consist only of the coordinates and types of atoms, together with the related potential energy of the system. While the algorithm is not able to predict the exact energy of the given complex, it is able to select the candidates for further optimization with reasonably good certainty. We have also found that the stability of the complex depends on the type of central atom in the nanoparticle, despite it not directly interacting with the intermediates.

KGDiff: towards explainable target-aware molecule generation with knowledge guidance.

Designing 3D molecules with high binding affinity for specific protein targets is crucial in drug design. One challenge is that the atomic interaction between molecules and proteins in 3D space has to be taken into account. However, the existing target-aware methods solely model the joint distribution between the molecules and proteins, disregarding the binding affinities between them, which leads to limited performance. In this paper, we propose an explainable diffusion model to generate molecules that can be bound to a given protein target with high affinity. Our method explicitly incorporates the chemical knowledge of protein-ligand binding affinity into the diffusion model, and uses the knowledge to guide the denoising process towards the direction of high binding affinity. Specifically, an SE(3)-invariant expert network is developed to fit the Vina scoring functions and jointly trained with the denoising network, while the domain knowledge is distilled and conveyed from Vina functions to the expert network. An effective guidance is proposed on both continuous atom coordinates and discrete atom types by taking advantages of the gradient of the expert network. Experiments on the benchmark CrossDocked2020 demonstrate the superiority of our method. Additionally, an atom-level explanation of the generated molecules is provided, and the connections with the domain knowledge are established.