Atomic Model Research Articles

Hirshfeld atom refinement and dynamical refinement of hexagonal ice structure from electron diffraction data.

Reaching beyond the commonly used spherical atomic electron density model allows one to greatly improve the accuracy of hydrogen atom structural parameters derived from X-ray data. However, the effects of atomic asphericity are less explored for electron diffraction data. In this work, Hirshfeld atom refinement (HAR), a method that uses an accurate description of electron density by quantum mechanical calculation for a system of interest, was applied for the first time to the kinematical refinement of electron diffraction data. This approach was applied here to derive the structure of ordinary hexagonal ice (Ih). The effect of introducing HAR is much less noticeable than in the case of X-ray refinement and it is largely overshadowed by dynamical scattering effects. It led to only a slight change in the O-H bond lengths (shortening by 0.01 Å) compared with the independent atom model (IAM). The average absolute differences in O-H bond lengths between the kinematical refinements and the reference neutron structure were much larger: 0.044 for IAM and 0.046 Å for HAR. The refinement results changed considerably when dynamical scattering effects were modelled - with extinction correction or with dynamical refinement. The latter led to an improvement of the O-H bond length accuracy to 0.021 Å on average (with IAM refinement). Though there is a potential for deriving more accurate structures using HAR for electron diffraction, modelling of dynamical scattering effects seems to be a necessary step to achieve this. However, at present there is no software to support both HAR and dynamical refinement.

Ab initio study on changing behaviors of lithium-rich layered composite cathode materials

The lithium-rich composite-layered oxide, xLi2MnO3•(1-x)LiM'O2, where M’ represents transition metals, is a promising cathode material with excellent capacity at high working voltage for lithium-ion batteries (LIBs). Nevertheless, an unusual charge-discharge feature is observed for this material with sloping and plateau regions during the initial charging. In this work, ab initio calculations based on density functional theory were employed to examine the stability of xLi2MnO3•(1-x)Li(Ni1/3Co1/3Mn1/3)O2 composite-layered cathode materials during the first charging. Optimized atomistic models for different compositions (x = 0.0, 0.3, 0.4, 0.5, 0.7, and 1) unveil a pseudo-rhombohedral or monoclinic-like structure within the solid solution phase, with simulated X-ray diffraction patterns closely matching experimental data. Using these atomistic models, the first charging process of the 0.4Li2MnO3•0.6Li(Ni1/3Co1/3Mn1/3)O2 cathode were investigated. There exists phase transition from a layered to a spinel structure. Additionally, Bader charge analysis reveals intriguing trends during the first delithiation process, where the valence states of Ni and Co gradual increase, while that of Mn almost remains unchanged. Simultaneously, significant oxidation of O is observed. This finding leads to further evaluations on oxygen vacancy formation in comparison to the energy required for delithiation. Defect formation energy calculations demonstrate that the plateau in charging curve is resulted from oxygen vacancy formation during the first charging. By empirical fitting oxygen chemical potential at μO = -1.200 eV, the calculated charging-discharging curves aligned well with experimental data during the first and second charging. Furthermore, the pathways of lithium and oxygen deintercalation are elucidated. This work contributes fundamental understandings on the xLi2MnO3•(1-x)LiM'O2 composite-layered oxides as cathode for LIBs, providing valuable guidance for further developments in high-capacity Co-lean cathode materials.

Grain boundary segregation for the Fe-P system: Insights from atomistic modeling and Bayesian inference

In this work we re-assess experimental data for grain boundary (GB) segregation of P in bcc Fe with thermodynamic and statistical methods. The data are based on Auger-Electron-Spectroscopy (AES) measurements which have provided P GB concentrations for various bulk contents and temperatures for ferrite. While in the previous investigations of this system a single-site McLean equation was used to extract segregation enthalpy and entropy, we employ multi-site segregation models as suggested by atomistic simulations. We use a recently introduced methodology based on inter-atomic potentials to calculate the segregation energy spectrum, as well as vibrational entropy and solute-solute interactions of P in polycrystalline Fe, thereby obtaining a three-dimensional distribution of energy, entropy and interactions. Using this trivariate distribution we predict P GB concentrations and compare them to the experimental measurements. Furthermore, we calibrate the parameters of physical models of increasing complexity to the experimental data with an inverse modeling approach based on Bayesian inference. While it is not possible to seamlessly link the results from AES to atomistic modeling, our investigation provides significant new insights for thermodynamic modeling of GB segregation. The vibrational entropy deduced from experiment differs from physics based atomistic simulations even when taking the spectral nature of energy, entropy and interactions into account. However, the correlations of the quantities are in good agreement when considering the trivariate model. Our investigation highlights the need for new and more accurate experimental datasets of GB segregation.

Protonation of histidine rings using quantum-mechanical methods.

Histidine can be protonated on either or both of the two N atoms of the imidazole moiety. Each of the three possible forms occurs as a result of the stereochemical environment of the histidine side chain. In an atomic model, comparing the possible protonation states in situ, looking at possible hydrogen bonding and metal coordination, it is possible to predict which is most likely to be correct. A more direct method is described that uses quantum-mechanical methods to calculate, also in situ, the minimum geometry and energy for comparison, and therefore to more accurately identify the most likely protonation state.

Molecular Dynamics Simulations of Gas Transport Properties in Cross-Linked Polyamide Membranes: Tracing the Morphology and Addition of Silicate Nanotubes.

This study employs molecular dynamics (MD) simulations to fundamentally provide insight into the role of cross-link density in the CO2 separation properties of interfacially polymerized polyamide (PA) membranes. For this purpose, two atomistic models of pure polyamide membranes with different cross-link densities are constructed by MD simulations to conceptually determine how the fractional free volume of polyamide affects the gas separation performance of the membrane. The PA membrane with a lower cross-link density (LCPA) shows a higher gas diffusion coefficient, a lower gas solubility coefficient, and a higher gas permeability than the PA membrane with a higher cross-link density (HCPA). Moreover, the pristine and modified silicate nanotubes (SNTs) as the fast gas transport channels are incorporated into the polyamide membranes to assess the effect of the SNT/PA interface chemistry on the CO2 separation properties of the membranes. SNTs are systematically modified by three modifying agents with different CO2-philic groups and different interfacial interaction energies with the polyamide matrix. The results of MD simulations demonstrate that the incorporation of silicate nanotubes into the PA matrix increases the gas diffusivity and permeability and decreases the CO2/gas selectivity. Moreover, the membranes containing modified SNTs possessing high CO2-philicity and high SNTs/PA interfacial interactions show a high CO2 separation performance.

Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning.

Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order-disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale.

AQuaRef: Machine learning accelerated quantum refinement of protein structures.

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions relying solely on nonbonded repulsions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. We present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 neural network potential mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data.

Discovery of a giant lattice in Mg97Zn1Yb2 alloy

The Mg-rich corner of Mg–metal (M)–rare earth element (REE) ternary systems results in a long-period stacking-ordered (LPSO) phase that increases the strength and elongation of the Mg alloy. Using Yb at the REE position does not result in the LPSO phase; instead, it forms unique crystalline structures with intriguing properties. In this study, we found a new giant lattice belonging to the P6/mmc space group by quenching the Mg97Zn1Yb2 alloy at 723 K. The giant lattice, which precipitates around the α-Mg matrix with a chemical composition of Mg75Zn7Yb18 (at%), comprises a basic structure and a superlattice. We developed an atomic configuration model that expresses the basic structure as alternating stacking of three types of layers. The observed features indicate that the structure of the giant lattice is similar to that of the approximant quasicrystal reported for Mg–M–REE ternary systems.

Identifying point defects and ordering in the high-entropy layered oxide Li1.5MO3-δ (M=Mn, Al, Fe, Co, Ni) for energy storage applications

High-entropy layered oxides (HELOs) represent a very promising class of next-generation battery cathodes, combining the well-studied properties of layered cathode materials such as LiCoO2 with the chemical tunability and stability of high-entropy materials. HELO materials often form particles with complex defects and domain structures, complicating accurate characterization of structure and cation mixing. Understanding disorder and order in HELO materials is necessary for understanding their performance and utility as cathodes. Here we demonstrate the characterization of the HELO Li1.5MO3-δ (M = Mn, Al, Fe, Co, Ni), wherein X-ray powder diffraction, transmission electron microscopy imaging, and electron diffraction patterns are analyzed to reveal the presence of ordering in the HELO, with imaging and diffraction simulation employed to compare experimental results to atomic modeling. Without rigorous characterization at the atomic scale, important features such as defect ordering can be easily overlooked and therefore remain unconsidered when interpreting experimental results.

Cartesian atomic cluster expansion for machine learning interatomic potentials

Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modeling in material science and chemistry. Many potentials use atomic cluster expansion or equivariant message-passing frameworks. Such frameworks typically use spherical harmonics as angular basis functions, followed by Clebsch-Gordan contraction to maintain rotational symmetry. We propose a mathematically equivalent and simple alternative that performs all operations in the Cartesian coordinates. This approach provides a complete set of polynormially independent features of atomic environments while maintaining interaction body orders. Additionally, we integrate low-dimensional embeddings of various chemical elements, trainable radial channel coupling, and inter-atomic message passing. The resulting potential, named Cartesian Atomic Cluster Expansion (CACE), exhibits good accuracy, stability, and generalizability. We validate its performance in diverse systems, including bulk water, small molecules, and 25-element high-entropy alloys.

Microstructure, electrochemical corrosion and corrosion mechanism of electroplated Ni–W alloy coating by DFT calculation

ABSTRACT A Ni–W alloy coating with a thickness of 5 μm was electroplated on N80 steel, and its electrochemical performance in a 3.5% NaCl solution was investigated. The atomic models of passive films and corrosion mechanism were analysed with the density functional theory calculation. The results show that the electroplated Ni–W alloy coating is composed of W atoms in the Ni lattice, and its surface roughness and particle size are 66.1 and 640.4 nm, respectively. The corrosion current density of Ni–W coating is lower than that of the substrate, indicating better corrosion resistance, while the corrosion potential is opposite, which is more passive than the substrate. The total impedance of Ni–W coating is higher than that of the substrate, which is attributed to the formation of NiO passive film during the corrosion process.

Integrated cryoEM structure of a spumaretrovirus reveals cross-kingdom evolutionary relationships and the molecular basis for assembly and virus entry

Foamy viruses (FVs) are an ancient lineage of retroviruses, with an evolutionary history spanning over 450 million years. Vector systems based on Prototype Foamy Virus (PFV) are promising candidates for gene and oncolytic therapies. Structural studies of PFV contribute to the understanding of the mechanisms of FV replication, cell entry and infection, and retroviral evolution. Here we combine cryoEM and cryoET to determine high-resolution in situ structures of the PFV icosahedral capsid (CA) and envelope glycoprotein (Env), including its type III transmembrane anchor and membrane-proximal external region (MPER), and show how they are organized in an integrated structure of assembled PFV particles. The atomic models reveal an ancient retroviral capsid architecture and an unexpected relationship between Env and other class 1 fusion proteins of the Mononegavirales. Our results represent the de novo structure determination of an assembled retrovirus particle.

Human cardiac β-myosin powerstroke energetics: Thin filament, Pi displacement, and mutation effects

The powerstroke of human cardiac β-myosin is an important stage of the cross-bridge cycle that generates force for muscle contraction. However, the starting structure of this process has never been resolved, and the relative timing of the powerstroke and inorganic phosphate (Pi) release is still controversial. In this study, we generated an atomistic model of myosin on the thin filament and utilized metadynamics simulations to predict the absent starting structure of the powerstroke. We demonstrated that the displacement of Pi from the active site during the powerstroke is likely necessary, reducing the energy barrier of the conformation change. The effects of the presence of the thin filament, the hypertrophic cardiomyopathy mutation R712L, and the binding of mavacamten on the powerstroke process were also investigated.

Accurately Modeling RNA Stem-Loops in an Implicit Solvent Environment.

Ribonucleic acid (RNA) molecules can adopt a variety of secondary and tertiary structures in solution, with stem-loops being one of the more common motifs. Here, we present a systematic analysis of 15 RNA stem-loop sequences simulated with molecular dynamics simulations in an implicit solvent environment. Analysis of RNA cluster ensembles showed that the stem-loop structures can generally adopt the A-form RNA in the stem region. Loop structures are more sensitive, and experimental structures could only be reproduced with modification of CH···O interactions in the force field, combined with an implicit solvent nonpolar correction to better model base stacking interactions. Accurately modeling RNA with current atomistic physics-based models remains challenging, but the RNA systems studied herein may provide a useful benchmark set for testing other RNA modeling methods in the future.

Investigation of the Bending Behavior in Silicon Nanowires: A Nanomechanical Modeling Perspective

Nanowires (NWs) play a crucial role across a wide range of disciplines such as nanoelectromechanical systems, nanoelectronics and energy applications. As NWs continue to reduce in dimensions, their mechanical properties are increasingly affected by surface attributes. This study conducts a comprehensive examination of nanomechanical models utilized for interpreting large deformations in the bending response of silicon NWs. Specifically, the Heidelberg, Hudson, Zhan, SimpZP and ExtZP nanomechanical models are explored regarding their capability to predict the elastic properties of silicon NWs with varying critical dimensions and crystal orientations. Molecular dynamics simulations are employed to model silicon NWs with unreconstructed surface states. The calculation of intrinsic stresses and the methodology for quantifying surface properties, including surface stresses and surface elasticity constants, are carried out using atomistic modeling. The findings reveal significant disparities of up to 100 GPa among nanomechanical models in interpreting a singular force-deflection response obtained for a silicon NW. Inadequate consideration of surface and intrinsic effects in nanomechanical modeling of NWs leads to substantial variability in their mechanical properties. This investigation yields valuable insights into the surface characteristics of silicon NWs, thereby enhancing our understanding of the essential role played by nanomechanical models in the intricate interpretation of mechanical properties at the nanoscale.

Study of Ammonia Adsorption on Magnetite Surfaces with Molecular Dynamics Simulations.

The present study proposes an atomistic molecular dynamics model system of a magnetite (Fe3O4) {111} surface. The effect of temperature on the adsorption process of ammonia (NH3) at low concentrations in the aqueous phase has been considered. The molecular dynamics simulations were carried out using the Clay force field (Clay FF) with a modification for the iron atoms in the NPT ensemble at a pressure of 90 bar. The considered system was heated in a temperature range from 293 to 473 K, and additional relaxations were performed at temperatures of interest. Within the scope of this study, the basic parameters of the magnetite surface were calculated and the distances between the ammonia molecules and the surface were determined. A general idea of the degree and rate of adsorption at specific temperatures was obtained. The calculation results were compared to the experimental data where possible and to other available simulations of adsorption processes on metal oxides.

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis.

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Here, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.

A Temperature-Transferable Coarse-Grained Model for Poly(lactic Acid) Melts.

We present a temperature-transferable coarse-grained (CG) model for poly(lactic acid) (PLA), specifically designed to replicate its volumetric properties and solubility parameter in the molten state. The CG-bonded potentials were derived by using the iterative Boltzmann inversion (IBI) optimization method to match structural properties from detailed atomistic models. A parametrization workflow was employed to determine nonbonded interaction parameters with temperature-dependent corrections that provide agreement with the target properties across the melting temperature range. The CG model successfully replicates key features of the PLA melt. It satisfactory reproduces the density and solubility parameter, maintains the dependence of chain conformation on molecular weight, and captures the dynamic behavior through agreement in scaled mean squared displacement and diffusion coefficients with the atomistic model. Additionally, the CG model offers much faster equilibration compared with the atomistic model. The proposed model is expected to be particularly useful for investigating the miscibility characteristics of PLA in various blends and composites that remain challenging to explore using fully atomistic simulations or experiments.

Chiral edge transport along domain walls in magnetic topological insulator nanoribbons

Quantum anomalous Hall insulators are topologically characterized by non-zero integer Chern numbers, the sign of which depends on the direction of the exchange field that breaks time-reversal symmetry. This feature allows the manipulation of the conducting chiral edge states present at the interface of two magnetic domains with opposite magnetization and opposite Chern numbers. Motivated by this broad understanding, the present study investigates the quantum transport properties of a magnetized Bi2Se3 topological insulator nanoribbon with a domain wall (DW) oriented either parallel or perpendicular to the transport direction. Employing an atomistic tight-binding model and a non-equilibrium Green’s function formalism, we calculate the quantum conductance and explore the nature of the edge states. We elucidate the conditions leading to exact conductance quantization and identify the origin of deviations from this behavior. Our analysis shows that although the conductance is quantized in the presence of the horizontal DW, the quantization is absent in the perpendicular DW case. Furthermore, the investigation of the spin character of the edge modes confirms that the conductance in the horizontal DW configuration is spin polarized. This finding underscores the potential of our system as a simple three dimensional spin-filter device.

A Benchmark Test of High-Throughput Atomistic Modeling for Octa-Acid Host–Guest Complexes

Years of massive applications of high-throughput atomistic modeling tools such as molecular docking and end-point free energy calculations in the drug industry and academic exploration have made them indispensable parts of hierarchical screening. While the similarities between host–guest and protein–ligand complexes lead to the direct extension of techniques for protein–ligand screening to host–guest systems, the practical performance of these hit identification tools remains unclear in host-–-guest binding. Recent reports on specific host–guest complexes suggest that the experience on the accuracy ladder accumulated from protein–ligand cases could be invalid in host–guest complexes, which makes it an urgent need to perform a systematic benchmark to secure solid numerical supports and guidance of practical setups. Concerning molecular docking, there still lacks a comprehensive benchmark considering popular docking programs. As for end-point reranking, quantitative and rigorous free energy estimation via end-point formulism requires establishing statistically meaningful measurements of uncertainties due to finite sampling, which is neglected or underestimated by a significant portion in almost all main-stream applications. Further, a face-to-face comparison between different screening tools is required for the design of a hierarchical workflow. To fill the above-mentioned critical gaps, in this work, using a dataset containing tens of host–guest complexes involving basket-like macromolecular hosts from the octa acid family, we extensively benchmark seven academic docking protocols and perform post-docking end-point rescoring with twenty protocols. The resulting comprehensive benchmark provides conclusive pictures of the practical value of docking and end-point screening in OA host–guest binding.