Interatomic Interactions Research Articles

Self-Consistent Chemical Pressure Analysis: Resolving Atomic Packing Effects through the Iterative Partitioning of Space and Energy.

While planewave DFT methods offer capabilities to calculate the relative stabilities and many physical properties exhibited by solid state structures, their detailed numerical output does not easily map onto the often empirical concepts and parameters used by synthetic chemists or materials scientists. The DFT-chemical pressure (CP) method is an approach to bridging this divide by explaining or anticipating a variety of structural phenomena in terms of atomic size and packing effects, but its reliance on adjustable parameters has limited the method's predictive potential. In this article, we present the self-consistent (sc)-DFT-CP analysis, in which the criterion of self-consistency is used to automatically solve these parameterization issues. We begin by illustrating the need for this improved method with the results for a series of CaCu5-type/MgCu2-type intergrowth structures, where unphysical trends emerge that have no clear structural origin. To address these challenges, we derive iterative treatments for assigning ionicity and for the partitioning of the EEwald + Eα terms in the DFT total energy into homogeneous and localized terms. In this method, self-consistency is achieved between the input and output charges from a variation of the Hirshfeld charge scheme, while the partitioning of the EEwald + Eα terms is adjusted to create equilibrium between the net atomic pressures calculated within atomic regions and from the interatomic interactions. The behavior of the sc-DFT-CP method is then tested using electronic structure data for several hundred compounds from the Intermetallic Reactivity Database. Finally, we return to the CaCu5-type/MgCu2-type intergrowth series with the sc-DFT-CP approach, showing that trends in the series are now easily traced to changes in the thicknesses of the CaCu5-type domains and lattice mismatch at the interface. Through this analysis and the complete update to the CP schemes in the IRD, the sc-DFT-CP method is demonstrated as a theoretical tool for the investigation of atomic packing issues across intermetallic chemistry.

MODEL DECISION-MAKING SYSTEM IN THE TASK OF CHOOSING THE OPTIMAL COMPOSITION OF THE BLAST FURNACE BURDEN UNDER SPECIFIC OPERATING CONDITIONS OF BF

This paper presents the methodological basis for creating a model system for selecting the optimum composition of blast furnace burden, providing the required technical and economic performance and the melting of pig iron of the required composition. The system implements a new systematic approach to modeling the processes of directed formation of blast-furnace melts. Description of composition and properties of metallurgical systems in different states is based on an original concept of directional chemical bonding using integral parameters of interatomic interaction of components in the system. The developed complex of mathematical and physical-chemical models and criteria provides a solution to the direct problem of predicting the composition of pig iron and properties of blast furnace slag, depending on burden and technological conditions. Determination of the optimal burden composition is carried out using vector optimization methods with mandatory verification of compliance with the technological requirements of high-temperature properties of the burden. The results of testing the model system on actual industrial data of blast furnace operation are illustrated, which made it possible to formulate recommendations on the composition of the loaded feed, taking into account the available raw material and energy resources.

Impact of highly deleterious non-synonymous polymorphisms on GRIN2A protein's structure and function.

GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases.

Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.

Accurate calculation of tunneling splittings in water clusters using path-integral based methods.

Tunneling splittings observed in molecular rovibrational spectra are significant evidence for tunneling motion of hydrogen nuclei in water clusters. Accurate calculations of the splitting sizes from first principles require a combination of high-quality inter-atomic interactions and rigorous methods to treat the nuclei with quantum mechanics. Many theoretical efforts have been made in recent decades. This Perspective focuses on two path-integral based tunneling splitting methods whose computational cost scales well with the system size, namely, the ring-polymer instanton method and the path-integral molecular dynamics (PIMD) method. From a simple derivation, we show that the former is a semiclassical approximation to the latter, despite that the two methods are derived very differently. Currently, the PIMD method is considered to be an ideal route to rigorously compute the ground-state tunneling splitting, while the instanton method sacrifices some accuracy for a significantly smaller computational cost. An application scenario of such a quantitatively rigorous calculation is to test and calibrate the potential energy surfaces of molecular systems by spectroscopic accuracy. Recent progress in water clusters is reviewed, and the current challenges are discussed.

Interaction models and configurational entropies of binary MoTa and the MoNbTaW high entropy alloy

We introduce a simplified method to model the interatomic interactions of high entropy alloys based on a lookup table of cluster energies. These interactions are employed in replica exchange Monte Carlo simulations with histogram analysis to obtain thermodynamic properties across a broad temperature range. Kikuchi's cluster variation method entropy formalism is applied to directly calculate entropy from statistics on short- and long-range chemical order, and we discuss the convergence of the entropy as clusters of differing size are included. A high temperature series expansion aids in our understanding of the convergence. Computer codes implementing these methods, and supporting data, are freely available on the internet.

Single-Atom Catalysts and Dual-Atom Catalysts for CO2 Electroreduction: Competition or Cooperation?

Developing highly active and selective electrocatalysts for electrochemical reduction of CO2 can reduce environmental pollution and mitigation of greenhouse gas emission. Owing to maximal atomic utilization, the atomically dispersed catalysts are broadly adopted in CO2 reduction reaction (CO2 RR). Dual-atom catalysts (DACs), with more flexible active sites, distinct electronic structures, and synergetic interatomic interactions compared to single-atom catalysts (SACs), may have great potential to enhance catalytic performance. Nevertheless, most of the existing electrocatalysts have low activity and selectivity due to their high energy barrier. Herein, 15 electrocatalysts are explored with noble metallic (Cu, Ag, and Au) active sites embedded in metal-organic hybrids (MOHs) for high-performance CO2 RR and studied the relationship between SACs and DACs by first-principles calculation. The results indicated that the DACs have excellent electrocatalytic performance, and the moderate interaction between the single- and dual-atomic center can improve catalytic activity in CO2 RR. Four among the 15 catalysts, including (CuAu), (CuCu), Cu(CuCu), and Cu(CuAu) MOHs inherited a capability of suppressing the competitive hydrogen evolution reaction with favorable CO overpotential. This work not only reveals outstanding candidates for MOHs-based dual-atom CO2 RR electrocatalysts but also provides new theoretical insights into rationally designing 2D metallic electrocatalysts.

MeGen - generation of gallium metal clusters using reinforcement learning

The generation of low-energy 3D structures of metal clusters depends on the efficiency of the search algorithm and the accuracy of inter-atomic interaction description. In this work, we formulate the search algorithm as a reinforcement learning (RL) problem. Concisely, we propose a novel actor-critic architecture that generates low-lying isomers of metal clusters at a fraction of computational cost than conventional methods. Our RL-based search algorithm uses a previously developed DART model as a reward function to describe the inter-atomic interactions to validate predicted structures. Using the DART model as a reward function incentivizes the RL model to generate low-energy structures and helps generate valid structures. We demonstrate the advantages of our approach over conventional methods for scanning local minima on potential energy surface. Our approach not only generates isomer of gallium clusters at a minimal computational cost but also predicts isomer families that were not discovered through previous density-functional theory (DFT)-based approaches.

Topological variations in aluminum-silicon alloys through inoculation with alkali earth elements: A first-principles study

Aluminum-silicon is a simple binary eutectic system with limited solid solubility of aluminum in silicon. Hypoeutectic compositions of Al-Si are the basis of several alloys for aerospace and automotive industries. The eutectic phase mixture contains coarse plate-like silicon. Typically, alkaline earth elements such as strontium, calcium, and barium are added in minute quantities that produce a fibrous network of silicon to improve the fracture toughness. Here, we use first-principles calculations to understand the origin of these morphological variations and explain these through interatomic interactions between aluminum, silicon, and inoculant atoms. Our results show that silicon atoms cluster together and arrange themselves in a planar (100) configuration within the aluminum matrix. When strontium, calcium, or barium atoms are added to the system, these atoms prefer to be near the silicon clusters and change the planar morphology to a three-dimensional configuration with facets on (111) planes.

Syntheses, crystal structures, Hirshfeld surface analyses and energy frameworks of two 4-amino-anti-pyrine Schiff base compounds: (E)-4-{[4-(di-ethyl-amino)-benzyl-idene]amino}-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one and (E)-4-[(4-fluoro-benzyl-idene)amino]-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one.

The title Schiff base compounds, C22H26N4O (I) and C18H16FN3O (II), were each synthesized by a single-step condensation reaction. The substituted benzyl-idene ring is inclined to the pyrazole ring mean planes by 22.92 (7)° in I and 12.70 (9)° in II. The phenyl ring of the 4-amino-anti-pyrine unit is inclined to the pyrazole ring mean plane by 54.87 (7)° in I and by 60.44 (8)° in II. In the crystal of I, the mol-ecules are linked by C-H⋯O hydrogen bonds and C-H⋯π inter-actions to form layers lying parallel to (001). In the crystal of II, the mol-ecules are linked by C-H⋯O and C-H⋯F hydrogen bonds and C-H⋯π inter-actions, thereby forming layers lying parallel to (010). Hirshfeld surface analysis was employed to further qu-antify the inter-atomic inter-actions in the crystals of both compounds.

A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs the energy of their interaction. Only a scoring function that accounts for the interatomic interactions involved in binding can accurately predict binding affinity on unseen molecules. However, many scoring functions make predictions based on data set biases rather than an understanding of the physics of binding. These scoring functions perform well when tested on similar targets to those in the training set but fail to generalize to dissimilar targets. To test what a machine learning-based scoring function has learned, input attribution, a technique for learning which features are important to a model when making a prediction on a particular data point, can be applied. If a model successfully learns something beyond data set biases, attribution should give insight into the important binding interactions that are taking place. We built a machine learning-based scoring function that aimed to avoid the influence of bias via thorough train and test data set filtering and show that it achieves comparable performance on the Comparative Assessment of Scoring Functions, 2016 (CASF-2016) benchmark to other leading methods. We then use the CASF-2016 test set to perform attribution and find that the bonds identified as important by PointVS, unlike those extracted from other scoring functions, have a high correlation with those found by a distance-based interaction profiler. We then show that attribution can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. We use this information to perform fragment elaboration and see improvements in docking scores compared to using structural information from a traditional, data-based approach. This not only provides definitive proof that the scoring function has learned to identify some important binding interactions but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design.

Computation of Finite Temperature Mechanical Properties of Zeolitic Imidazolate Framework Glasses by Molecular Dynamics

Mechanical properties of amorphous phases of metal–organic frameworks (MOFs), such as MOF glasses, are difficult to determine experimentally. Moreover, computational characterization is limited by the level of theory chosen for the description of interatomic interactions and is often computationally expensive. In this work, we have extensively investigated the computation of finite temperature mechanical properties of ZIF-4 in the crystal and glass phases. We critically assessed computational methodologies including ab initio molecular dynamics, reactive force fields, and classical force fields, based on a variety of glass models. We find that ZIF-4 glasses have a larger bulk modulus than the crystal and confirm previous studies that the density is larger for the glass phases. Moreover, we confirm in the case of zeolitic imidazolate framework (ZIF) glasses the relationship between density and bulk modulus, showing that obtaining models of correct density is key to the prediction of physical properties for these systems.

Investigating Metal–Tributyl Phosphate Complexes during Supercritical Fluid Extraction of the NdFeB Magnet Using Density Functional Theory and X-ray Absorption Spectroscopy

Supercritical fluid extraction (SCFE) is gaining significant interest as a green technology for the recycling of end-of-life waste electrical and electronic equipment (WEEE). Neodymium iron boron (NdFeB) magnets, which contain large quantities of critical rare-earth elements such as neodymium, praseodymium, and dysprosium, are widely used in wind turbines and electric/hybrid vehicles. Hence, they are considered a promising secondary resource for these elements when they reach their end-of-life. Previously, the SCFE process was developed for recycling WEEE, including NdFeB; however, the process mechanism remains unexplored. Here, density functional theory, followed by extended X-ray absorption fine structure and X-ray absorption near-edge structure analyses, are utilized to determine the structural coordination and interatomic interactions of complexes formed during the SCFE of the NdFeB magnet. The results indicate that Fe(II), Fe(III), and Nd(III) form Fe(NO3)2(TBP)2, Fe(NO3)3(TBP)2, and Nd(NO3)3(TBP)3 complexes, respectively. This theory-guided investigation elucidates the complexation chemistry and mechanism during the SCFE process by rigorously determining the structural models.

Tailoring and functionalizing the graphitic-like GaN and GaP nanostructures as selective sensors for NO, NO2, and NH3 adsorbing: a DFT study.

Langmuir adsorption of gas molecules of NO, NO2, and NH3 on the graphitic GaN and GaP sheets has been accomplished using density functional theory. The changes of charge density have shown a more important charge transfer for GaN compared to GaP which acts both as the electron donor while gas molecules act as the stronger electron acceptors through adsorption on the graphitic-like GaN surface. The adsorption of NO and NO2 molecules introduced spin polarization in the PL-GaN sheet, indicating that it can be employed as a magnetic gas sensor for NO and NO2 sensing. The partial electron density states based on "PDOS" graphs have explained that the NO and NO2 states in both of GaN and GaP nanosheets, respectively, have more of the conduction band between - 5 and - 10eV, while expanded contribution of phosphorus states is close to gallium states, but nitrogen and oxygen states have minor contributions. GaN and GaP nanosheets represent having enough capability for adsorbing gases of NO, NO2, and NH3 through charge transfer from nitrogen atom and oxygen atom to the gallium element owing to intra-atomic and interatomic interactions. Ga sites in GaN and GaP nanosheets have higher interaction energy from Van der Waals' forces with gas molecules.

Excitonic Order in Strongly Correlated Systems with the Spin Crossover

Features of the formation of the magnetic structure and the exciton Bose–Einstein condensate phase of magnetic excitons in strongly correlated systems near the spin crossover have been considered with the effective Hamiltonian obtained from the two-band Hubbard–Kanamori model. The coexistence of antiferromagnetism and exciton condensate, as well as the appearance of the long-range excitonic antiferromagnetic order even in the absence of the interatomic exchange interaction, has been revealed. The role of the electron–phonon coupling has been considered.

Study of the weakly bound pair Helium–Krypton: Interatomic potential, interaction hyperpolarizabilities and hyperRayleigh spectra

Two interatomic potentials of the He–Kr mixture are empirically developed from the BFW and MTT type potentials by fitting them to the thermophysical and transport properties over a wide temperature range. The vector part b1 and septor part b3 of the interaction hyperpolarizability tensor of the He–Kr pair are obtained on the basis of several ab initio methods as functions of the interatomic distance. The collision-induced hyperRayleigh scattering (CIHRS) spectra scattered by the He–Kr pairs at room temperature are calculated in absolute units for the two proposed potentials and other potentials from the literature. The spectral moments are used as a criterion of the reliability of the computed profiles. The frequency-dependent depolarization ratio of the CIHRS spectra is evaluated. The spectral features of the CIHRS profiles due to b1 and b3 are discussed.

Comparative analysis of frictional behavior and mechanism of molybdenum ditelluride with different structures

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have layered structures with excellent tribological properties. Since the energy difference between hexagonal-molybdenum ditelluride (2H-MoTe2) and distorted octahedral-molybdenum ditelluride (1T’-MoTe2) is very small among the transition metal dichalcogenides (TMDCs), MoTe2 becomes one of the most promising candidates for phase engineering. In our experiment, we found that the friction force and friction coefficient (COF) of 2H-MoTe2 were an order of magnitude smaller than those of 1T’-MoTe2 by the atomic force microscope (AFM) experiments. The friction difference between 1T’-MoTe2 and 2H-MoTe2 was further verified in molecular dynamics (MD) simulations. The density functional theory (DFT) calculations suggest that the friction contrast is related to the difference in sliding energy barrier of the potential energy surface (PES) for a tip sliding across the surface. The PES obtained from the DFT calculation indicates that the maximum energy barrier and the minimum energy path (MEP) energy barrier of 2H-MoTe2 are both smaller than those of 1T’-MoTe2, which means that less energy needs to be dissipated during the sliding process. The difference in energy barrier of the PES could be ascribed to its larger interlayer spacing and weaker Mo–Te interatomic interactions within the layers of 2H-MoTe2 than those of 1T’-MoTe2. The obvious friction difference between 1T’-MoTe2 and 2H-MoTe2 not only provides a new non-destructive means to detect the phase transition by the AFM, but also provides a possibility to tune friction by controlling the phase transition, which has the potential to be applied in extreme environments such as space lubrication.

Enhancing the Thermoelectric Performance of GeSb4Te7 Compounds via Alloying Se.

Ge-Sb-Te compounds (GST), the well-known phase-change materials, are considered to be promising thermoelectric (TE) materials due to their decent thermoelectric performance. While Ge2Sb2Te5 and GeSb2Te4 have been extensively studied, the TE performance of GeSb4Te7 has not been well explored. Reducing the excessive carrier concentration is crucial to improving TE performance for GeSb4Te7. In this work, we synthesize a series of Se-alloyed GeSb4Te7 compounds and systematically investigate their structures and transport properties. Raman analysis reveals that Se alloying introduces a new vibrational mode of GeSe2, enhancing the interatomic interaction forces within the layers and leading to the reduction of carrier concentration. Additionally, Se alloying also increases the effective mass and thus improves the Seebeck coefficient of GeSb4Te7. The decrease in carrier concentration reduces the carrier thermal conductivity, depressing the total thermal conductivity. Finally, a maximum zT value of 0.77 and an average zT value of 0.48 (300-750 K) have been obtained in GeSb4Te5.5Se1.5. This work investigates the Raman vibration modes and the TE performance in Se-alloyed GeSb4Te7 sheddinglight on the performance optimization of other GST materials.

Interface Bonding Properties of CrAlSiN-Coated Cemented Carbides Doped with CeO2 and Y2O3 Rare Earth Oxides.

This study performed first-principle-based calculations of the interface adhesion work in interface models of three terminal systems: CrAlSiNSi/WC-Co, CrAlSiNN/WC-Co, and CrAlSiNAl/WC-Co. The results proved that the CrAlSiNSi/WC-Co and CrAlSiNAl/WC-Co interface models had the highest and lowest interface adhesion work values (4.312 and 2.536 J·m-2), respectively. Thus, the latter model had the weakest interface bonding property. On this basis, rare earth oxides CeO2 and Y2O3 were doped into the Al terminal model (CrAlSiNAl/WC-Co). Then, doping models of CeO2 and Y2O3 doped on the WC/WC, WC/Co, and CrAlSiNAl/WC-Co interfaces were established. The adhesion work value was calculated for the interfaces in each doping model. When CeO2 and Y2O3 were doped in the WC/WC and CrAlSiNAl/WC-Co interfaces, four doping models were constructed, each model contains interfaces withreduced adhesion work values, indicating deteriorated interface bonding properties. When the WC/Co interface was doped with CeO2 and Y2O3, the interface adhesion work values of the two doping models are both increased, and Y2O3 doping improved the bonding properties of the Al terminal model (CrAlSiNAl/WC-Co) more significantly than CeO2 doping. Next, the charge density difference and the average Mulliken bond population were estimated. The WC/WC and CrAlSiNAl/WC-Co interfaces doped with CeO2 or Y2O3, with decreased adhesion work, exhibited low electron cloud superposition and reduced values of charge transfer, average bond population, and interatomic interaction. When the WC/Co interface was doped with CeO2 or Y2O3, superposition of the atomic charge densities of electron clouds was consistently observed at the CrAlSiNAl/WC-Co interface in the CrAlSiNAl/WC/CeO2/Co and CrAlSiNAl/WC/Y2O3/Co models; the atomic interactions were strong, and the interface bonding strength increased. When the WC/Co interface was doped with Y2O3, the superposition of atomic charge densities and the atomic interactions were stronger than for CeO2 doping. In addition, the average Mulliken bond population and the atomic stability were also higher, and the doping effect was better.

Chirped nonlinear waves in the cubic-quintic distributed nonlinear Schrödinger equation with external trap, self-steepening and self-frequency shift

A cubic-quintic inhomogeneous nonautonomous nonlinear Schrödinger (NLS) equation with an external trap, self-steepening, and self-frequency shift that models the transmission of nonlinear waves in nonautonomous inhomogeneous systems is considered. The integrability conditions under which the equation is converted into the standard NLS equation are obtained and a variety of nonlinear wave solutions with nonlinear chirping such as one-soliton solution, two-soliton solution, Akhmediev breather (AB) solution, Ma breather (MB) solution, as well as first- and second-order rogue wave (RW) solution are reported. We show how various parameters of either the model equation or the solutions may affect the nature of nonlinear waves with their frequency chirps propagating in nonautonomous inhomogeneous systems modeled by the NLS equation under consideration. The density profiles of nonlinear waves obtained from all these solutions are analyzed. These solutions are used for investigating analytically the dynamics of nonlinear waves in Bose-Einstein condensates (BECs) with both two- and three-body interatomic interactions when the gain/loss of atoms is taken into consideration. Considering BEC systems with time-independent trap frequency, we show that under a constant gain/loss parameter, the trap parameter can be used to convert triplet second-order RWs into either doublet second-order RWs or a composite of one bright solitary wave and a doublet second-order RWs. Also, our results reveal that for BECs with constant trap frequency under kink-like gain/loss parameter, the trap parameter can be used to convert (i) triplet second-order RWs into single second-order RWs, and (ii) two-soliton into doublet rogue wave. The family of chirped nonlinear wave solutions presented in this work may prove significance for designing the manipulation and transmission of nonlinear waves. Also, the found here results may provide possibilities to manipulate experimentally some nonlinear waves such as rogue waves or two-soliton in BEC systems.