Cluster Configuration Research Articles

Influence of ZrF4 additive on the local structures and thermophysical properties of molten NaF-BeF2

Molten NaF-BeF2 (FNaBe) eutectic salt containing a small amount of ZrF4 additive has attracted extensive attention in the application of fuel salt carriers for the Gen IV nuclear reactor. Herein, the local structures of radial and angular distribution functions (RDFs and ADFs) as well as the thermophysical properties of densities, thermal conductivities, and viscosities for molten FNaBe-xZrF4 (x = 2.3 to 12.5 mol%) are investigated through first-principles molecular dynamics simulations. Compared with pure FNaBe, the additive hardly changes the nearest neighbor structures between cations and F anions but has a noticeable impact on the RDFs of F-F and Na-Na pairs. Besides, the interaction potential barriers between Zr-F and Be-F ionic pairs are quite similar, indicating that the ability of Zr and Be to capture F ions is equivalent. From ADFs, the quadrilateral Na-F-Na-F rings, corner-shared tetrahedral Be-F series, and corner-shared octahedral Zr-F cluster configurations are clearly identified. Overall, the density, thermal conductivity, and viscosity of molten FNaBe are prominently increased by the addition of ZrF4. Eventually, it is predicted that the optimal additive concentration of ZrF4 is 8.7 mol% considering the comprehensive physicochemical performance of the molten mixture.

Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning.

Computational modeling of atmospheric molecular clusters requires a comprehensive understanding of their complex configurational spaces, interaction patterns, stabilities against fragmentation, and even dynamic behaviors. To address these needs, we introduce the Jammy Key framework, a collection of automated scripts that facilitate and streamline molecular cluster modeling workflows. Jammy Key handles file manipulations between varieties of integrated third-party programs. The framework is divided into three main functionalities: (1) Jammy Key for configurational sampling (JKCS) to perform systematic configurational sampling of molecular clusters, (2) Jammy Key for quantum chemistry (JKQC) to analyze commonly used quantum chemistry output files and facilitate database construction, handling, and analysis, and (3) Jammy Key for machine learning (JKML) to manage machine learning methods in optimizing molecular cluster modeling. This automation and machine learning utilization significantly reduces manual labor, greatly speeds up the search for molecular cluster configurations, and thus increases the number of systems that can be studied. Following the example of the Atmospheric Cluster Database (ACDB) of Elm (ACS Omega, 4, 10965-10984, 2019), the molecular clusters modeled in our group using the Jammy Key framework have been stored in an improved online GitHub repository named ACDB 2.0. In this work, we present the Jammy Key package alongside its assorted applications, which underline its versatility. Using several illustrative examples, we discuss how to choose appropriate combinations of methodologies for treating particular cluster types, including reactive, multicomponent, charged, or radical clusters, as well as clusters containing flexible or multiconformer monomers or heavy atoms. Finally, we present a detailed example of using the tools for atmospheric acid-base clusters.

Improved Configurational Sampling Protocol for Large Atmospheric Molecular Clusters.

The nucleation process leading to the formation of new atmospheric particles plays a crucial role in aerosol research. Quantum chemical (QC) calculations can be used to model the early stages of aerosol formation, where atmospheric vapor molecules interact and form stable molecular clusters. However, QC calculations heavily depend on the chosen computational method, and when dealing with large systems, striking a balance between accuracy and computational cost becomes essential. We benchmarked the binding energies and structures and found the B97-3c method to be a good compromise between the accuracy and computational cost for studying large cluster systems. Further, we carefully assessed configurational sampling procedures for targeting large atmospheric molecular clusters containing up to 30 molecules (approximately 2 nm in diameter) and proposed a funneling approach with highly improved accuracy. We find that several parallel ABCluster explorations lead to better guesses for the cluster global energy minimum structures than one long exploration. This methodology allows us to bridge computational studies of molecular clusters, which typically reach only around 1 nm, with experimental studies that often measure particles larger than 2 nm. By employing this workflow, we searched for low-energy configurations of large sulfuric acid-ammonia and sulfuric acid-dimethylamine clusters. We find that the binding free energies of clusters containing dimethylamine are unequivocally more stable than those of the ammonia-containing clusters. Our improved configurational sampling protocol can in the future be applied to study the growth and dynamics of large clusters of arbitrary compositions.

Molecular dynamics study on the dissolution characteristics and cluster formation of Li2CO3 in supercritical water

Recycling lithium from electronic waste is the focus of current research on waste treatment and resource utilization, and environmentally friendly supercritical water technology is an important recycling method. It is necessary to study the dissolution and precipitation characteristics of lithium salt in supercritical water. In this paper, molecular dynamics (MD) and quantum mechanics (QM) were used to study the dissolution properties of Li2CO3 in supercritical water and the structure and properties of clusters. The potential mean force (PMF), radial distribution function (RDF), and coordination number (CN) showed that the solubility of Li2CO3 in supercritical water was more affected by temperature, and the trend was opposite to that of temperature, and the ion pairs in solution were mostly in CIP state. Cluster searches were performed for the cluster configurations and their distribution probabilities of CIP states in supercritical water. The electrostatic potential distribution and intermolecular interactions of the initial cluster configuration in the supercritical solution of Li2CO3 are calculated by the QM method. In this study, the cluster configuration with a planar symmetry structure accounted for the largest proportion, and the lowest free energy was −431.86308400 a.u.

Overlapping Graph Clustering in Attributed Networks via Generalized Cluster Potential Game

Overlapping graph clustering is essential to understand the nature and behavior of real complex systems including human interactions, technical systems and transportation network. However, in addition of topological structure, many real-world networked systems contain spare factors, i.e., attributes of networks. Despite the considerable efforts that have been made in graph clustering, they only concentrate on the topological structure, which lack a profound understanding of cluster configuration on attributed graphs. To address this great challenge, in this article, we propose a new overlapping graph clustering algorithm by integrating the topological and attributive information into a cluster potential game (CPG). Firstly, a generalized definition of the utility function is provided, which measures the payoff of each node based on different node-to-cluster distance functions. It is worth mentioning that the model we proposed is able to associate with the classic ordinal potential game well. Then, we define the measures of both tightness and the homogeneity in each cluster, and introduce a novel two-way selection mechanism. The goal is to extend the flexibility of the cluster potential game, so that one can achieve a win-win situation between nodes and clusters. Finally, a distributed and heterogeneous multiagent system (DHMAS) is carefully designed based on a fast self-learning algorithm (SLA) for attributed overlapping graph clustering. Two series of experiments are implemented in multi-types datasets and the results verify the effectiveness and the scalability after the comparison with the most advanced approaches of literature.

Neural Network Water Model Based on the MB-Pol Many-Body Potential.

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor-liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. In order to address this limitation, we developed a "deep potential" neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor-liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

An enhanced distributed framework for real-time performance testing of large scale IoT dataset using big data analytic tools

<p>The demand for analyzing enormous IoT datasets is rising in parallel with the popularity of the IoT. There are considerable obstacles to effective processing and analysis due to the amount, velocity, and variety of IoT data. In this research, we present a distributed system that makes use of big data analytic tools like Apache Hive, Spark, and Hadoop to efficiently test the performance of massive IoT datasets. The framework addresses the lack of a comprehensive solution by providing a scalable and fault-tolerant architecture. We discuss the motivation behind real-time performance testing in the context of big data analytics for IoT datasets and highlight the need for a distributed framework. A literature review is conducted to explore existing performance testing frameworks, big data analytic tools, and approaches for performance testing big data analytics. The proposed framework’s key components, including dataset generation, test scenario specification, cluster configuration, performance metrics collection, analysis and visualization modules, and implementation details, including tool choices, are discussed. An experimental evaluation is conducted to validate the framework’s performance, and it is suggested to incorporate blockchain technology. Overall, the proposed framework offers a comprehensive solution for real-time performance testing of large-scale IoT datasets, providing organizations and researchers with a valuable tool to ensure efficient and reliable IoT data processing and analysis.</p>

Trihydrogen Cation Helium Clusters: A New Potential Energy Surface.

We present a new analytical potential energy surface (PES) for the interaction between the trihydrogen cation and a He atom, , in its electronic ground state. The proposed PES has been built as a sum of two contributions: a polarization energy term due to the electric field generated by the molecular cation at the position of the polarizable He atom, and an exchange-repulsion and dispersion interactions represented by a sum of "atom-bond" potentials between the three bonds of and the He atom. All parameters of this new PES have been chosen and fitted from data obtained from high-level ab-initio calculations. Using this new PES plus the Aziz-Slaman potential for the interaction between Helium atoms and assuming pair-wise interactions, we carry out classical Basin-Hopping (BH) global optimization, semiclassical BH with Zero Point Energy corrections, and quantum Diffusion Monte Carlo simulations. We have found the minimum energy configurations of small He clusters doped with , , with N=1-16. The study of the energies of these clusters allows us to find a pronounced anomaly for N=12, in perfect agreement with previous experimental findings, which we relate to a greater relative stability of this aggregate.

Toward Modeling the Growth of Large Atmospheric Sulfuric Acid-Ammonia Clusters.

Studying large atmospheric molecular clusters is needed to understand the transition between clusters and aerosol particles. In this work, we studied the (SA)n(AM)n clusters with n up to 30 and the (SA)m(AM)m±2 clusters, with m = 6-20. The cluster configurations are sampled using the ABCluster program, and the cluster geometries and thermochemical parameters are calculated using GFN1-xTB. The cluster binding energies are calculated using B97-3c. We find that the addition of sulfuric acid is preferred to the addition of ammonia. The addition free energies were found to have large uncertainties, which could potentially be attributed to errors in the applied level of theory. Based on DLPNO-CCSD(T0)/aug-cc-pVTZ benchmarks of the binding energies of the large (SA)8-9(AM)10 and (SA)10(AM)10-11 clusters, we find that ωB97X-D3BJ with a large basis set is required to yield accurate binding and addition energies. However, based on recalculations of the single-point energy at r2SCAN-3c and ωB97X-D3BJ/6-311++G(3df,3pd), we show that the single-point energy contribution is not the primary source of error. We hypothesize that a larger source of error might be present in the form of insufficient configurational sampling. Finally, we train Δ machine learning model on (SA)n(AM)n clusters with n up to 5 and show that we can predict the binding energies of clusters up to sizes of (SA)30(AM)30 with a binding energy error below 0.6 %. This is an encouraging approach for accurately modeling the binding energies of large acid-base clusters in the future.

Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

SummaryClustering is an indispensable strategy that helps towards the extension of lifetime of each sensor nodes with energy stability in wireless sensor networks (WSNs). This clustering process aids in sustaining energy efficiency and extended network lifetime in sensitive and critical real‐life applications that include landslide monitoring and military applications. The dynamic characteristics of WSNs and several cluster configurations introduce challenge in the process of searching an ideal network structure, a herculean challenge. In this paper, Hybrid Chameleon Search and Remora Optimization Algorithm‐based Dynamic Clustering Method (HCSROA) is proposed for dynamic optimization of wireless sensor node clusters. It utilized the global searching process of Chameleon Search Algorithm for selecting potential cluster head (CH) selection with balanced trade‐off between intensification and extensification. It determines an ideal dynamic network structure based on factors that include quantity of nodes in the neighborhood, distance to sink, predictable energy utilization rate, and residual energy into account during the formulation of fitness function. It specifically achieved sink node mobility through the integration of the local searching capability of Improved Remora Optimization Algorithm for determining the optimal points of deployment over which the packets can be forwarded from the CH of the cluster to the sink node. This proposed HCSROA scheme compared in contrast to standard methods is identified to greatly prolong network lifetime by 29.21% and maintain energy stability by 25.64% in contrast to baseline protocols taken for investigation.

Unraveling the roles of aromatic cluster side-chain interactions on the structural stability and functional significance of psychrophilic Sphingomonas sp. glutaredoxin 3.

This study investigates the impact of aromatic cluster side-chain interactions in Grx3 (SpGrx3) from the psychrophilic Arctic bacterium Sphingomonas sp. Grx3 is a class I oxidoreductase with a unique parallel arrangement of aromatic residues in its aromatic cluster, unlike the tetrahedral geometry observed in Trxs. Hydrophilic-to-hydrophobic substitutions were made in the aromatic cluster, in β1 (E5V and Y7F), adjacent β2 (Y32F and Y32L), both β1 and β2 (E5V/Y32L), and short α2 (R47F). The hydrophobic substitutions, particularly those at or near Tyr7 (E5V, Y7F, Y32F, and R47F), increased melting temperatures and conformational stability, whereas disrupting β1-β2 interactions (Y32L and E5V/Y32L) led to structural instability of SpGrx3. However, excessive hydrophobic interactions (Y7F and E5V/Y32L) caused protein aggregation at elevated temperatures. All mutations resulted in a reduction in α-helical content and an increase in β-strand content. The R47F mutant, which formed dimers and exhibited the highest β-strand content, showed increased conformational flexibility and a significant decrease in catalytic rate due to the disturbance of β1-α2 interactions. In summary, the configuration of the aromatic cluster, especially Tyr7 in the buried β1 and Arg47 in the short α2, played crucial roles in maintaining the active conformation of SpGrx3 and preventing its protein aggregation. These modifications, reducing hydrophobicity in the central β-sheet, distinguish Grx3 from other Trx-fold proteins, highlighting evolutionary divergence within the Trx-fold superfamily and its functional versatility.

Planar NbnOm Clusters on the Au(111) Surface

Planar oxide atomic clusters are of considerable scientific interest because of their potential for enhanced catalytic activity versus their three‐dimensional counterparts. This enhancement is the result of the substrate stabilizing novel planar configurations that have an extensive periphery where catalytic reactions can occur. A class of planar NbnOm atomic clusters that are synthesized by the evaporation of metallic Nb onto an Au(111) substrate in an ultrahigh vacuum environment and subsequent oxidation at elevated temperatures is reported. The atomic structures of the clusters are determined using a combination of scanning tunneling microscopy and density functional theory. The clusters are composed of structural units with four‐, five‐, and sixfold rotational symmetry and these units can assemble to form larger planar clusters. The theoretical comparison of supported structures with their hypothetical freestanding counterparts shows that the atomic and electronic structures of the oxide clusters are significantly altered by the interaction with the Au substrate. The substrate effects include interfacial charge transfer and structural relaxation to relieve the strain in the Nb‐O bonds. The substrate interactions also reduce the energy differences between clusters of different configurations and this enables the coexistence of a large variety of cluster configurations.

Spatial Evolution and Governance Transformation of Cross-Border Regions in Institutional Transition: A Case Study of the Tokyo Bay

In the process of globalization, the transformation of production methods has triggered a restructuring of scales, resulting in the emergence of a new spatial phenomenon known as cross-border regions. Previous studies have focused on the coordination of cross-border regions, often concentrating solely on either spatial or governance aspects, and lacking a comprehensive exploration of the underlying mechanisms linking space and governance. This study examines the evolution of cross-border governance using the Tokyo Bay Area as a case study, taking into account the dual characteristics of space and governance. The findings of this research indicate a certain correlation between spatial evolution and governance mechanisms during different stages of development in the Tokyo Bay Area. Over time, the spatial configuration of the port cluster has undergone significant changes, while the governance mechanisms of the cross-border region have transitioned from informal to formal approaches. Additionally, the study summarizes both diachronic and synchronic characteristics, confirming the effectiveness of the space governance and providing an analysis of its underlying mechanisms. These findings provide valuable insights for promoting the modernization of national governance systems and governance capabilities.

Influence of Doping Technology on the Stoichiometry and Features of the Localization of B3+ Cations in LiNbO3:B Single Crystals

We have established that relatively simple calculations of the Coulomb interaction in the lattice of doped lithium niobate (LN, LiNbO3) can confirm the physical properties of real crystals. We have developed a method for the double adjustment of real XRD data for calculations of Coulomb interaction in a LN cluster. The study considers two crystals doped with boron (LN:B); LN:B(1) has been grown from a charge with 0.02 mol% B2O3, boron has been introduced by homogeneous doping, LN:B(2) has been grown from a charge with 0.547 mol% B2O3, and boron has been introduced by direct solid-state doping. XRD and Rietveld method data have been obtained for these crystals. The obtained data have been used to build a model of the LN cluster; the cluster in the calculations consists of six oxygen octahedra of the LN structure. The cluster configuration has been chosen in such a way that the structure contains two tetrahedral voids. We have studied 10 variants of filling a cluster with intrinsic cations (Li, Nb), defects, and vacancies. There are 10 of them because, in addition to the basic cations in their positions, defects are present in the structure. In terms of the defects used (NbLi, NbV), we have used only those that Rietveld found for these exact LN:B crystals, and the vacancy in the niobium octahedron (VNb) compensates for these defects, according to the models known for LN. The energy of the Coulomb interaction between the cluster structure of a real crystal and the boron cation localized in it in different positions has been calculated for each of the configurations. Calculations have demonstrated that B is more likely to be embedded near a defect than in a regular structure. This means that boron positively influences the local substructure of doped LN crystals, not only structures the melt during crystal growth. Calculations have shown that the type and location of structural defects affect the position of boron in the structure of a LN crystal. Calculations have also shown that LN:B(1) has a more stable structure, including optical damage resistance. The photoinduced light scattering (PILS) patterns and conoscopic patterns confirm this conclusion for the studied LN:B crystals. The information obtained in this study may be useful for interpreting the defective structure of LN crystals co-doped with boron and metals (Mg, Zn, etc.). This will supplement the knowledge available in the literature regarding models that describe the structure of complexly doped LN crystals.

Effects of size and temperature on the configurations of the Re-Ni clusters

The diffusion and growth dynamic processes of Re adatoms on a Wulff Ni substrate were simulated by combining molecular dynamics and embedded atom method. The configurations of the Re adatoms on Wulff1289 clusters at 100, 200, 300, and 600 K at different growth stages were compared. Results show that the optimal core-shell configurations were achieved at 100 K. The factors of substrate size (201, 586 and 1289 atoms) on cluster growth processes were analyzed. The quality of core-shell clusters obtained at different temperatures and sizes varies. The optimal quality of core-shell structure of Re adatoms growing on wulff586 Ni cluster was obtained at 200 K, while the corresponding temperature for Wulff201 Ni cluster was at 100 K. These findings indicate that optimal core-shell clusters can be obtained within our study’s size range at low temperatures, and the cluster size and temperature should be fully considered when designing the cluster configuration.

Entropy regularization in probabilistic clustering

Bayesian nonparametric mixture models are widely used to cluster observations. However, one major drawback of the approach is that the estimated partition often presents unbalanced clusters’ frequencies with only a few dominating clusters and a large number of sparsely-populated ones. This feature translates into results that are often uninterpretable unless we accept to ignore a relevant number of observations and clusters. Interpreting the posterior distribution as penalized likelihood, we show how the unbalance can be explained as a direct consequence of the cost functions involved in estimating the partition. In light of our findings, we propose a novel Bayesian estimator of the clustering configuration. The proposed estimator is equivalent to a post-processing procedure that reduces the number of sparsely-populated clusters and enhances interpretability. The procedure takes the form of entropy-regularization of the Bayesian estimate. While being computationally convenient with respect to alternative strategies, it is also theoretically justified as a correction to the Bayesian loss function used for point estimation and, as such, can be applied to any posterior distribution of clusters, regardless of the specific model used.

Calculation of the recombination radii between various point defects and defect clusters in nickel via kinetic Activation Relaxation Technique

The recombination radii between various point defects and defect clusters are essential input parameters for mathematical and computational models of radiation-induced damage evolution. In this work, such recombination radii is calculated by the kinetic Activation Relaxation Technique (k-ART)—an off-lattice, self-learning kinetic Monte Carlo algorithm—in conjunction with molecular dynamics simulations. We found vacancy clusters exhibit larger average recombination radii with interstitials than single vacancies, reaching as large as 2.2a0 (a0 is the lattice parameter of nickel). Such recombination enhancement can be attributed to the intensified distortion near cluster surfaces. Unlike vacancy clusters, free surfaces feature smaller recombination radii than single vacancies. Their recombination radii are correlated with the angle between surface normal and <110> directions. Finally, the recombination radii of interstitial clusters are larger than vacancy clusters and strongly depends on the cluster configurations. Overall, the recombination radii of most internal defect clusters range from ∼2a0 to ∼2.4a0. Because previous research has found that recombination radii affect the density-size distribution of point defect clusters but not the fraction of a particular defect, our analysis suggests that given an accurate initial estimate of cluster density-size distributions, choosing a uniform recombination radius for different clusters in object kinetic Monte Carlo simulations should be sufficient to predict subsequent microstructural evolution.

The Impact of Residential Cluster Layout on Building Energy Consumption and Carbon Emissions in Regions with Hot Summers and Cold Winters in China

Since the early 2010s, building energy consumption in regions in China with hot summers and cold winters has experienced an average annual growth rate of 6.5%, while building carbon emissions demonstrated an average annual growth rate of 3.7%. This underscores the pressing need to reduce building energy consumption and carbon emissions. The layout of residential clusters plays a critical role in determining the effective shading coefficient, which directly impacts solar radiation gain and subsequently affects energy consumption and carbon emissions. To explore this correlation and optimize the layout configuration of residential clusters to achieve the objective of minimizing energy consumption and carbon emissions in buildings, our study employed ECOTECT 2011 software to assess the layout attributes of different residential clusters through an analysis of the effective shading coefficient. Furthermore, using VirVil-HTB2 17_04_21 software, we simulated the solar radiation, building energy consumption, and carbon emissions for different residential cluster layouts. To examine the interplay between solar radiation, energy consumption, and carbon emissions, SPSS 27 software was used. The findings revealed that different residential cluster configurations exhibit unique effective shading coefficients, substantially influencing the solar radiation received by buildings and, consequently, their thermal performance. Our research reveals that adopting a staggered layout can lead to a reduction in average operating energy consumption by up to 2.23% and cooling energy consumption by up to 7.17%, compared to an enclosed layout. Similarly, enclosed layouts can contribute to a decrease in heating energy consumption by up to 4.06%, in contrast to courtyard layouts. Additionally, scattered layouts can effectively reduce carbon emissions by up to 0.95% when compared to courtyard layouts.

Structures, Electronic, and Magnetic Properties of CoKn (n = 2-12) Clusters: A Particle Swarm Optimization Prediction Jointed with First-Principles Investigation.

Transition-metal-doped clusters have long been attracting great attention due to their unique geometries and interesting physical and/or chemical properties. In this paper, the geometries of the lowest- and lower-energy CoKn (n = 2-12) clusters have been screened out using particle swarm optimization and first principles relaxation. The results show that except for CoK2 the other CoKn (n = 3-12) clusters are all three-dimensional structures, and CoK7 is the transition structure from which the lowest energy structures are cobalt atom-centered cage-like structures. The stability, the electronic structures, and the magnetic properties of CoKn clusters (n = 2-12) clusters are further investigated using the first principles method. The results show that the medium-sized clusters whose geometries are cage-like structures are more stable than smaller-sized clusters. The electronic configuration of CoKn clusters could be described as 1S1P1D according to the spherical jellium model. The main components of petal-shaped D molecular orbitals are Co-d and K-s states or Co-d and Co-s states, and the main components of sphere-like S molecular orbitals or spindle-like P molecular orbitals are K-s states or Co-s states. Co atoms give the main contribution to the total magnetic moments, and K atoms can either enhance or attenuate the total magnetic moments. CoKn (n = 5-8) clusters have relatively large magnetic moments, which has a relation to the strong Co-K bond and the large amount of charge transfer. CoK4 could be a magnetic superatom with a large magnetic moment of 5 μB.

Quantum cluster equilibrium prediction of liquid ethanol

Quantum cluster equilibrium theory (QCE) has been widely used to determine the properties of pure and binary mixture of liquids. The main limitation of the application of QCE is the exploration of different possible clusters formed by the solvent molecules. Therefore, in this study, we applied the QCE theory to predict liquid properties of ethanol after thorough exploration of the potential energy surfaces (PESs) of the ethanol clusters from dimer to hexamer. The exploration started by generating possible structures using classical molecular dynamics followed by optimizations at the MP2/aug-cc-pVDZ level of theory. 484 different configurations of the ethanol clusters have been finally used in the QCE theory. The results show that the population of liquid ethanol is constituted from the contribution of hexamer, pentamer, and tetramer. In addition, we noted that the ethanol monomer, dimer and trimer do not contribute to the population of liquid ethanol. Furthermore, based on the predicted population of the liquid ethanol, we calculated its infrared spectrum at different temperatures. The calculated infrared spectrum is found to be in qualitative agreement with experiment. Some thermodynamic properties, such as the heat capacity, are also predicted to be in good agreement with experiment.