46 results about "Atom model" patented technology

The application belongs to the technical field of high-performance tool coating, and relates to a design method of a cross-scale biomimetic gradient coating based on a multi-principal-element high-entropy system, which sequentially forms a high-entropy alloy plastic layer, a high-entropy alloy / high-entropy ceramic strong and tough layer and a nanocrystalline high-entropy ceramic superhard layer on the surface of a tool base body, constructs a gradient transition structure, matches the high-entropy alloy plastic layer with a bonding phase of the tool base body, optimizes the high-entropy alloy / high-entropy ceramic strong and tough layer material, improves the interface bonding force, adopts thermal-mechanical coupling calculation to optimize the gradient transition design, constructs an atomic model and optimizes the microstructure based on molecular dynamics simulation, establishes a finite element model to simulate thermal cycles and mechanical loads, optimizes the interface performance, constructs a mathematical model by using a BP neural network and a particle swarm optimization algorithm, and realizes the gradual transition design of the tool base body and the coating. The application solves the problems of large brittleness, insufficient adhesion and limited thermal stability of the tool coating interface, and improves the interface bonding performance of the coating, the stability and durability of the coating tool.

A method for identifying compounds with threshold activity against a target macromolecule first generates multiple poses for each compound fragment in a plurality of compound fragments against an atomic model of the target macromolecule. This creates a collection of configurations, or a “pose set,” for the compound fragments. Each pose is associated with a subset of interaction features drawn from a broader set of such features. Each feature corresponds to a subregion of the target macromolecule's atomic model. Each pose is quantified by application to a physics model. This assigns a score to the interaction features associated with the poses. A binding hypothesis is formed for the macromolecule, using the collection of interaction features and their corresponding scores. From this hypothesis, derived compounds are identified. These derived compounds are tested for their activity against the macromolecule, leading to the identification of those that exhibit the desired threshold activity.

The invention relates to the technical field of metal powder metallurgy process simulation. The high-entropy alloy hot pressing sintering and nanoindentation coupled molecular dynamics simulation method comprises the steps that a three-dimensional multi-particle atomic model of a high-entropy alloy is built, and initial boundary conditions are set; adjusting an initial boundary condition to be a contractility boundary condition, setting process parameters of hot pressing sintering on the model, and executing molecular dynamics simulation of the hot pressing sintering process; performing molecular dynamics and Monte Carlo hybrid simulation on the sintered model; mechanical boundary conditions are set for the optimized atomic model, a virtual indenter is controlled to be pressed into the optimized atomic model, molecular dynamics simulation of a nanoindentation test is executed, and physical field response parameters are collected in the test process; and based on the physical field response parameters, macroscopic mechanical property indexes are extracted. According to the method, the mapping relation between the hot pressing sintering process parameters and the macroscopic mechanical property is established, and a basis is provided for optimization of the hot pressing sintering process of the high-entropy alloy powder.

To provide a method enabling creation of a polymer material model having a crosslinked structure close to reality.SOLUTION: A method for creating a polymer material model is provided. The creation method includes: a first step S1 of inputting, to a computer, a pre-crosslinking coarse grained molecular model in which a structural unit of a molecular chain before crosslinking is substituted with coarse grained particles; a second step S2 of analyzing the molecular chain after crosslinking; a third step S3 of inputting, to the computer, a chemical structure of the structural unit after crosslinking on the basis of a result of the analysis; a fourth step S4 of inputting, to the computer, a post-crosslinking structural unit model in which the chemical structure of the structural unit after crosslinking is modeled as an entire atomic model or a united atom model; a fifth step S5 of the computer calculating structural relaxation based on a molecular dynamics method for the pre-crosslinking coarse grained molecular model; and a sixth step S6 of the computer allocating a post-crosslinking structural unit model to the coarse grained particles of the pre-crosslinking coarse grained molecular model after the structural relaxation, to create a post-crosslinking molecular model in which the molecular chain after crosslinking is modeled as the entire atomic model or the united atom model.SELECTED DRAWING: Figure 3

This invention discloses a method for analyzing the wear of micro-milling tools, relating to the fields of wear mechanism research and molecular dynamics analysis in micro-milling. Specifically, it includes: constructing atomic models of the tool and workpiece, incorporating the geometric features of the cutting edge arc; setting up a simulation environment and dividing the workpiece atoms into boundary layers, isothermal layers, and Newtonian layers; assigning potential functions to the atoms and performing energy minimization and relaxation; constructing a dimensionless scale ratio loading condition based on the equivalent uncut thickness and the cutting edge arc radius; extracting the mechanothermal process quantities and quantitatively characterizing the wear state according to the number of detached atoms, the change in effective cutting edge radius, and interface mixing indices. The aim is to achieve atomic-level quantitative characterization of the wear process of micro-milling tools.

This invention provides an atomic-level electron tomography 3D reconstruction method. Its core is to match the simulated projection of an atomic model with experimental images through iterative optimization. The method first constructs a 3D space based on the sample contour and initializes the atomic model parameters. Then, it acquires experimental images from multiple angles and their imaging parameters. In each iteration, a simulated projection image is generated based on the current atomic model and imaging parameters, and the difference between it and the corresponding experimental image is calculated as the loss value. An optimization algorithm dynamically adjusts atomic parameters (position, size, etc.) and imaging parameters (such as focus distance and aberrations) to minimize the total loss. This process includes intelligent simplification steps, which can merge or delete atoms to optimize model complexity. Finally, using the converged high-precision atomic model, 3D reconstructed images with arbitrary viewpoints and depths can be generated. This method can achieve high-precision atomic-level 3D reconstruction without completely relying on 360-degree scan data, helping to reduce data acquisition costs.

The invention provides a material microstructure digital design method based on a non-local model. The material microstructure digital design method can be used for controlling the microstructure and performance of a material. The method comprises the following steps: establishing a fractional order Cahn-Hilliard equation model for describing diffusion and phase separation processes of components in a material; selecting non-local parameters to control the phase distribution characteristics of the material; the fractional order equation is solved through numerical values, and material component space distribution under different non-local parameter values is obtained; constructing a material atom model based on the obtained component distribution; and analyzing the performance of the constructed model in combination with molecular dynamics simulation. By regulating and controlling non-local parameters, materials with different microstructures can be obtained under the same component proportion, accurate control of the microstructures and collaborative optimization of multiple properties such as strength, toughness and conductivity are achieved, and the method is suitable for material design in the fields of electronic packaging, high-performance conductors, aerospace and the like.

The application provides a coarse-grained molecular dynamics simulation and analysis method for researching microstructure properties of electroplating liquid, the coarse-grained model is used instead of the full atomic model, the molecular details which have less influence on system properties are ignored, a few atomic groups are used as basic units of simulation for modeling and simulation, so that the time required for the evolution of the whole system is reduced, and the time scale of the system simulation is prolonged. The interaction between components is described by a classical physical potential. By fitting and adjusting the interaction force parameters between different components, the method of the application can well approximate and model different component electroplating liquid systems, and reproduce experimental phenomena on the microscale.

A method for identifying compounds with threshold activity against a target macromolecule first generates multiple poses for each compound fragment in a plurality of compound fragments against an atomic model of the target macromolecule. This creates a collection of configurations, or a "pose set," for the compound fragments. Each pose is associated with a subset of interaction features drawn from a broader set of such features. Each feature corresponds to a subregion of the target macromolecule's atomic model. Each pose is quantified by application to a physics model. This assigns a score to the interaction features associated with the poses. A binding hypothesis is formed for the macromolecule, using the collection of interaction features and their corresponding scores. From this hypothesis, derived compounds are identified. These derived compounds are tested for their activity against the macromolecule, leading to the identification of those that exhibit the desired threshold activity.

This invention discloses a method for generating typical underwater acoustic functional components based on an open architecture, belonging to the field of underwater acoustic system technology. The core of this method lies in pre-establishing an underwater acoustic functional model library containing standardized interface underwater acoustic functional atomic models, and a component configuration rule library containing configuration templates and constraints. When a component generation request is received, the system automatically selects, instantiates, and logically links multiple atomic models from the model library according to the rule library to form an initial functional component. Then, the component undergoes rigorous static verification and high-fidelity dynamic simulation verification combined with a physical model. After successful verification, the component is compiled and packaged into an executable functional component conforming to the open architecture standard. This invention solves the technical problems existing in traditional underwater acoustic system development, such as tight hardware-software coupling, poor reusability of functional components, long development and verification cycles, and insufficient deployment flexibility, through a standardized, modular, automated, and verification-driven integrated process.

The application discloses a method for simulating crystallinity and mechanical properties of polytetrafluoroethylene based on molecular dynamics, which comprises the following steps: constructing a full-atom model of amorphous polytetrafluoroethylene; performing sintering simulation on the full-atom model of amorphous polytetrafluoroethylene, and constructing a coarse-grained model of polytetrafluoroethylene according to bond order information obtained from the sintering simulation; performing cooling crystallization simulation on the coarse-grained model of polytetrafluoroethylene, and calculating the crystallinity of polytetrafluoroethylene according to particle coordinate information obtained from the cooling crystallization simulation; and performing uniaxial tensile simulation on the coarse-grained model of polytetrafluoroethylene after the cooling crystallization simulation, and evaluating the mechanical properties of polytetrafluoroethylene according to the result of the uniaxial tensile simulation. The method can simulate the crystallinity and mechanical properties of polytetrafluoroethylene with different molecular weights, and the simulation result is consistent with the actual result, so that the test time and cost can be saved, and the method can provide certain guidance for actual production.

The application relates to a long-period stacking-ordered magnesium-rare earth alloy prediction method and application thereof, and belongs to the technical field of metal materials. The prediction method comprises the following steps: liquid structure models of pure Mg, binary Mg-RE alloy, ternary Mg-RE-X (X is other alloy elements) alloy and single atom models of each alloy element are respectively constructed; through molecular dynamics simulation and first principle calculation, the volume, cohesive energy, chemical short program parameters and proportion of each local structure symmetry of the above models are obtained; according to the evolution trend of the volume and cohesive energy of the ternary Mg-RE-X alloy relative to the binary Mg-RE alloy, the consistency of the distribution state of the chemical short program characteristics and the proportion of each local structure symmetry evolved from the binary Mg-RE alloy and pure Mg, effective prediction of whether a long-period stacking-ordered phase can be formed in the Mg-RE-X alloy is realized. Through theoretical calculation, element design basis for development of the long-period stacking-ordered magnesium-rare earth alloy is provided, and the blindness of alloy design is reduced.

The invention provides a hydropower engineering simulation model construction method and device, equipment and a storage medium, and is applied to the field of engineering simulation, and the method comprises the steps: determining a plurality of preset hydropower engineering component types, and determining a hydropower engineering object corresponding to each hydropower engineering component type; for the hydropower engineering object in each hydropower engineering component type, constructing an individual model corresponding to each hydropower engineering object to obtain an individual model set corresponding to the hydropower engineering component type; obtaining description information corresponding to each hydropower engineering component type, and generating a first input set, a first output set, a component feature set and a processing function based on the description information; and on the basis of the individual model set, the first input set, the first output set, the component feature set and the processing function, constructing and storing an atomic model to obtain an atomic model database. According to the method, the flexibility of hydropower engineering simulation modeling can be improved.

The invention discloses a design method for cooperatively screening a high-dynamic-toughness refractory high-entropy alloy based on molecular simulation. The method comprises the following steps: 1, determining candidate elements and component spaces in the refractory high-entropy alloy; 2, constructing an initial atomic model; 3, determining a generalized fault energy curve; 4, calculating heat conductivity; 5, screening components and establishing performance criteria; and 6, experimental verification. According to the method, the dynamic toughness of the refractory high-entropy alloy is predicted and screened by taking two key parameters gamma usf--a dominant deformation mechanism and k--a dominant thermal softening behavior as quantitative indexes of collaborative screening and establishing a collaborative relationship of the key parameters gamma usf--the dominant deformation mechanism and the k-the dominant thermal softening behavior; two most core physical essence influencing the dynamic toughness of the BCC structure refractory high-entropy alloy are grasped, and the spanning from'empirical design 'to'physical mechanism driven design' is realized.

The invention provides a technical support product system contribution rate prediction method based on a BP neural network, and the method comprises the steps: obtaining a support task, serializing the current support task according to the execution process of the support task, and obtaining a support task sequence, the task atomic models required by all the process steps of the task sequence are retrieved from the task atomic model set in combination with the task atomic models required by all the process steps of the task sequence; matching a technical support product with corresponding technical capability information from a technical support product capability model library according to a technical specialty required by execution of the support task; and inputting the guarantee task, the task atomic model corresponding to the guarantee task, the attribute information of the technical support product, the service life information and the environment information into the trained technical support product system contribution rate prediction model, and outputting the contribution rate of the technical support product to the guarantee system. The artificial intelligence algorithm is introduced to predict the system contribution rate of a novel technology guarantee product, meanwhile, product life data is added, and the problem that adaptability is not high in a conventional prediction method is solved.

The present application relates to a method for RNA structure prediction based on multi-scale molecular dynamics simulation, and a model for representing one nucleotide by three particles is constructed, which includes the linkage relationship mode of each particle in the coarse-grained model, the fitting method of the parameters of each particle in the coarse-grained model, and the fitting method of the mapping relationship between the coarse-grained model and the all-atom model. The present application provides a method for RNA structure prediction based on all-atom and coarse-grained iterative simulation. The simulation efficiency of the model is higher than that of the all-atom model, and the simulation accuracy of the all-atom model is ensured to a certain extent.

The invention discloses a wettability-adjustable porous quartz atomic-scale model generation method based on CT scanning data, and relates to the technical field of petroleum engineering and molecular simulation, and the method comprises the following steps: obtaining template coordinates; generating a quartz substrate; carrying out atom screening and hollowing; performing surface atom cleaning and surface hydroxylation treatment on the initial atomic model; on the basis of a wettability field obtained through CT scanning, the functional group type of a hydroxylation area on the pore surface is automatically adjusted, and a target atom model is obtained; and exporting the atomic coordinates of the target atomic model as a standard atomic structure file. According to the method, an atomic-scale model with geometric fidelity, chemical stability and adjustable wettability can be automatically constructed from CT scanning data, so that the physical consistency and prediction precision of molecular simulation on real rock core wetting behaviors are improved, and a high-quality initial structure is provided for molecular dynamics simulation and oil and gas flow research.

The invention provides a method for calculating the influence of rubber compatibility on mechanical properties by using dissipative particle dynamics. The method comprises the following steps: constructing a full-atom model of a to-be-calculated rubber molecular structure unit and performing energy minimization calculation; calculating solubility parameters of each unit structure by using a molecular dynamics method; carrying out coarse graining on the unit structures and calculating conservative repulsive force parameters among different particles; constructing a dissipative particle model of a to-be-calculated rubber system, wherein particles are connected through spring keys; constructing an input file to execute dissipative particle dynamics calculation, and calculating the compatibility among different rubber components; and introducing an entanglement point structure into the dissipative particle model to calculate the mechanical property in the uniaxial tensile mode. The method provides an efficient and reliable molecular simulation method for researching the influence of the rubber compatibility on the mechanical property of a composite system, effectively saves the test cost, and is suitable for solving the technical problem that the mechanical property of a rubber material is poor due to uneven dispersion of rubber.

To provide a crack analysis method, a program, a material design support method and a material design method, which can simply and objectively analyze cracks on a crystal interface.SOLUTION: A crack analysis method for analyzing a crack on a crystal interface of atomic simulation includes: an atomic model preparation step of preparing an atomic model including a crystal interface; a deformation step of deforming the atomic model; a first calculation step of Voronoi-dividing the atomic model before and after the deformation in the deformation step, and calculating a Voronoi volume and a frequency of the Voronoi volume; and an analysis step of analyzing cracks on the crystal interface on the basis of a relation between the Voronoi volume and the frequency of the Voronoi volume.SELECTED DRAWING: Figure 1

The invention relates to the technical field of heat insulation processes, and discloses a molecular dynamics optimization process for regulating and controlling a microporous structure of an aerogel layer, which comprises the following steps of: constructing an initial atomic model of aerogel molecules and generating a corresponding force field parameter file on the basis of a multi-software cooperative workflow; based on the three-dimensional space size of the initial atomic model, a space index data structure is initialized, and the data structure is used for efficiently storing and querying atomic space position information; and performing energy minimization calculation on the initial atomic model to obtain an initial equilibrium system. According to the method, an automatic modeling process is constructed by integrating multiple software, the calculation efficiency is greatly improved by adopting a spatial index and dynamic updating mechanism based on a hash table, super-large-scale system simulation is supported, and meanwhile, an intelligent optimization engine is introduced to automatically optimize simulation parameters; the problems of flow splitting, limited calculation scale and dependence on manual trial and error parameter adjustment in the prior art are effectively solved.

The invention provides a molecular binding path reconstruction method and system based on multi-scale sampling. The molecular binding path reconstruction method based on multi-scale sampling comprises the following steps: performing large-range path search by adopting a coarse graining model of a simplified force field, and identifying a key path section through clustering analysis and an energy evaluation algorithm; converting the key path section into a full-atomic model, and performing high-precision sampling by adopting a precise force field to obtain a track file of atomic-scale interaction time sequence data; and performing multi-scale data integration based on the critical path segment and the trajectory file of the atomic-scale interaction time series data to reconstruct a molecular binding path. According to the method, the precise path information of the atomic level can be obtained while the calculation efficiency is ensured, so that the efficient and precise reconstruction of the atom combination path is realized; the molecular binding path reconstruction method based on multi-scale sampling is particularly suitable for complex molecular system research which needs to consider both large-range conformation search and high-precision sampling.

To provide an atomic model generation method capable of easily generating an atomic model with random grain boundary, a program, a material design support method, and a material design method.SOLUTION: The atomic model generation method generates an atomic model with random grain boundary by atomic simulation. The atomic model generation method includes: an initial atomic model preparation process for preparing two initial atomic models including monocrystal atomic model; a first atomic model generation process for rotating the two initial atomic models and then joining the monocrystal atomic models to thereby generate a first atomic model including a junction interface; a second atomic model generation process for retaining the first atomic model at a retention temperature which is equal to or higher than amorphous-processing temperature of the first atomic model and equal to or lower than a prescribed temperature not exceeding the melting point of the first atomic model, thereby shifting the junction interface to random grain boundary and generating a second atomic model including random grain boundary; and a cooling process for cooling the second atomic model to a cooling temperature which is lower than the amorphous-processing temperature of the first atomic model.SELECTED DRAWING: Figure 2

The present application relates to the technical field of signal processing, and provides a kind of catenary non-stationary vibration signal sparse decomposition method, the method comprises: collecting impact vibration response signal, and carrying out denoising pretreatment;Establish parametric impact response atom model, and construct over-complete parametric atom dictionary;Using matching pursuit algorithm and combining sparse decomposition algorithm, obtain a series of linear combination of sparse atom components;According to the best matching atom set and its corresponding parameter vector obtained in iteration process, the incident wave component set and the reflected wave component set are obtained, and the decoupled incident wave signal and reflected wave signal are reconstructed.The present application realizes the accurate separation and reconstruction of rigid catenary mixed vibration waveform by constructing over-complete parametric atom library containing frequency modulation factor, combined with matching pursuit algorithm.

The invention discloses a three-dimensional Gaussian modeling method for analyzing biomacromolecule components and conformation heterogeneity, and belongs to the technical field of cryoelectron microscope image processing. According to the method, the component and conformation heterogeneity can be directly represented through the change of Gaussian parameters, and an intuitive and explainable structure change description is provided; the consistency and integrity of the local structure along the transformation trajectory are ensured through geometric constraint; modeling of tens of thousands of Gaussian models on a pseudo-atom level is supported; gaussian variation is converted into an atomic conformation landscape through natural support, and a direct bridge is provided between density-based analysis and pseudo-atom level interpretation; the method is realized through an efficient Gaussian projection algorithm and parallel computing, and the processing capacity of a large-scale data set is remarkably improved. According to the method, the component and conformation heterogeneity can be effectively processed at the same time, the continuous conformation change is visually represented, the local structure consistency is kept, the density model and the atomic model are naturally connected, and the defects in the prior art are overcome.

The application relates to a precursor hydrolysis-pyrolysis amorphous model construction molecular dynamics simulation method, which is characterized in that the main products of the precursor hydrolysis and pyrolysis process are water and hydrogen and methane, a Python script program is written, a reasonable criterion is selected, the bonding distance between atoms is judged, the generated product is identified, batch and cyclic molecular dynamics simulation is realized, and an amorphous SiOC atomic model is obtained. The application finally realizes automatic, batch and open source molecular dynamics calculation software LAMMPS, eliminates the influence of human subjective factors, improves the calculation efficiency, saves the time cost of scientific researchers, and avoids the mistakes caused by a large number of operations. In addition, after the hydrolysis and solidification of the sol small molecules, a good network skeleton structure can be formed, the escape speed of the gas product is slowed down, a higher free carbon content amorphous SiOC model can be constructed, and the amorphous-nanocrystalline interface density of the material is improved.

A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process including acquiring a distribution of latent variables output from an encoder in a process of training an autoencoder, generating a path related to deformation based on the distribution of the latent variables, selecting, from a plurality of latent variables generated by inputting a plurality of particle images to the trained encoder, a plurality of neighboring latent variables of which a distance to the path is less than a threshold, selecting a plurality of neighboring particle images corresponding to the plurality of neighboring latent variables among the plurality of particle images, and estimating a plurality of three-dimensional atom models based on the plurality of neighboring particle images.

The application is a method and system for constructing a wide-temperature-range constitutive model of a viscoelastic fluid in a space environment, which relates to the cross-technical field of computational materials science, aerospace tribology and fluid dynamics, and comprises the following steps: performing sinusoidal shearing on a balanced full-atom model at multiple key temperature points, simulating the dynamic response under sinusoidal shearing to obtain original strain time series data and original stress time series data, performing filtering and smoothing processing and feature extraction to obtain waveform features, and calculating shear rate time series data; inputting the filtered and smoothed original strain time series data and the shear rate time series data into a constitutive mathematical model to obtain predicted stress, minimizing the error between the predicted stress and the filtered and smoothed original stress time series data for multi-stage optimization, and obtaining a wide-temperature-range constitutive model of a viscoelastic fluid. The application solves the technical problem that the constitutive model is difficult to obtain rheological parameters of a space lubricating fluid under wide-temperature-range and large-amplitude shearing, resulting in low prediction accuracy.

This invention discloses a method and system for dislocation defect identification based on image recognition and deep learning networks, belonging to the fields of image recognition and machine learning. The method first calibrates the dimensions of a high-resolution electron micrograph, identifies atomic coordinates using a deep learning object detection model, and calculates the local atomic environment vector for each atom. Second, using an atomic model dataset covering various materials, dislocation types, and deformation states, a deep learning network is trained capable of predicting the distance between atoms and dislocations based on the local environment vector. Finally, the image is meshed, and the neighboring atomic information of each mesh node is extracted. Combined with the dislocation distance information output by the trained deep learning network, unsupervised clustering is used to identify the dislocation location. This method achieves automated and high-precision identification of dislocation defects in face-centered cubic materials, while also enabling dynamic characterization of crystal defect deformation behavior.

This invention relates to the fields of materials science and computational chemistry, and particularly to a design method for sludge-based porous ceramic modifiers based on molecular dynamics simulation. The method constructs a multi-scale atomic model of the sludge matrix and candidate modifiers based on sludge component characterization data, establishing a periodic simulation system. It performs molecular dynamics simulations of interface evolution and high-temperature sintering phase transitions, outputting a multi-scale spatiotemporal trajectory dataset. A decomposition-pore-forming phase transition synchronization dynamic descriptor is constructed, and a kinetic synchronization index characterizing the matching degree between gas production rate and glass phase encapsulation ability is calculated. A molecular configuration back-optimization algorithm is run, combined with a graph neural network surrogate model and a multi-objective Bayesian optimization strategy, to output the optimal modifier molecular topology, functional group arrangement, and mass ratio. This invention effectively overcomes the shortcomings of traditional trial-and-error methods, which are characterized by high blindness and long cycles, significantly improving the pore uniformity, mechanical strength, and R&D efficiency of porous ceramics.

This invention provides a molecular simulation method for quickly calculating the equilibrium density of a first molecular model, which is modeled as an all-atom model, and at least one of the terminal distance and radius of inertia. [Solution] The molecular simulation method includes an equilibrium state calculation step which includes a step S11 of inputting a plurality of first molecular models that model the first molecule as an all-atom model, a step S12 of inputting a second molecular model that model the second molecule as an all-atom model, a step S13 of inputting a mixed system model that includes a plurality of first molecular models and second molecular models, and a step S14 of calculating the equilibrium state of the mixed system model, and a density estimation step which calculates at least one of the terminal distance and radius of inertia of the first molecular models in the equilibrium state mixed system model, and estimates the equilibrium density of a system that includes only the first molecular model based on the equilibrium density of the second molecule, the mass of the first molecular model, the mass of the second molecular model, and the volume of the mixed system model in the equilibrium state.