A method for calculating a rare earth oxide molecular simulation force field and a method for evaluating glass component modulus performance

Abstract

The application discloses a calculation method of a rare earth oxide molecular simulation force field and a glass component modulus performance evaluation method, and the method comprises the following steps: collecting parameters of a rare earth oxide crystal state in a crystal database, including a lattice constant, a space group number, a stiffness matrix coefficient and a density; establishing a potential energy relationship model between a rare earth oxide cation and an oxygen atom; fitting parameters of the potential energy relationship model; constructing a rare earth oxide molecular simulation force field; collecting glass component and modulus performance data in a glass performance database; and using the rare earth oxide molecular simulation force field obtained by the calculation method of the rare earth oxide molecular simulation force field, in combination with a molecular dynamics simulation technology, to calculate the modulus performance of the glass component. The fitting method provided by the application can obtain an accurate molecular simulation force field for simulating a rare earth oxide, and is helpful to simulate and calculate the Young's modulus of a fiber glass containing the rare earth oxide.

Description

Technical Field

[0001] This invention belongs to the field of molecular simulation of high modulus glass materials, and in particular, it provides a method for calculating the force field of rare earth oxide molecules and a method for evaluating the modulus performance of glass components. Background Technology

[0002] Glass fiber is a high-performance inorganic non-metallic material with excellent mechanical properties, strong heat resistance, and good corrosion resistance, making it widely used in various industrial fields. For example, high-modulus glass fibers are required for large wind turbine blades, and high-refractive-index glass fibers are needed for optical fiber communication. Therefore, developing higher-performance glass fiber materials is an ongoing pursuit in related fields.

[0003] Currently, the mainstream methods for calculating the modulus of glass fiber materials are machine learning and molecular simulation. For example, patent number ZL2023105209648 discloses a glass fiber composition design method based on a machine learning model and the AGE algorithm. The glass fiber composition designed using this invention can achieve a tensile modulus of over 95 Ga. The paper "Predicting and interpreting oxide glass properties by machine learning using large datasets" uses machine learning algorithms for modeling and prediction, achieving a high level of accuracy in calculating glass modulus properties, with a coefficient of determination exceeding 0.92. The paper "Quantitative structure-property relationship (QSPR) analysis of calcium aluminosilicate glasses based on molecular dynamics simulations" uses a combination of molecular simulation and theoretical analysis to present a method for predicting the modulus of glass materials, with a coefficient of determination reaching 0.98.

[0004] However, to develop larger wind turbine blades, the modulus of glass fibers still needs further improvement. Currently, the mainstream view in the industry is that the glass modulus can be increased by adding rare earth oxides such as Y₂O₃, La₂O₃, and CeO₂. However, there are currently no reports on complete molecular simulation force fields for these rare earth oxides. For example, the paper "A New Self-Consistent Empirical Interatomic Potential Model for Oxides, Silicates, and Silica-Based Glasses" provides molecular simulation force fields for various oxides, but not for rare earth oxides. This makes simulation calculations for rare earth oxide-containing glasses difficult to conduct, hindering the composition design of high-modulus glass fibers. To address this, a method for calculating the molecular simulation force field of rare earth oxides needs to be developed. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the prior art by providing a method for calculating the simulated force field of rare earth oxide molecules, and by combining molecular dynamics simulation technology to accurately calculate the modulus properties of multi-component glasses containing rare earth oxides.

[0006] The technical solution for achieving the objective of this invention is as follows: On one hand, a method for calculating the simulated force field of rare earth oxide molecules is provided, the method comprising:

[0007] Step 1: Collect parameters of rare earth oxide crystals in the crystal database, including lattice constant, space group number, stiffness matrix coefficients, and density.

[0008] Step 2: Establish a potential energy relationship model between rare earth oxide cations and oxygen atoms;

[0009] Step 3: Fit the parameters of the potential energy relationship model;

[0010] Step 4: Construct a simulated force field for rare earth oxide molecules.

[0011] Furthermore, the potential energy relationship model between rare earth oxide cations and oxygen atoms in step 2 is as follows:

[0012] ;

[0013] In the formula, This represents the potential energy relationship between oxide cations and oxygen atoms. Indicates the distance between atoms. The potential well depth represents the potential energy. This represents the potential well width parameter. This represents the distance between cations and oxygen atoms when they are in equilibrium. As the preconditioner for the exclusion term, C is the feature length parameter for the exclusion term. ij K represents the coefficient of the dispersion attraction term; ij To correct the parameters.

[0014] Furthermore, the parameters for fitting the potential energy relationship model in step 3 are specifically obtained by sequentially performing optimization fitting, structural relaxation optimization fitting, and free energy fitting on the initial structure of the oxide crystal.

[0015] Furthermore, step 3 specifically includes:

[0016] Step 3-1: Based on the crystal database data in Step 1, set the initial parameters of the simulated force field, i.e., the initial parameters of the potential energy relationship model.

[0017] Step 3-2: Based on the initial parameters, calculate the stress of the experimental lattice using first-principles calculations. , internal stress and elasticity coefficient Q mq ;in, lattice stress , , Let represent the normal stresses in the x, y, and z directions, respectively. , , These represent the shear stresses in the xy, xz, and yz planes, respectively. , representing the spatial coordinate components x, y, z, Q mq This represents the magnitude of the stress response in the m direction when subjected to force or deformation in the q direction, which is the element in the m-th row and q-th column of the material stiffness matrix;

[0018] Step 3-3: Set the objective function for initial structure optimization :

[0019]

[0020] In the formula, , and As a weighting factor; and These are the crystal elastic parameters obtained from first-principles calculations and the elastic parameters from experimental crystal data, respectively; where, ;

[0021] Steps 3-4 involve optimizing the parameters using a quasi-Newtonian variable metric method to achieve the desired objective function. To minimize this, we obtain a set of parameters T1 for the potential energy relationship model;

[0022] Steps 3-5 involve structural relaxation optimization fitting, specifically including:

[0023] (1) Using parameter T1 as the initial parameter of the simulated force field, calculate the lattice parameters. Atomic coordinates and elastic parameter Q mn ;

[0024] (2) Set the objective function for structural relaxation optimization fitting. :

[0025]

[0026] In the formula, , and As a weighting factor, The lattice constant of the oxide crystal is obtained through first-principles calculations. The lattice constant of the oxide crystal in the experimental lattice data. These are the fractional coordinates of the atomic positions within the unit cell, obtained through first-principles calculations. Here, represents the fractional coordinates of the atomic positions within the unit cell in the experimental lattice data; where, ;

[0027] (3) Optimize the parameters using the quasi-Newton variable metric method so that the scaling function Minimize, and obtain a set of potential energy relationship model parameters T2;

[0028] Steps 3-6 involve fitting the free energy, specifically including:

[0029] (1) Using parameter T2 as the initial parameter of the simulated force field, calculate the Gibbs free energy G and chemical potential μ of the crystal, and introduce temperature and pressure conditions;

[0030] (2) Set the objective function for fitting the free energy :

[0031]

[0032] In the formula, and As a weighting factor, The Gibbs free energy component is obtained through first-principles calculations. The Gibbs free energy component obtained in the experiment. The chemical potential obtained through first-principles calculations, The chemical potential obtained in the experiment; where, and ;

[0033] (3) Optimize the parameters using the quasi-Newton variable metric method to make the objective function Minimize, and obtain a set of potential energy relationship model parameters T3;

[0034] Steps 3-7: Calculate the lattice constant α and elastic modulus Q of the rare earth oxide based on parameter T3. 11 Q 44 and Q 12 ;

[0035] Step 3-8: Set different initial parameters in the same way as in Step 3-1, and repeat Step 3-2 to Step 3-7 to obtain multiple sets of potential energy relationship model parameters T3;

[0036] Steps 3-9: Use a genetic algorithm to globally optimize and screen multiple sets of potential energy relationship model parameters T3, and select the set of optimal potential energy relationship model parameters T3 that simultaneously satisfies structural accuracy and mechanical performance reliability, as the final potential energy relationship model parameters T4.

[0037] Furthermore, the rules for setting the initial parameters of the simulated force field in step 3-1 are as follows:

[0038] .

[0039] Furthermore, in steps 3-9, a genetic algorithm is used to globally optimize and screen the parameters T3 of multiple potential energy relationship models, and a fitness function is set. for:

[0040]

[0041] Set constraints:

[0042]

[0043] In the formula, These are the lattice constant a and the elastic coefficient Q, respectively. 11 Q 44 and Q 12 The relative error.

[0044] Furthermore, step 4, which involves constructing a simulated force field for rare earth oxide molecules, specifically involves using the final potential energy relationship model parameter T4 obtained in step 3 as the simulated force field for oxide molecules.

[0045] On the other hand, a method for evaluating the modulus performance of glass components is provided, the method comprising the following steps:

[0046] Collect glass composition and modulus data from the glass performance database;

[0047] The modulus properties of the glass component are calculated using the simulated force field of rare earth oxide molecules obtained by the calculation method of the simulated force field of rare earth oxide molecules, combined with molecular dynamics simulation technology.

[0048] On the other hand, a computational system for simulating the force field of rare earth oxide molecules is provided, the system comprising:

[0049] The first module is used to collect parameters of rare earth oxide crystal states from the crystal database, including lattice constant, space group number, stiffness matrix coefficients, and density.

[0050] The second module is used to establish a potential energy relationship model between rare earth oxide cations and oxygen atoms.

[0051] The third module is used to fit the parameters of the potential energy relationship model;

[0052] The fourth module is used to construct simulated force fields for rare earth oxide molecules.

[0053] On the other hand, a glass composition modulus performance evaluation system is provided, the system comprising:

[0054] The fifth module is used to collect glass composition and modulus performance data from the glass performance database;

[0055] The sixth module is used to calculate the modulus properties of the glass composition by using the simulated force field of rare earth oxide molecules obtained by the calculation method of the simulated force field of rare earth oxide molecules, combined with molecular dynamics simulation technology.

[0056] Compared with the prior art, the significant advantages of this invention are:

[0057] (1) This invention innovatively provides a method for calculating the simulated force field of rare earth oxide molecules, which can obtain the simulated force field parameters of rare earth oxide molecules. This method comprehensively considers the covalent bonds, chemical bonds, and the balance of attractive and repulsive forces between atoms in rare earth oxide molecules, and also considers the effects of extreme conditions such as high temperature and high pressure. The relevant parameters can not only accurately describe the motion of oxide molecules at room temperature, but also restore the motion trajectory of oxide molecules during the high-temperature melting process of glass raw materials, thereby ensuring the accuracy of the simulation performance.

[0058] (2) The present invention adopts a progressive fitting method, which gradually considers the energy, structure and thermodynamic effects of rare earth oxide crystals, so that the simulated force field parameters of rare earth oxide molecules can be obtained efficiently and more accurately and reliably under actual temperature and pressure conditions.

[0059] (3) This invention combines genetic algorithm and molecular simulation force field fitting technology to establish a multi-objective intelligent optimization and screening method for molecular simulation force field parameters of rare earth oxides, which can select the most accurate molecular simulation force field parameters based on different initial values.

[0060] (4) The rare earth oxide molecular simulation force field combined with molecular dynamics simulation method provided by the present invention can accurately calculate the Young's modulus of rare earth oxide fiber glass.

[0061] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating the calculation method for simulating the force field of rare earth oxide molecules according to the present invention.

[0063] Figure 2 This is a schematic diagram of the molecular model of Example 1.

[0064] Figure 3 This is a schematic diagram of the molecular model for Example 2.

[0065] Figure 4 This is a schematic diagram of the molecular model for Example 3.

[0066] Figure 5 This is a schematic diagram of the molecular model for Example 4.

[0067] Figure 6 This is a schematic diagram of the molecular model for Example 5. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0069] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0070] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0071] In one embodiment, combined Figure 1 A method for calculating the simulated force field of rare earth oxide molecules is provided, the method comprising:

[0072] Step 1: Collect parameters of rare earth oxide crystals in the crystal database, including lattice constant, space group number, stiffness matrix coefficients, and density.

[0073] Step 2: Establish a potential energy relationship model between rare earth oxide cations and oxygen atoms;

[0074] Step 3: Fit the parameters of the potential energy relationship model;

[0075] Step 4: Construct a simulated force field for rare earth oxide molecules.

[0076] Furthermore, in one embodiment, the potential energy relationship model between the rare earth oxide cation and oxygen atom in step 2 is as follows:

[0077]

[0078] The first term on the right-hand side of the formula describes the covalent and chemical bond interactions in oxides. The second and third terms on the right-hand side describe short-range repulsion and dispersive interactions. The fourth term on the right-hand side is a high-temperature and high-pressure correction term, used to describe interactions under extreme conditions.

[0079] In the formula, This represents the potential energy relationship between oxide cations and oxygen atoms. Indicates the distance between atoms. The potential well depth represents the potential energy. This represents the potential well width parameter. This represents the distance between cations and oxygen atoms when they are in equilibrium, at which point the attractive and repulsive forces between the atoms are completely balanced. As the preconditioner for the exclusion term, C is the feature length parameter for the exclusion term. ij K represents the coefficient of the dispersion attraction term; ij To correct the parameters.

[0080] Furthermore, in one embodiment, the parameters of fitting the potential energy relationship model in step 3 are specifically obtained by sequentially performing optimization fitting on the initial structure of the oxide crystal, structural relaxation optimization fitting, and free energy fitting.

[0081] Step 3 specifically includes:

[0082] Step 3-1: Based on the crystal database data in Step 1, set the initial parameters of the simulated force field, i.e., the initial parameters of the potential energy relationship model.

[0083] Step 3-2: Based on the initial parameters, calculate the stress of the experimental lattice using first-principles calculations. , internal stress and elasticity coefficient Q mq ;in, lattice stress , , Let represent the normal stresses in the x, y, and z directions, respectively. , , These represent the shear stresses in the xy, xz, and yz planes, respectively. , representing the spatial coordinate components x, y, z, Q mq This represents the magnitude of the stress response in the m direction when subjected to force or deformation in the q direction, which is the element in the m-th row and q-th column of the material stiffness matrix;

[0084] Step 3-3: Set the objective function for initial structure optimization :

[0085]

[0086] In the formula, , and As a weighting factor; and These are the crystal elastic parameters obtained from first-principles calculations and the elastic parameters from experimental crystal data, respectively; where, ;

[0087] Preferably, here, =1000, = 10000, = 0.01.

[0088] Steps 3-4 involve optimizing the parameters using a quasi-Newtonian variable metric method to achieve the desired objective function. To minimize this, we obtain a set of parameters T1 for the potential energy relationship model;

[0089] Steps 3-5 involve structural relaxation optimization fitting, specifically including:

[0090] (1) Using parameter T1 as the initial parameter of the simulated force field, calculate the lattice parameters. Atomic coordinates and elastic parameter Q mn ;

[0091] (2) Set the objective function for structural relaxation optimization fitting. :

[0092]

[0093] In the formula, , and As a weighting factor, The lattice constant of the oxide crystal is obtained through first-principles calculations. The lattice constant of the oxide crystal in the experimental lattice data. These are the fractional coordinates of the atomic positions within the unit cell, obtained through first-principles calculations. Here, represents the fractional coordinates of the atomic positions within the unit cell in the experimental lattice data; where, ;

[0094] Preferably, here, =100, = 1000, = 0.01;

[0095] (3) Optimize the parameters using the quasi-Newton variable metric method so that the scaling function Minimize, and obtain a set of potential energy relationship model parameters T2;

[0096] Steps 3-6 involve fitting the free energy, specifically including:

[0097] (1) Using parameter T2 as the initial parameter for simulating the force field, calculate the Gibbs free energy G and chemical potential μ of the crystal, and introduce temperature and pressure conditions; preferably, the pressure is 10. −4 GPa, temperature 300K;

[0098] (2) Set the objective function for fitting the free energy :

[0099]

[0100] In the formula, and As a weighting factor, The Gibbs free energy component is obtained through first-principles calculations. The Gibbs free energy component obtained in the experiment. The chemical potential obtained through first-principles calculations, The chemical potential obtained in the experiment; where, and ;

[0101] Preferably, here, =1, = 10;

[0102] (4) Optimize the parameters using the quasi-Newton variable metric method to make the objective function Minimize, and obtain a set of potential energy relationship model parameters T3;

[0103] Steps 3-7: Calculate the lattice constant α and elastic modulus Q of the rare earth oxide based on parameter T3. 11 Q 44 and Q 12 ;

[0104] Step 3-8: Set different initial parameters in the same way as in Step 3-1, and repeat Step 3-2 to Step 3-7 to obtain multiple sets of potential energy relationship model parameters T3;

[0105] Steps 3-9: Use a genetic algorithm to globally optimize and screen multiple sets of potential energy relationship model parameters T3, and select the set of optimal potential energy relationship model parameters T3 that simultaneously satisfies structural accuracy and mechanical performance reliability, as the final potential energy relationship model parameters T4.

[0106] Preferably, in some embodiments, the initial parameters of the simulated force field in step 3-1 are set according to the following rules:

[0107] .

[0108] Furthermore, in steps 3-9, a genetic algorithm is used to globally optimize and screen the parameters T3 of multiple potential energy relationship models, and a fitness function is set. for:

[0109]

[0110] Set constraints:

[0111]

[0112] In the formula, These are the lattice constant a and the elastic coefficient Q, respectively. 11 Q 44 and Q 12 The relative error.

[0113] Preferably, the population size is set to 50, the number of generations is 100, the crossover probability is 0.8, and the mutation probability is 0.05 through iterative optimization using a genetic algorithm.

[0114] Furthermore, in one embodiment, the construction of the rare earth oxide molecular simulated force field in step 4 specifically involves using the final potential energy relationship model parameter T4 obtained in step 3 as the oxide molecular simulated force field.

[0115] In one embodiment, a method for evaluating the modulus performance of glass components is provided, the method comprising the following steps:

[0116] Collect glass composition and modulus data from the glass performance database;

[0117] The modulus properties of the glass component are calculated using the simulated force field of rare earth oxide molecules obtained by the calculation method of the simulated force field of rare earth oxide molecules, combined with molecular dynamics simulation technology.

[0118] In one embodiment, a computational system for simulating the force field of rare earth oxide molecules is provided, the system comprising:

[0119] The first module is used to collect parameters of rare earth oxide crystal states from the crystal database, including lattice constant, space group number, stiffness matrix coefficients, and density.

[0120] The second module is used to establish a potential energy relationship model between rare earth oxide cations and oxygen atoms.

[0121] The third module is used to fit the parameters of the potential energy relationship model;

[0122] The fourth module is used to construct simulated force fields for rare earth oxide molecules.

[0123] Specific limitations regarding the computational system for simulating the force field of rare earth oxide molecules can be found in the limitations of the computational method for simulating the force field of rare earth oxide molecules described above, and will not be repeated here. Each module in the aforementioned computational system for simulating the force field of rare earth oxide molecules can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of the processor, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0124] In one embodiment, a glass component modulus performance evaluation system is provided, the system comprising:

[0125] The fifth module is used to collect glass composition and modulus performance data from the glass performance database;

[0126] The sixth module is used to calculate the modulus properties of the glass composition by using the simulated force field of rare earth oxide molecules obtained by the calculation method of the simulated force field of rare earth oxide molecules, combined with molecular dynamics simulation technology.

[0127] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for calculating the simulated force field of rare earth oxide molecules and also implements a method for evaluating the modulus performance of glass components.

[0128] For specific limitations on each step, please refer to the limitations on the calculation method of the simulated force field of rare earth oxide molecules and the evaluation method of glass component modulus performance mentioned above, which will not be repeated here.

[0129] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon. When executed by a processor, the computer program implements a method for calculating the simulated force field of rare earth oxide molecules and also implements a method for evaluating the modulus performance of glass components.

[0130] For specific limitations on each step, please refer to the limitations on the calculation method of the simulated force field of rare earth oxide molecules and the evaluation method of glass component modulus performance mentioned above, which will not be repeated here.

[0131] As a specific example, the invention will be further verified and illustrated in one embodiment.

[0132] In this embodiment, the crystal structure parameters of rare earth oxides Y2O3, La2O3, and CeO2 were collected using the ICSD and Materials Project databases and divided into two modules: first-principles calculation of modulus and experimental testing.

[0133] The potential energy relationship model parameters of different rare earth oxides obtained by the method of this invention are shown in Table 1:

[0134] Table 1. Parameters of the potential energy relationship model for different rare earth oxides

[0135]

[0136] The calculated and experimental values ​​of the lattice constants and elastic moduli of different rare earth oxide crystals are shown in Table 2 below:

[0137] Table 2 Calculated and experimental values ​​of lattice constants and elastic moduli of different rare earth oxides

[0138]

[0139] As can be seen from Table 2, the calculated lattice constants of the three different rare earth oxides are very close to the experimental data, all less than 1%, indicating that the potential energy relationship parameters obtained by fitting using the method of this invention are highly accurate in simulating the crystal structure of rare earth oxides.

[0140] The calculated and experimental values ​​of the elastic coefficients of the three rare earth oxides are all less than 6%, indicating that the relevant potential energy relationship model parameters can accurately characterize the mechanical properties of rare earth oxide crystals and help simulate the mechanical properties of fiber glass materials containing rare earth oxides.

[0141] Based on the glass composition data, the molecular force field of rare earth oxide molecules obtained above was used to simulate the molecular model of rare earth oxide fiber glass, such as... Figures 2-6 As shown in the figure. The Young's modulus of the glass molecular model was calculated, and the results are shown in Table 3 below.

[0142] Table 3. Composition, calculated Young's modulus, and experimental Young's modulus of rare earth oxide fiber glass materials.

[0143]

[0144] As shown in Table 3, the molecular simulated force field of rare earth oxides provided by this invention can accurately simulate and calculate the Young's modulus of fiber glass containing rare earth oxides. The embodiments of this invention include combinations of various rare earth oxides, including single rare earth oxides and two rare earth oxides. In Example 1, the difference between the calculated and experimental Young's modulus is the smallest, only -1.69%. In the other embodiments, the absolute value of the error between the calculated and experimental Young's modulus is also less than 5%. The relevant results indicate that the fitting method provided by this invention can accurately simulate the molecular simulated force field of rare earth oxides and is helpful in simulating and calculating the Young's modulus of fiber glass containing rare earth oxides.

[0145] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention without departing from its spirit and scope should be included within the protection scope of the present invention.

Claims

1. A method for calculating a rare earth oxide molecular analog force field, comprising: The method includes: Step 1: Collect parameters of rare earth oxide crystals in the crystal database, including lattice constant, space group number, stiffness matrix coefficients, and density. Step 2: Establish a potential energy relationship model between rare earth oxide cations and oxygen atoms; Step 3, fitting the parameters of the potential energy relationship model; specifically including: Step 3-1: Based on the crystal database data in Step 1, set the initial parameters of the simulated force field, i.e., the initial parameters of the potential energy relationship model. Step 3-2: Based on the initial parameters, calculate the stress of the experimental lattice using first-principles calculations. , internal stress and elasticity coefficient Q mq ;in, lattice stress , , Let represent the normal stresses in the x, y, and z directions, respectively. , , These represent the shear stresses in the xy, xz, and yz planes, respectively. , representing the spatial coordinate components x, y, z, Q mq This represents the magnitude of the stress response in the m direction when subjected to force or deformation in the q direction, which is the element in the m-th row and q-th column of the material stiffness matrix; Step 3-3: Set the objective function for initial structure optimization : In the formula, , and As a weighting factor; and These are the crystal elastic parameters obtained from first-principles calculations and the elastic parameters from experimental crystal data, respectively; where, ; Steps 3-4 involve optimizing the parameters using a quasi-Newtonian variable metric method to achieve the desired objective function. To minimize this, we obtain a set of parameters T1 for the potential energy relationship model; Steps 3-5 involve structural relaxation optimization fitting, specifically including: (1) Using parameter T1 as the initial parameter of the simulated force field, calculate the lattice parameters. Atomic coordinates and elastic parameter Q mn ; (2) Set the objective function for structural relaxation optimization fitting. : In the formula, , and As a weighting factor, The lattice constant of the oxide crystal is obtained through first-principles calculations. The lattice constant of the oxide crystal in the experimental lattice data. These are the fractional coordinates of the atomic positions within the unit cell, obtained through first-principles calculations. Here, represents the fractional coordinates of the atomic positions within the unit cell in the experimental lattice data; where, ; (3) Optimize the parameters using the quasi-Newton variable metric method so that the scaling function Minimize, and obtain a set of potential energy relationship model parameters T2; Steps 3-6 involve fitting the free energy, specifically including: (1) Using parameter T2 as the initial parameter of the simulated force field, calculate the Gibbs free energy G and chemical potential μ of the crystal, and introduce temperature and pressure conditions; (2) Set the objective function for fitting the free energy : In the formula, and As a weighting factor, The Gibbs free energy component is obtained through first-principles calculations. The Gibbs free energy component obtained in the experiment. The chemical potential obtained through first-principles calculations, The chemical potential obtained in the experiment; where, and ; (3) Optimize the parameters using the quasi-Newton variable metric method to make the objective function Minimize, and obtain a set of potential energy relationship model parameters T3; Step 3-7, calculate the lattice constant a of the rare earth oxide and the elastic coefficient Q according to the parameter T3 11 , Q 44 , and Q 12 ; Step 3-8: Set different initial parameters in the same way as in Step 3-1, and repeat Step 3-2 to Step 3-7 to obtain multiple sets of potential energy relationship model parameters T3; Steps 3-9: Use a genetic algorithm to globally optimize and screen multiple sets of potential energy relationship model parameters T3, and select the set of optimal potential energy relationship model parameters T3 that simultaneously satisfies structural accuracy and mechanical performance reliability, as the final potential energy relationship model parameters T4; Step 4: Construct a simulated force field for rare earth oxide molecules.

2. The calculation method for the simulated force field of rare earth oxide molecules according to claim 1, characterized in that, The potential energy relationship model between rare earth oxide cations and oxygen atoms in step 2 is as follows: In the formula, This represents the potential energy relationship between oxide cations and oxygen atoms. Indicates the distance between atoms. The potential well depth represents the potential energy. This represents the potential well width parameter. This represents the distance between cations and oxygen atoms when they are in equilibrium. As the preconditioner for the exclusion term, C is the feature length parameter for the exclusion term. ij K represents the coefficient of the dispersion attraction term; ij To correct the parameters.

3. The calculation method for the simulated force field of rare earth oxide molecules according to claim 1, characterized in that, Step 3, fitting the parameters of the potential energy relationship model, specifically involves: sequentially performing optimization fitting on the initial structure of the oxide crystal, structural relaxation optimization fitting, and free energy fitting to obtain the parameters of the potential energy relationship model.

4. The calculation method for the simulated force field of rare earth oxide molecules according to claim 1, characterized in that, The rules for setting the initial parameters of the simulated force field in step 3-1 are as follows: 。 5. The method for calculating the simulated force field of rare earth oxide molecules according to claim 1, characterized in that, In steps 3-9, a genetic algorithm is used to globally optimize and filter the parameters T3 of multiple potential energy relationship models, and a fitness function is set. for: Set constraints: In the formula, These are the lattice constant a and the elastic coefficient Q, respectively. 11 Q 44 and Q 12 The relative error.

6. The method for calculating the simulated force field of rare earth oxide molecules according to claim 1, characterized in that, Step 4 involves constructing a simulated force field for rare earth oxide molecules, specifically by using the final potential energy relationship model parameter T4 obtained in step 3 as the simulated force field for oxide molecules.

7. A method for evaluating the glass component modulus performance based on the calculation method according to any one of claims 1 to 6, characterized in that, The method includes the following steps: Collect glass composition and modulus data from the glass performance database; The modulus properties of the glass component are calculated using the simulated force field of rare earth oxide molecules obtained by the calculation method of the simulated force field of rare earth oxide molecules, combined with molecular dynamics simulation technology.

8. A calculation system for simulating the force field of rare earth oxide molecules based on the calculation method described in any one of claims 1 to 6, characterized in that, The system includes: The first module is used to collect parameters of rare earth oxide crystal states from the crystal database, including lattice constant, space group number, stiffness matrix coefficients, and density. The second module is used to establish a potential energy relationship model between rare earth oxide cations and oxygen atoms. The third module is used to fit the parameters of the potential energy relationship model; The fourth module is used to construct simulated force fields for rare earth oxide molecules.

9. A glass component modulus performance evaluation system based on the evaluation method of claim 7, characterized in that, The system includes: The fifth module is used to collect glass composition and modulus performance data from the glass performance database; The sixth module is used to calculate the modulus properties of the glass composition by using the simulated force field of rare earth oxide molecules obtained by the calculation method of the simulated force field of rare earth oxide molecules, combined with molecular dynamics simulation technology.

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