Glass elastic modulus prediction method and system based on molecular dynamics simulation

Abstract

The invention provides a glass elasticity modulus prediction method and system based on molecular dynamics simulation, and belongs to the technical field of glass performance prediction.The glass elasticity modulus prediction method comprises the steps that glass materials of different systems are obtained and tested, and an elasticity modulus test database is constructed; constructing an atomic model containing different system glass material component atoms, and optimizing the model; performing molecular dynamics simulation calculation on the optimized atomic model to obtain structure information of glass materials of different systems; enabling the elasticity modulus test databases of the glass materials of different systems to correspond to the structural information, and constructing a component-structure-performance data set; the elastic modulus is used as a dependent variable, the structural information is used as an independent variable, a multiple regression model is constructed, a quantitative structure-activity relationship for predicting the modulus of the glass elastic model is established, and the elastic modulus of a glass material in actual experimental production is predicted through simulation of molecular dynamics on the structural information. According to the invention, accurate simulation of the glass system structure and accurate prediction of the elastic modulus are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of glass property prediction, and in particular relates to a glass elastic modulus prediction method and system based on molecular dynamics simulation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Glass applications are becoming increasingly widespread, significantly enriching its variety and properties. In the glass design process, glass composition and structure are among the most important factors determining its performance. Therefore, studying the relationship between glass composition, structure, and performance has become extremely important. However, in traditional glass composition design experiments, due to long experimental cycles and complex experimental procedures, accurate and rapid quantitative analysis of the glass composition, structure, and performance is difficult. For example, in traditional glass composition design experiments, the selected glass components must first be batched. After batching, the preparation process includes high-temperature melting and annealing. After preparation, the samples must be cut and ground. The samples are then subjected to various performance and structural tests and analyses. Finally, adjustments are made based on the test and analysis results until the final accurate result is obtained, thereby achieving the ideal formulation. Therefore, predicting the elastic modulus of a large number of different glass system components requires large-scale elastic modulus testing, which increases time and costs and hinders guidance for both experimental and actual production.

[0004] At the same time, traditional experimental methods for designing glass components cannot achieve a quantitative structure-activity relationship between components, structure and performance, resulting in low simulation accuracy of different glass system structures and inability to more accurately predict the elastic modulus of different glass systems. Summary of the Invention

[0005] To overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for predicting the elastic modulus of glass based on molecular dynamics simulation. By adopting molecular dynamics simulation, high-throughput simulation of structure and performance is performed, and a quantitative structure-activity relationship between component-structure-performance is established based on molecular dynamics simulation. Combined with the calculation of the short- and medium-range structure of glass by molecular dynamics simulation, accurate simulation of the glass system structure and precise calculation of the elastic modulus are achieved. A regression model is established, and a quantitative structure-activity relationship between performance and structure is constructed. The method and system can accurately predict the measured modulus of glass materials in different systems, providing guidance for the actual performance of glass.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: A first aspect of the present invention provides a method for predicting the elastic modulus of glass based on molecular dynamics simulation.

[0007] A method for predicting glass elastic modulus based on molecular dynamics simulation, comprising: Obtain elastic modulus test data of glass materials of different systems and build an elastic modulus test database; Constructing an atomic model containing atoms of glass material components in different systems and optimizing the model to obtain an optimized atomic model; Perform molecular dynamics simulation calculations on the optimized atomic model to obtain structural information of glass materials in different systems; The elastic modulus test database of different glass materials is matched with the structural information to construct a component-structure-performance data set; Based on the constructed component-structure-property dataset, a multivariate regression model was constructed with elastic modulus as the dependent variable and structural information as the independent variable to establish a quantitative structure-activity relationship for predicting the modulus of the glass elastic model; A glass material to be predicted is obtained, and the elastic modulus of the glass material to be predicted is predicted based on a quantitative structure-activity relationship of the modulus of the predicted glass elastic model.

[0008] A second aspect of the present invention provides a glass elastic modulus prediction system based on molecular dynamics simulation.

[0009] A glass elastic modulus prediction system based on molecular dynamics simulation, comprising: The database and model building module is configured to: obtain elastic modulus test data of glass materials of different systems and build an elastic modulus test database; Constructing an atomic model containing atoms of glass material components in different systems and optimizing the model to obtain an optimized atomic model; Perform molecular dynamics simulation calculations on the optimized atomic model to obtain structural information of glass materials in different systems; The quantitative structure-activity relationship building module is configured to: correspond the elastic modulus test database of different glass materials with structural information to construct a component-structure-property data set; Based on the constructed component-structure-property dataset, a multivariate regression model was constructed with elastic modulus as the dependent variable and structural information as the independent variable to establish a quantitative structure-activity relationship for predicting the modulus of the glass elastic model; The prediction module is configured to: obtain a glass material to be predicted, and predict the elastic modulus of the glass material to be predicted based on a quantitative structure-activity relationship of the modulus of the predicted glass elastic model.

[0010] The third aspect of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a method as described in the second aspect of the present invention are implemented.

[0011] A fourth aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a method as described in the second aspect of the present invention.

[0012] A fifth aspect of the present invention provides a computer program product comprising instructions, which, when run on a computer, enables the computer program to be executed by a processor to implement the steps of a method as described in the second aspect of the present invention.

[0013] One or more of the above technical solutions have the following beneficial effects: The present invention combines the test of elastic modulus with the calculation of the short-range structure of glass through molecular dynamics simulation to obtain the motion trajectory of each atom in the glass system, thereby establishing a clear component-structure-performance quantitative structure-activity relationship. The high throughput and efficiency of molecular dynamics simulation calculations are utilized to achieve accurate simulation of different glass system structures and precise prediction of elastic modulus.

[0014] The present invention constructs a component-structure-performance dataset to obtain descriptors of the corresponding structure factors of the components. Then, through molecular dynamics simulation of the corresponding structural information, the elastic modulus of different glass materials in actual experimental production can be quickly and accurately predicted, avoiding large-scale elastic modulus testing, saving time and cost, and providing guidance for experiments and actual production.

[0015] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0017] Figure 1 This is a flow chart of a method for predicting glass elastic modulus based on molecular dynamics simulation according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0018] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0019] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0020] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0021] Example 1 This embodiment discloses a method for predicting the elastic modulus of glass based on molecular dynamics simulation. Through molecular dynamics simulation, the motion trajectory of each atom in the glass system can be obtained, thereby obtaining the relationship between the components and the structure. The elastic modulus test is combined with the calculation of the short-range structure of the glass by molecular dynamics simulation to establish a structure-property relationship. This can achieve accurate simulation of the glass system structure and precise prediction of the elastic modulus. Specifically, it includes: Collect and compile composition information of glass systems with different formulations, obtain elastic modulus test data of glass materials with different systems, and build an elastic modulus test database; An atomic model of glass systems with different formulations was established using MS software, and the model was optimized using lammps software to obtain the optimized atomic model. The optimized atomic model was then integrated using Python software to obtain the structural information of glass systems with different formulations. The elastic modulus test database of glass systems with different formulations is matched with structural information to construct a component-structure-property data set; Based on the constructed component-structure-property dataset, a multivariate regression model was constructed with elastic modulus as the dependent variable and structural information as the independent variable to determine the quantitative structure-activity relationship between the elastic modulus and structural information of glass. The structure factor parameters were calculated through the quantitative structure-activity relationship to obtain the descriptor of the structure factor. Obtain glass materials from actual experiments or production, and obtain the corresponding structural information based on the quantitative structure-property relationship between the elastic modulus and structural information of the glass, using descriptors of the glass material structure factor. Finally, use the structural information obtained from molecular dynamics simulations to predict the elastic modulus of the glass material in actual experiments or production.

[0022] In order to explain this embodiment more clearly, Figure 1 As shown in Figure 2, the process of predicting the glass elastic modulus based on molecular dynamics simulation can be described as follows: Step 1: Conduct experiments to test the elastic modulus of glass materials of different systems, collect statistics and composition information of glass systems with different formulas, obtain elastic modulus test data of glass materials of different systems, and build an elastic modulus test database.

[0023] In this embodiment, the glass materials with different compositions obtained include but are not limited to CaO-MgO-Al2O3-SiO2, MgO-Al2O3-SiO2, Na2O-CaO-SiO2, B2O3-Al2O3-SiO2 and other glass systems. The formula composition of the above different glass systems is determined by the component design through a large amount of literature research. The elastic modulus is tested using a modulus tester to record the transverse wave velocity and longitudinal wave velocity when the sound wave passes through the glass sample. The elastic modulus (E) of the glass is calculated by the following formula, where v is the Poisson's ratio, V T is the shear wave velocity, V L is the longitudinal wave velocity, and ρ is the sample density:

[0024]

[0025] The target components of different glass materials were obtained. The target components here are the formula components screened in the glass system for testing and molecular dynamics simulation. By comparing experimental data with simulation results under the same components, the effectiveness of this patent in predicting the elastic modulus was verified. Table 1 includes the component parameters and process parameters required for the simulation calculations.

[0026] Table 1 Composition and calculation parameter information of different glass materials used in this example

[0027] Step 2: Use MS software to establish an atomic model containing glass systems with different formulas, and optimize the model using lammps software to obtain an optimized atomic model.

[0028] In this embodiment, atomic models containing atoms from different glass material components with varying randomness were constructed as models for molecular dynamics simulations. Specifically, the size of the atomic model was preferably set to include, but not limited to, at least 3,000 atoms from the glass components, i.e., the number of atoms was at least 3,000. The modeling process was implemented using MS software. First, single-atom models of the elements contained in the glass components were established. Next, the number of atoms corresponding to each element in the glass system was calculated. The different types and numbers of atoms were then mixed using MS software.

[0029] In this example, the model was optimized by optimizing the atomic force field, selecting the CVFF and PCFF force fields as its atomic model force fields. The CVFF and PCFF force fields have been shown to be suitable for modeling most glass systems, and the initial model structures established are reasonable and stable. After selecting the corresponding force field, the MS software will uniformly mix the different atoms contained in the glass components according to the different force fields.

[0030] Optimizing the atomic model established for the experimental glass system enables accurate simulation of the structure.

[0031] Step 3: Perform molecular dynamics simulations on the optimized atomic models to obtain structural information for glass materials of different systems. Specifically, the optimized atomic models are integrated using Python software to obtain structural information for glass systems of different formulations.

[0032] In this example, molecular dynamics simulations were performed on the optimized atomic model, specifically including optimizing the interatomic interaction potentials, including but not limited to the Lennard-Jones potential, Morse potential, Born-Mayer potential, Johnson potential, and Buckingham potential. First, the number of atoms of the required element was calculated based on the glass's molar ratio. Then, different numbers of atoms were preliminarily mixed using MS software to generate an initial model. The established initial model was exported (data file). Next, a parameter file (in file) containing the potential function, simulation steps, and the desired structure was compiled. Finally, simulations were performed using LAMMPS software on a supercomputing platform to produce a reasonable final model.

[0033] In this embodiment, during the molecular dynamics simulation calculation of the optimized atomic model, its melting temperature is 3000-6000K, and its relaxation temperature after cooling is 300-2000K.

[0034] In this embodiment, during the molecular dynamics simulation calculation of the optimized atomic model, the high-temperature melting time is 30-60 ps, and the low-temperature relaxation time is 30-60 ps.

[0035] In this embodiment, during the molecular dynamics simulation calculation of the optimized atomic model, after the high-temperature melting stage, the cooling rate is 0.1-50 K / ps, including but not limited to 0.1 K / ps, 0.5 K / ps, 1 K / ps, 2 K / ps, 5 K / ps, 10 K / ps, etc.

[0036] In this embodiment, molecular dynamics simulation calculations are performed on the optimized atomic model, which also includes optimizing the selected ensemble, and the ensemble selections in the high-temperature melting stage, cooling stage, and relaxation stage include but are not limited to the canonical ensemble (NVT), microcanonical ensemble (NVE), isothermal and isobaric ensemble (NPT), isobaric and isoenthalpic ensemble (NPH), and grand canonical ensemble (VTμ).

[0037] The ensembles of the above various stages are all set up before the molecular dynamics simulation calculation process, and the specific requirements are reflected in the parameter file (in file).

[0038] In this embodiment, the structural information of glass materials of different systems is obtained, including but not limited to bond length (BL), bond angle (BA), ring size (RS), Qn, bridging oxygen (BO), non-bridging oxygen (NBO), tri-cluster oxygen (TBO), coordination number (CN) and Qn, etc. It also includes but is not limited to the connection between the forming body and the forming body, between the forming body and the modified body, and between the tetrahedron and the tetrahedron. The acquisition of all the above structural information is realized after simulation by LAMMPS software, that is, after LAMMPS software optimizes the initial model to the final model, the above structural information is obtained from the final model. Different changes in structural information will have different effects on the elastic modulus of the glass.

[0039] Step 4: Establishment of a component-structure-property dataset based on molecular dynamics simulation.

[0040] The elastic modulus test database of different glass materials is matched with the structural information to construct a component-structure-performance data set.

[0041] Composition-Structure-Property Dataset: Each row contains a dataset, and the columns contain composition, structure, and properties. The composition includes the total oxide content of the glass, the structure includes all structural information obtained through molecular dynamics simulation in step 3, and the properties include all elastic modulus information obtained through testing in step 1.

[0042] Step 5: Based on the constructed component-structure-property dataset, with elastic modulus as the dependent variable and structural information as the independent variable, a multivariate regression model is constructed to establish a quantitative structure-activity relationship for predicting the elastic modulus of glass. The structure factor parameters are calculated through the quantitative structure-activity relationship, where the independent variable structural information is a descriptor representing the component.

[0043] In this embodiment, the calculated elastic modulus is used as the dependent variable and the structural information of the glass is used as the independent variable. The analysis is performed, different structural factor parameters are calculated, and a multivariate regression model is constructed to construct the relationship between the elastic modulus and the structural information. The elastic modulus is used as the dependent variable y, and different structural information is used as the independent variables x1, x2, ... x k , 1, 2...k are used as numbers to distinguish different structural information. The structure factor parameters are calculated based on the simulation results. The regression model is obtained by solving the above parameters. The relationship is not limited to the following formula:

[0044] Among them, β0 is the total structure factor constant of the fitting, β1 is the structure factor parameter represented by the structural information x1, and β k is the structural information x k represents the structure factor parameter, and ε is a fixed empirical constant.

[0045] Step 6: Obtain glass materials used in actual experimental production, and establish a quantitative structure-activity relationship between the glass elastic modulus and different structural information through the above relationship. Based on the components corresponding to the glass materials in actual experiments or production, combined with the structure factor parameters corresponding to the different structural information obtained in the simulation, finally, calculate the elastic modulus of the glass corresponding to the corresponding structural information through the formula, thereby realizing the prediction of the elastic modulus of the glass materials in actual experiments or production.

[0046] This embodiment tests the elastic modulus of glass materials of different systems based on molecular dynamics simulation and constructs an elastic modulus database; calculates structure factor parameters of glass materials of different systems based on molecular dynamics simulation, and achieves accurate simulation of the elastic modulus by optimizing the parameters, while obtaining structural information of glass materials of different systems; corresponds the elastic modulus test database of different systems to the components, and constructs a component-structure-performance data set; uses the elastic modulus as the dependent variable and the different components as independent variables for analysis, calculates the structure factor parameters, constructs a multivariate regression model, and constructs the relationship between the elastic modulus and the components; predicts the elastic modulus in actual experimental production of glass materials through the component-structure-performance data set, through the descriptors of the structure factors corresponding to the components, and through the simulation of structural information by molecular dynamics.

[0047] Example 2 The purpose of this embodiment is to provide a glass elastic modulus prediction system based on molecular dynamics simulation, including: The database and model building module is configured to: obtain elastic modulus test data of glass materials of different systems and build an elastic modulus test database; Constructing an atomic model containing atoms of glass material components in different systems and optimizing the model to obtain an optimized atomic model; Perform molecular dynamics simulation calculations on the optimized atomic model to obtain structural information of glass materials in different systems; The quantitative structure-activity relationship building module is configured to: correspond the elastic modulus test database of different glass materials with structural information to construct a component-structure-property data set; Based on the constructed component-structure-property dataset, a multivariate regression model was constructed with elastic modulus as the dependent variable and structural information as the independent variable to establish a quantitative structure-activity relationship for predicting the modulus of the glass elastic model; The prediction module is configured to: obtain a glass material to be predicted, and predict the elastic modulus of the glass material to be predicted based on a quantitative structure-activity relationship of the modulus of the predicted glass elastic model.

[0048] Example 3 The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0049] Example 4 The purpose of this embodiment is to provide a computer-readable storage medium.

[0050] A computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the above method.

[0051] Example 5 The purpose of this embodiment is to provide a computer program product containing instructions, which, when running on a computer, enables the computer to execute the methods and functions involved in any of the above embodiments. The steps involved in the apparatus of the above embodiment correspond to those of the method embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0052] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0053] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for predicting glass elastic modulus based on molecular dynamics simulation, characterized in that: include: Obtain elastic modulus test data of glass materials of different systems and build an elastic modulus test database; Constructing an atomic model containing atoms of glass material components in different systems and optimizing the model to obtain an optimized atomic model; Perform molecular dynamics simulation calculations on the optimized atomic model to obtain structural information of glass materials in different systems; The elastic modulus test database of different glass materials is matched with the structural information to construct a component-structure-performance data set; Based on the constructed component-structure-property dataset, a multivariate regression model was constructed with elastic modulus as the dependent variable and structural information as the independent variable to establish a quantitative structure-activity relationship for predicting the modulus of the glass elastic model; A glass material to be predicted is obtained, and the elastic modulus of the glass material to be predicted is predicted based on a quantitative structure-activity relationship of the modulus of the predicted glass elastic model.

2. A method for predicting glass elastic modulus based on molecular dynamics simulation according to claim 1, characterized in that: The obtained glass system materials of different systems include glass materials of CaO-MgO-Al2O3-SiO2, MgO-Al2O3-SiO2, Na2O-CaO-SiO2, and B2O3-Al2O3-SiO2 systems.

3. The method for predicting glass elastic modulus based on molecular dynamics simulation according to claim 1, wherein: The structure includes atomic models of atoms of different glass material components, specifically: Establishing a single-atom model of the elements contained in the glass component, calculating the number of atoms corresponding to each element contained in the glass system, and mixing atoms of different types and numbers to obtain the atomic model; The model is optimized to obtain an optimized atomic model, specifically by optimizing the atomic force field, selecting CVFF and PCFF force fields as atomic model force fields, and uniformly mixing different atoms contained in the glass components according to different force fields.

4. The method for predicting glass elastic modulus based on molecular dynamics simulation according to claim 1, wherein: Molecular dynamics simulations are performed on the optimized atomic model, specifically including: optimizing the interatomic interaction potential, with options including Lennard-Jones potential, Morse potential, Born-Mayer potential, Johnson potential, and Buckingham potential. First, the number of atoms of the required corresponding elements is calculated based on the molar ratio of the glass. Then, atoms of different numbers are preliminarily mixed to generate an initial model. The final model is simulated based on the initial model and parameter file. Molecular dynamics simulation calculations are performed on the optimized atomic model, which also includes the optimized selected ensemble. The ensemble selections for the high-temperature melting stage, cooling stage, and relaxation stage include canonical ensemble, microcanonical ensemble, isothermal and isobaric ensemble, and isobaric and isoenthalpic ensemble.

5. The method for predicting glass elastic modulus based on molecular dynamics simulation according to claim 1, wherein: The structural information of glass materials of different systems is obtained, specifically including bond length, bond angle, ring size, Qn, bridging oxygen and non-bridging oxygen, coordination, and the connection between formers and formers, between formers and modified bodies, and between tetrahedra and tetrahedra.

6. The method for predicting glass elastic modulus based on molecular dynamics simulation according to claim 1, wherein: The establishment of a quantitative structure-activity relationship for predicting the elastic modulus of glass is specifically as follows: Taking the calculated elastic modulus as the dependent variable and the structural information of the glass as the independent variable, we conducted an analysis, calculated different structural factor parameters, constructed a multivariate regression model, and constructed the relationship between the elastic modulus and structural information. The relationship is expressed as follows: Among them, x1, x2, ... x k is the independent variable, representing different structural information, 1, 2...k is used as a number to distinguish different structural information, β0 is the total structure factor constant of the fitting, β1 is the structure factor parameter represented by the structural information x1, β k is the structural information x k represents the structural factor parameter, ε is a fixed empirical constant, and y is the elastic modulus.

7. A glass elastic modulus prediction system based on molecular dynamics simulation, characterized in that: include: The database and model building module is configured to: obtain elastic modulus test data of glass materials of different systems and build an elastic modulus test database; Constructing an atomic model containing atoms of glass material components in different systems and optimizing the model to obtain an optimized atomic model; Perform molecular dynamics simulation calculations on the optimized atomic model to obtain structural information of glass materials in different systems; The quantitative structure-activity relationship building module is configured to: correspond the elastic modulus test database of different glass materials with structural information to construct a component-structure-property data set; Based on the constructed component-structure-property dataset, a multivariate regression model was constructed with elastic modulus as the dependent variable and structural information as the independent variable to establish a quantitative structure-activity relationship for predicting the modulus of the glass elastic model; The prediction module is configured to: obtain a glass material to be predicted, and predict the elastic modulus of the glass material to be predicted based on a quantitative structure-activity relationship of the modulus of the predicted glass elastic model.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are performed.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

Images