CN-122024941-A - Method, apparatus, computer device, readable storage medium, and program product for predicting glass performance

CN122024941ACN 122024941 ACN122024941 ACN 122024941ACN-122024941-A

Abstract

The present application relates to a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for predicting the performance of glass. The method comprises the steps of obtaining target glass component information for indicating components of glass to be predicted, searching potential crystalline structures of the components in a space group through a particle swarm optimization algorithm and a Monte Carlo method to determine stable crystalline structures of the components, establishing a composition relation between the stable crystalline structures and the glass to be predicted based on a thermodynamic statistics principle, and obtaining a performance parameter prediction result corresponding to the glass to be predicted based on the content of each stable crystalline structure and corresponding performance parameters. The method can improve the accuracy of the performance prediction result of the glass material.

Inventors

CHEN DONGDAN
SHEN YIHONG
WU MINBO
Lun Zhenjie
YANG ZHONGMIN

Assignees

华南理工大学

Dates

Publication Date: 20260512
Application Date: 20251210

Claims (10)

1. A method of predicting the performance of a glass, the method comprising: acquiring target glass component information, wherein the target glass component information is used for indicating the composition components of glass to be predicted; Searching potential crystalline structures of the components in a space group through a particle swarm optimization algorithm and a Monte Carlo method, and determining all stable crystalline structures of the components; Establishing a composition relation between the stable crystalline structure and the glass to be predicted based on a thermodynamic statistics principle, wherein the composition relation is used for indicating the content of each stable crystalline structure required by combining to form the glass to be predicted; and obtaining a performance parameter prediction result corresponding to the glass to be predicted based on the content of each stable crystalline structure and the performance parameters corresponding to each stable crystalline structure.
2. The method of claim 1, wherein the searching for potential crystalline structures of the constituent components by a particle swarm optimization algorithm and a monte carlo method to determine all stable crystalline structures of the constituent components comprises: searching potential crystalline structures of the components by a particle swarm optimization algorithm and a Monte Carlo method, and determining the potential crystalline structures corresponding to the components; And obtaining the corresponding formation energy of each potential crystalline structure, and taking each potential crystalline structure with the corresponding formation energy smaller than 0 as a stable crystalline structure of the composition component.
3. The method according to claim 1, wherein said establishing a composition relationship between the stable crystalline structure and the glass to be predicted based on thermodynamic statistics comprises: obtaining the formation energy corresponding to each stable crystalline structure, and obtaining the thermodynamic environment temperature of a thermodynamic system formed by each stable crystalline structure; Determining the formation probability corresponding to any stable crystalline structure according to the formation energy corresponding to all the stable crystalline structures, the formation energy corresponding to any stable crystalline structure and the thermodynamic environment temperature, wherein the formation probability corresponding to any stable crystalline structure is represented in the category of statistical thermodynamics, and the probability of any stable crystalline structure appearing in a glass structure space; and taking the forming probability of each stable crystalline structure as the content of each stable crystalline structure required for forming the glass to be predicted.
4. A method according to claim 3, wherein the formula for the formation probability is as follows: Wherein, the Representing the constituent elements Seed stable crystalline structure of the first kind The corresponding formation energy of the stable crystalline structure, Representing the constituent elements Seed stable crystalline structure of the first kind The corresponding formation energy of the stable crystalline structure, Representing the boltzmann constant, Representing the temperature of the said thermodynamic environment, Represent the first The probability of formation corresponding to the stable crystalline structure.
5. The method according to claim 4, wherein the obtaining the predicted result of the performance parameter corresponding to the glass to be predicted based on the content of each stable crystalline structure and the performance parameter corresponding to each stable crystalline structure comprises: And carrying out weighted calculation on the forming probability and the performance parameters corresponding to each stable crystalline structure to obtain a performance parameter prediction result corresponding to the glass to be predicted, wherein the calculation formula of the performance parameter prediction result is as follows: Wherein, the Representing the result of the prediction of the performance parameter, Represent the first And stabilizing the corresponding performance parameters of the crystalline structure.
6. The method of claim 1, wherein the performance parameter comprises at least one of an optical performance parameter and a dielectric performance parameter, the optical performance parameter comprises a reflectance spectrum parameter, and the dielectric performance parameter comprises an absorbance spectrum parameter and a dielectric constant.
7. A device for predicting the performance of glass, the device comprising: the information acquisition module is used for acquiring target glass component information, wherein the target glass component information is used for indicating the composition components of glass to be predicted; the structure searching module is used for searching potential crystalline structures of the components in a space group through a particle swarm optimization algorithm and a Monte Carlo method, and determining all stable crystalline structures of the components; the relationship establishing module is used for establishing a composition relationship between the stable crystalline structure and the glass to be predicted based on a thermodynamic statistics principle, wherein the composition relationship is used for indicating the content of each stable crystalline structure required by combining to form the glass to be predicted; and the performance prediction module is used for obtaining a performance parameter prediction result corresponding to the glass to be predicted based on the content of each stable crystalline structure and the performance parameters corresponding to each stable crystalline structure.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

Description

Method, apparatus, computer device, readable storage medium, and program product for predicting glass performance Technical Field The present application relates to the technical field of glass materials, and in particular, to a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for predicting glass performance. Background Glass materials are one of the most important materials and play a vital role in daily life and scientific research. The properties of glass, such as optical properties, which determine the reflective properties of the glass material for light when interacting with light of different frequencies, and dielectric properties, which determine the behavior of the glass when interacting with an electric field, are very important fundamental physical parameters. In the related art, the development of new components of the optical glass depends on a repeated experiment trial-and-error method with long periodicity, high cost and low efficiency, and as the glass structure is a topologically disordered atomic network and lacks of long-range order of similar crystal structures, the mathematical model of the glass structure in the related art is low in accuracy and poor in interpretability, the performance of the glass material is difficult to accurately predict, and the research and development of the glass with specific performance are seriously hindered. Therefore, there is a problem in the related art that the performance prediction result of the glass material is not accurate enough. Disclosure of Invention In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer-readable storage medium, and a computer program product for predicting the performance of a glass that can improve the accuracy of the performance prediction result of the glass material. In a first aspect, the present application provides a method for predicting the performance of glass, comprising: acquiring target glass component information, wherein the target glass component information is used for indicating the composition components of glass to be predicted; Searching potential crystalline structures of the components in a space group through a particle swarm optimization algorithm and a Monte Carlo method, and determining all stable crystalline structures of the components; Establishing a composition relation between the stable crystalline structure and the glass to be predicted based on a thermodynamic statistics principle, wherein the composition relation is used for indicating the content of each stable crystalline structure required by combining to form the glass to be predicted; and obtaining a performance parameter prediction result corresponding to the glass to be predicted based on the content of each stable crystalline structure and the performance parameters corresponding to each stable crystalline structure. In one embodiment, the searching the latent crystalline structure of the composition component by the particle swarm optimization algorithm and the Monte Carlo method to determine all stable crystalline structures of the composition component comprises: searching potential crystalline structures of the components by a particle swarm optimization algorithm and a Monte Carlo method, and determining the potential crystalline structures corresponding to the components; And obtaining the corresponding formation energy of each potential crystalline structure, and taking each potential crystalline structure with the corresponding formation energy smaller than 0 as a stable crystalline structure of the composition component. In one embodiment, the establishing a composition relationship between the stable crystalline structure and the glass to be predicted based on thermodynamic statistics principles includes: obtaining the formation energy corresponding to each stable crystalline structure, and obtaining the thermodynamic environment temperature of a thermodynamic system formed by each stable crystalline structure; Determining the formation probability corresponding to any stable crystalline structure according to the formation energy corresponding to all the stable crystalline structures, the formation energy corresponding to any stable crystalline structure and the thermodynamic environment temperature, wherein the formation probability corresponding to any stable crystalline structure is represented in the category of statistical thermodynamics, and the probability of any stable crystalline structure appearing in a glass structure space; and taking the forming probability of each stable crystalline structure as the content of each stable crystalline structure required for forming the glass to be predicted. In one embodiment, the formula for the formation probability is as follows: Wherein, the Representing the constituent elementsSeed stable crystalline structure of the first kindThe corresponding formation energ