Glass performance prediction method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a glass performance prediction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring target glass component information for indicating components of to-be-predicted glass; in a space group, searching potential crystalline structures of the components through a particle swarm optimization algorithm and a Monte Carlo method, and determining stable crystalline structures of the components; establishing a composition relationship between the stable crystalline structure and the to-be-predicted glass based on a thermodynamic statistical principle; and obtaining a performance parameter prediction result corresponding to the to-be-predicted glass based on the content of each stable crystalline structure and the corresponding performance parameter. The method can improve the accuracy of the performance prediction result of the glass material.

Description

Technical Field

[0001] This application relates to the field of glass materials technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting the performance of glass. Background Technology

[0002] Glass, as one of the most important materials, plays a vital role in both daily life and scientific research. The properties of glass, such as optical and dielectric properties, are crucial fundamental physical parameters. Optical properties determine the light reflection characteristics of glass when interacting with light of different frequencies, while dielectric properties determine its behavior when interacting with an electric field.

[0003] In related technologies, the development of new optical glass components relies on a repetitive trial-and-error method that is time-consuming, costly, and inefficient. Since the glass structure is a topologically disordered atomic network and lacks the long-range order of a crystal structure, the mathematical models of glass structures in related technologies have low accuracy and poor interpretability, making it difficult to accurately predict the performance of glass materials and seriously hindering the research and development of glasses with specific properties.

[0004] Therefore, there is a problem with the inaccuracy of performance prediction results for glass materials in related technologies. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for predicting the performance of glass materials, which can improve the accuracy of the performance prediction results of glass materials, in response to the above-mentioned technical problems.

[0006] In a first aspect, this application provides a method for predicting the performance of glass, including:

[0007] Obtain target glass composition information; the target glass composition information is used to indicate the composition of the glass to be predicted;

[0008] In the space group, the potential crystalline structures of the constituent components are searched using particle swarm optimization algorithm and Monte Carlo method to determine all stable crystalline structures of the constituent components;

[0009] The compositional relationship between the stable crystalline structure and the glass to be predicted is established based on the principles of thermodynamic statistics; the compositional relationship is used to indicate the content of each of the stable crystalline structures required to form the glass to be predicted.

[0010] Based on the content of each stable crystalline structure and the performance parameters corresponding to each stable crystalline structure, the prediction results of the performance parameters corresponding to the glass to be predicted are obtained.

[0011] In one embodiment, the step of searching for potential crystalline structures of the constituent components using a particle swarm optimization algorithm and a Monte Carlo method to determine all stable crystalline structures of the constituent components includes:

[0012] The potential crystalline structures of the constituent components are searched using particle swarm optimization and Monte Carlo methods to determine the potential crystalline structures corresponding to the constituent components.

[0013] Obtain the formation energy corresponding to each potential crystalline structure, and take each potential crystalline structure with a formation energy less than 0 as the stable crystalline structure of the constituent component.

[0014] In one embodiment, establishing the compositional relationship between the stable crystalline structure and the glass to be predicted based on thermodynamic statistical principles includes:

[0015] The formation energy corresponding to each of the stable crystalline structures and the thermodynamic environment temperature of the thermodynamic system composed of each of the stable crystalline structures are obtained.

[0016] For any stable crystalline structure, the formation probability of any stable crystalline structure is determined based on the formation energy corresponding to all the stable crystalline structures, the formation energy corresponding to any stable crystalline structure, and the thermodynamic environment temperature. The formation probability of any stable crystalline structure represents the probability that any stable crystalline structure appears in the glass structure space within the scope of statistical thermodynamics.

[0017] The formation probability of each of the aforementioned stable crystalline structures is taken as the content of each of the aforementioned stable crystalline structures required to form the glass to be predicted.

[0018] In one embodiment, the formula for the formation probability is as follows:

[0019]

[0020] in, Indicating the components The first stable crystalline structure The formation energy corresponding to a stable crystalline structure Indicating the components The first stable crystalline structure The formation energy corresponding to a stable crystalline structure Represents Boltzmann's constant. This represents the temperature of the thermodynamic environment. Indicates the first The probability of formation corresponding to a stable crystalline structure.

[0021] In one embodiment, obtaining the performance parameter prediction result of the glass to be predicted based on the content of each of the stable crystalline structures and the performance parameters corresponding to each of the stable crystalline structures includes:

[0022] The formation probability and performance parameters corresponding to each of the aforementioned stable crystalline structures are weighted and calculated to obtain the predicted performance parameters of the glass to be predicted; the calculation formula for the predicted performance parameters is as follows:

[0023]

[0024] in, This indicates the predicted results of the performance parameters. Indicates the first Performance parameters corresponding to a stable crystalline structure.

[0025] In one embodiment, the performance parameters include at least one of optical performance parameters and dielectric performance parameters; the optical performance parameters include a reflection spectrum parameter; and the dielectric performance parameters include an absorption spectrum parameter and a dielectric constant.

[0026] Secondly, this application also provides a glass performance prediction device, comprising:

[0027] An information acquisition module is used to acquire target glass composition information; the target glass composition information is used to indicate the composition of the glass to be predicted.

[0028] The structure search module is used to search for the potential crystalline structures of the constituent components in the space group using the particle swarm optimization algorithm and the Monte Carlo method, and to determine all stable crystalline structures of the constituent components.

[0029] A relationship establishment module is used to establish a compositional relationship between the stable crystalline structure and the glass to be predicted based on thermodynamic statistical principles; the compositional relationship is used to indicate the content of each of the stable crystalline structures required to form the glass to be predicted;

[0030] The performance prediction module is used to obtain the performance parameter prediction results of the glass to be predicted based on the content of each of the stable crystalline structures and the performance parameters corresponding to each of the stable crystalline structures.

[0031] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the steps of the method described above.

[0032] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0033] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method.

[0034] The aforementioned glass performance prediction method, apparatus, computer equipment, computer-readable storage medium, and computer program product acquire target glass composition information; the target glass composition information is used to indicate the composition of the glass to be predicted; in a space group, the potential crystalline structures of the composition are searched using particle swarm optimization algorithm and Monte Carlo method to determine all stable crystalline structures of the composition; a compositional relationship between the stable crystalline structures and the glass to be predicted is established based on thermodynamic statistical principles; the compositional relationship is used to indicate the content of each stable crystalline structure required to form the glass to be predicted; based on the content of each stable crystalline structure and the performance parameters corresponding to each stable crystalline structure, the performance parameter prediction results corresponding to the glass to be predicted are obtained.

[0035] Thus, after determining the composition of the glass to be predicted using the target glass composition information, a broad and deep search of the potential crystalline structures of the components is performed using particle swarm optimization and Monte Carlo methods to identify all stable crystalline structures of the components. This systematically lists the stable structures that the components of the glass to be predicted can form. Then, based on statistical thermodynamics, the more complex glass structures are mathematically analyzed to determine the content of each stable crystalline structure required to form the glass to be predicted. Based on the content of each stable crystalline structure and the corresponding performance parameters, the performance parameters of the glass to be predicted can be predicted, effectively improving the accuracy of the predicted performance parameters. This method is applicable to various glass systems and can achieve quantitative prediction of glass material properties without experimental data fitting. Furthermore, it can effectively screen out glasses with superior performance, shorten the development cycle of new glass components, and reduce costs. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1This is a flowchart illustrating a method for predicting the performance of glass in one embodiment;

[0038] Figure 2 This is a schematic diagram showing the predicted reflectance spectral parameters of a glass in one embodiment;

[0039] Figure 3 This is a schematic diagram showing the predicted dielectric properties of a glass in one embodiment;

[0040] Figure 4 This is a flowchart illustrating a method for predicting the performance of glass in another embodiment;

[0041] Figure 5 This is a structural block diagram of a glass performance prediction device in one embodiment;

[0042] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] 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.

[0044] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0045] In one embodiment, such as Figure 1 As shown, a method for predicting the performance of glass is provided. This embodiment illustrates the application of this method to a computer device. It is understood that the computer device can be a terminal, a server, or a system including both a terminal and a server, and the method is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0046] Step S110: Obtain the target glass composition information.

[0047] Among them, the target glass composition information is used to indicate the composition of the glass to be predicted.

[0048] In practice, the computer device can acquire the target glass composition information, which is used to indicate the composition of the glass to be predicted. The glass to be predicted refers to glass for which performance prediction is required, such as glass for which optical performance prediction is required, or glass for which dielectric performance prediction is required.

[0049] In practical applications, users can input the chemical formula of the glass composition to be predicted (such as GeO2, germanium dioxide) into the computer device. In other embodiments, users can select the corresponding composition from a preset material composition library provided by the computer device.

[0050] Step S120: In the space group, the potential crystalline structures of the constituent components are searched using the particle swarm optimization algorithm and the Monte Carlo method to determine all stable crystalline structures of the constituent components.

[0051] Among them, the potential crystalline structure can refer to the crystalline structure that the components of the glass to be predicted may form under specific conditions (such as temperature and pressure).

[0052] In practice, the computer equipment can acquire all stable crystalline structures of the constituent components in the structural space of the glass to be predicted. Specifically, in the space group, the potential crystalline structures of the constituent components are searched in both breadth and depth using particle swarm optimization and Monte Carlo methods. The collaboration of the two algorithms achieves full coverage and precise screening of potential crystalline structures, thereby determining all stable crystalline structures of the constituent components.

[0053] In some embodiments, during the search for potential crystalline structures, atoms are randomly placed into a space group according to an atomic ratio (e.g., 1:2 for GeO2), and then each space group is traversed; this process is called Monte Carlo random sampling. The positions of the atoms are then adjusted based on the symmetry of the space group. Subsequently, a particle swarm optimization algorithm is used to iteratively optimize the initial structure of the glass to be predicted, minimizing its energy. Each space group has its own unique symmetry and atomic positions.

[0054] In some embodiments, the initial structure of the glass to be predicted can be calculated based on first principles and then iteratively optimized in multiple steps under different constraints and accuracies. The particle swarm optimization algorithm is used to select atomic combinations with a formation energy of less than 0 as stable crystalline structures.

[0055] Thus, by employing particle swarm optimization and Monte Carlo methods in synergy to achieve both breadth and depth of search, the probability of discovering globally stable structures can be significantly improved.

[0056] Step S130: Establish the compositional relationship between the stable crystalline structure and the glass to be predicted based on the principle of thermodynamic statistics.

[0057] The compositional relationship is used to indicate the content of each stable crystalline structure required to form the glass to be predicted.

[0058] In practice, since the glass structure is a topologically disordered atomic network and lacks the long-range order of a crystal structure, the glass structure is suitable for being described by statistics. The structure of the glass to be predicted can be analyzed as a statistical set of stable structures.

[0059] Therefore, in this embodiment, a compositional relationship between stable crystalline structures and the glass to be predicted can be established based on thermodynamic statistical principles. This compositional relationship is used to indicate the content of each stable crystalline structure required to form the glass to be predicted.

[0060] Step S140: Based on the content of each stable crystalline structure and the performance parameters corresponding to each stable crystalline structure, obtain the prediction results of the performance parameters corresponding to the glass to be predicted.

[0061] In practice, computer equipment can combine the content of each stable crystalline structure required to form the glass to be predicted, as well as the performance parameters corresponding to each stable crystalline structure, and obtain the prediction results of the performance parameters of the glass to be predicted.

[0062] The performance parameters include at least one of optical performance parameters and dielectric performance parameters; the optical performance parameters include reflection spectrum parameters, which represent the material's ability to reflect light of different energies; the dielectric performance parameters include absorption spectrum parameters (characterizing the material's absorption characteristics of electromagnetic waves) and dielectric constant (characterizing the material's polarization and dispersion capabilities).

[0063] Accordingly, the predicted performance parameters of the glass to be predicted may include the predicted optical performance parameters (such as the predicted reflection spectrum parameters) and / or the predicted dielectric performance parameters (such as the predicted absorption spectrum parameters and the predicted dielectric constant).

[0064] In the aforementioned method for predicting the performance of glass, the following steps are taken: First, the target glass composition information is obtained; this information indicates the composition of the glass to be predicted. Then, in a space group, a particle swarm optimization algorithm and a Monte Carlo method are used to search for the potential crystalline structures of the composition, identifying all stable crystalline structures. Based on thermodynamic statistical principles, a compositional relationship is established between the stable crystalline structures and the glass to be predicted. This relationship indicates the content of each stable crystalline structure required to form the glass to be predicted. Finally, based on the content of each stable crystalline structure and the corresponding performance parameters, the predicted performance parameters of the glass to be predicted are obtained.

[0065] Thus, after determining the composition of the glass to be predicted using the target glass composition information, a broad and deep search of the potential crystalline structures of the components is performed using particle swarm optimization and Monte Carlo methods to identify all stable crystalline structures of the components. This systematically lists the stable structures that the components of the glass to be predicted can form. Then, based on statistical thermodynamics, the more complex glass structures are mathematically analyzed to determine the content of each stable crystalline structure required to form the glass to be predicted. Based on the content of each stable crystalline structure and the corresponding performance parameters, the performance parameters of the glass to be predicted can be predicted, effectively improving the accuracy of the predicted performance parameters. This method is applicable to various glass systems and can achieve quantitative prediction of glass material properties without experimental data fitting. Furthermore, it can effectively screen out glasses with superior performance, shorten the development cycle of new glass components, and reduce costs.

[0066] In some embodiments, the potential crystalline structures of the constituent components are searched using a particle swarm optimization algorithm and a Monte Carlo method to determine all stable crystalline structures of the constituent components. This includes: searching for potential crystalline structures of the constituent components using a particle swarm optimization algorithm and a Monte Carlo method to determine the potential crystalline structures corresponding to the constituent components; obtaining the formation energy corresponding to each potential crystalline structure; and taking each potential crystalline structure with a formation energy less than 0 as a stable crystalline structure of the constituent components.

[0067] In practice, the computer device searches for the potential crystalline structures of the constituent components using particle swarm optimization and Monte Carlo methods to determine all stable crystalline structures of the constituent components. By performing breadth and depth searches on the potential crystalline structures of the constituent components using particle swarm optimization and Monte Carlo methods, the potential crystalline structures corresponding to the constituent components can be determined. By obtaining the formation energy corresponding to each potential crystalline structure, potential crystalline structures with formation energies greater than 0 are eliminated, and potential crystalline structures with formation energies less than 0 are taken as stable crystalline structures of the constituent components.

[0068] In some embodiments, during the search process, the formation energy of each potential crystalline structure can be calculated in real time using first-principles calculation software, and finally all potential crystalline structures with a formation energy < 0 are selected, which are the stable crystalline structures of the composition of the glass to be predicted.

[0069] Thus, first-principles calculations are based on fundamental principles of quantum mechanics and do not rely on empirical parameters, ensuring the objectivity and reliability of formation energy calculations and providing a basis for judging structural stability. Using formation energy < 0 as the thermodynamic stability criterion, stable crystalline structures and metastable structures are accurately distinguished, ensuring the accuracy of screening. The stable crystalline structures obtained through screening directly reflect the thermodynamically optimal structure under specific conditions, providing core structural data for subsequent performance prediction.

[0070] In one application embodiment, a joint search system combining particle swarm optimization (PSO) and Monte Carlo methods can be built based on crystal structure prediction software to collaboratively achieve breadth and depth searches, significantly improving the probability of discovering globally stable structures. Crystal structure prediction software is often used in conjunction with first-principles calculation software to analyze energy data and drive structural evolution. Taking GeO2 as an example, the crystal structure prediction software, combined with first-principles calculation software, is used to search for globally stable structures in GeO2. Due to the extremely high computational cost of the global search, the system needs to be constrained at three different levels of precision, with multiple iterations to obtain the desired globally stable structure more quickly. This corresponds to three computational parameter configuration files for the crystal structure prediction software, with precision level labels ranging from Low, Normal to Accurate, and different electronic self-consistent convergence thresholds set. The global search yielded 1500 possible structures (i.e., potential crystalline structures), of which 266 potential crystalline structures have a formation energy greater than 0. Therefore, there are 1234 stable structures (i.e., stable crystalline structures).

[0071] In some embodiments, establishing the compositional relationship between stable crystalline structures and the glass to be predicted based on thermodynamic statistical principles includes: obtaining the formation energy corresponding to each stable crystalline structure and the thermodynamic environment temperature of the thermodynamic system composed of each stable crystalline structure; for any stable crystalline structure, determining the formation probability corresponding to any stable crystalline structure based on the formation energy corresponding to all stable crystalline structures, the formation energy corresponding to any stable crystalline structure, and the thermodynamic environment temperature; the formation probability corresponding to any stable crystalline structure characterizes the probability that any stable crystalline structure appears in the glass structure space within the scope of statistical thermodynamics; and using the formation probability corresponding to each stable crystalline structure as the content of each stable crystalline structure required to form the glass to be predicted.

[0072] Since glass structures are topologically disordered atomic networks, lacking the long-range order of crystal structures, they are best described using statistical measures. Therefore, the formation probability of each stable crystalline structure can be calculated based on its formation energy, and this probability can be used to replace the required content of each stable crystalline structure to form the glass to be predicted.

[0073] In practice, the computer equipment can obtain the formation energy corresponding to each stable crystalline structure and the thermodynamic environment temperature of the thermodynamic system composed of each stable crystalline structure. For any stable crystalline structure, the formation probability corresponding to that stable crystalline structure is determined based on the formation energy corresponding to all stable crystalline structures, the formation energy corresponding to that particular stable crystalline structure, and the thermodynamic environment temperature. The formation probability corresponding to that particular stable crystalline structure represents the probability that the particular stable crystalline structure appears in the glass structure space within the scope of statistical thermodynamics. The formation probability corresponding to each stable crystalline structure is used as the content of each stable crystalline structure required to form the glass to be predicted.

[0074] The formula for the formation probability is as follows:

[0075]

[0076] in, Indicates the components The first stable crystalline structure The formation energy corresponding to a stable crystalline structure Indicates the components The first stable crystalline structure The formation energy corresponding to a stable crystalline structure Represents Boltzmann's constant. This indicates the temperature of the thermodynamic environment (system temperature). Indicates the first The probability of formation corresponding to a stable crystalline structure.

[0077] In some embodiments, based on the content of each stable crystalline structure and the performance parameters corresponding to each stable crystalline structure, the performance parameter prediction results for the glass to be predicted are obtained, including: weighting the formation probability and performance parameters corresponding to each stable crystalline structure to obtain the performance parameter prediction results for the glass to be predicted; the calculation formula for the performance parameter prediction results is as follows:

[0078]

[0079] in, This indicates the predicted performance parameters. Indicates the first Performance parameters corresponding to a stable crystalline structure.

[0080] Specifically, when the performance parameters corresponding to the input stable crystalline structure are optical performance parameters, the output performance parameter prediction result is the optical performance parameter prediction result; when the performance parameters corresponding to the input stable crystalline structure are dielectric performance parameters, the output performance parameter prediction result is the dielectric performance parameter prediction result.

[0081] Thus, based on the principles of statistical thermodynamics, the formation energies of each stable crystalline structure and the thermodynamic environment temperature of the thermodynamic system composed of each stable crystalline structure are combined to calculate the formation probability of each stable crystalline structure, accurately quantifying the thermodynamic proportion of each stable crystalline structure, thus avoiding the deviation between the performance prediction of a single structure and the actual state of the polycrystalline glass material. Then, by weighting the performance parameters corresponding to each stable crystalline state with the formation probability as the weight, the performance prediction results of the glass to be predicted are more in line with the actual state of the material, improving the accuracy and reliability of the prediction results.

[0082] In some embodiments, obtaining the optical performance parameters corresponding to stable crystalline structures includes performing a first-step self-consistent calculation on the stable crystalline structures selected by the crystal structure prediction software, adding band number labels to accurately describe optical transitions; and performing a second-step non-self-consistent calculation based on the first-step self-consistent calculation using optical performance calculation labels to obtain the optical performance parameters of each stable crystalline structure.

[0083] In other embodiments, the acquisition of dielectric performance parameters corresponding to stable crystalline structures includes performing a first self-consistent calculation on the stable crystalline structures selected by the crystal structure prediction software to ensure the accurate convergence of charge density and wave function to lay the foundation for dielectric response calculation; and performing a second non-self-consistent calculation based on the first self-consistent calculation using dielectric performance calculation tags to obtain the dielectric performance parameters of each stable crystalline structure.

[0084] In some application examples, taking GeO2 as the composition of the glass to be predicted, and considering that GeO2 has 1234 stable crystalline structures, first-principles calculations are performed based solely on the fundamental laws of quantum mechanics without relying on empirical parameters. This allows for the calculation of material properties at the atomic scale, enabling the prediction of macroscopic properties from atomic structure. Since the glass structure is a topologically disordered atomic network, lacking the long-range order of crystal structures, it is suitable for description using statistical measures. The structure of GeO2 glass is analyzed as a statistical set of stable crystalline structures, and first-principles calculation software is used to perform atomic-scale computer simulations of the glass.

[0085] Specifically, using a preset script, the atomic position information data of 1234 stable crystalline structures calculated by the crystal structure prediction software are edited into basic structure files (used to store key information of the crystalline structure) recognizable by the first-principles calculation software. These files are then placed into 1234 folders as input files for self-consistent calculations. The preset script then batch-generates input files (key files for electronic structure, energy, etc.) based on each basic structure file. It is important to note that an electronic band structure control parameter can be added to the calculation parameter configuration file (used to store control parameters for electronic structure calculations) in one of the input files. This is to generate an initial wavefunction file containing a sufficient number of unoccupied states (empty bands). This initial wavefunction file, rich in empty bands, is the basis for accurately calculating the optical transition matrix elements and / or dielectric response matrix elements in subsequent non-self-consistent calculations. Only by containing a sufficient number of high-energy unoccupied states can the excitation of electrons from the valence band to the conduction band (especially high-energy excitations) be accurately described. This ensures that the imaginary part of the calculated dielectric function decays naturally at sufficiently high energies, thereby guaranteeing the convergence and accuracy of the subsequent Kramers-Kronig transform. In this embodiment, the electronic band number control parameter is set to 256, and first-principles calculation software is used to perform the first step of self-consistent calculation for each stable crystal structure.

[0086] After the self-consistent calculation in the first step has converged, the calculation parameter configuration files of each stable crystalline structure are rewritten in batches using a preset script. By controlling the tags of optical property calculation and / or dielectric property calculation, the second step of non-self-consistent calculation is performed. After the calculation is completed, the formation energy and corresponding performance parameters (such as optical performance parameters and dielectric performance parameters) of each stable crystalline structure are extracted from the output results using a preset script.

[0087] Specifically, the formation probability of 1234 stable crystalline structures is calculated using the following formulas. :

[0088]

[0089] The performance parameters of the glass to be predicted are predicted by weighted calculation based on the formation probability and performance parameters of each stable crystalline structure.

[0090] The calculation formulas for the predicted performance parameters of the glass to be predicted are as follows:

[0091]

[0092] In some embodiments, the optical performance parameter predictions calculated by the first-principles calculation software are primarily the reflection spectrum parameter predictions. Since the energy data calculated by the software has slight discrepancies, interpolation can be used during data export to integrate the dielectric functions of 1234 stable crystalline structures into a unified grid for subsequent data processing. Interpolation is a mathematical method for constructing continuous functions from known discrete data points; its core objective is to reasonably infer the values ​​of unknown points from given data points.

[0093] The reflection spectrum is plotted based on the predicted results of the weighted reflection spectrum parameters, as shown in the figure. Figure 2 As shown, the calculated simulation results of the reflected spectrum parameters of GeO2 glass (cal data) are in good agreement with the experimental results (expt data), indicating that the prediction method can effectively predict the optical properties of glass.

[0094] The method provided in this application allows for accurate prediction of the optical performance parameters of various glasses simply by calculating the optical performance parameters of the stable crystalline structure of the glass to be predicted.

[0095] In other embodiments, the dielectric property parameter predictions calculated by the first-principles calculation software can be characterized by dielectric functions, with the real part corresponding to the predicted dielectric constant and the imaginary part corresponding to the predicted absorption spectrum. Due to slight discrepancies in the energy data calculated by the software, interpolation is used to integrate the dielectric functions of 1234 stable crystalline structures into a unified grid for subsequent data processing. A graph showing the weighted calculated dielectric property parameter predictions is shown below. Figure 3 The diagram shown includes the real part ε1 and the imaginary part ε2 of the dielectric function. Figure 3 As shown, the calculated and simulated absorption spectrum parameters (ε2) of GeO2 glass are in good agreement with the experimental measurements (Expt.ref). Furthermore, ε2 and ε1 are strictly coupled through the Kramers-Kronig relationship, satisfying the causality requirement. This indicates that the prediction method can effectively predict the dielectric properties of the glass.

[0096] It is understood that by using the method provided in this application, it is only necessary to calculate the dielectric properties of the stable crystalline structure of the glass to be predicted, and then the dielectric properties of various different glasses can be accurately predicted.

[0097] In another embodiment, such as Figure 4 The diagram shows a flowchart of a method for predicting the performance of glass, including the following steps:

[0098] Step S402: Obtain the target glass composition information.

[0099] Step S404: Search for the potential crystalline structures of the constituent components using particle swarm optimization algorithm and Monte Carlo method to determine the potential crystalline structures corresponding to the constituent components.

[0100] Step S406: Obtain the formation energy corresponding to each potential crystalline structure, and take each potential crystalline structure with a formation energy less than 0 as the stable crystalline structure of the constituent component.

[0101] Step S408: Obtain the formation energy corresponding to each stable crystalline structure, and the thermodynamic environment temperature of the thermodynamic system composed of each stable crystalline structure.

[0102] Step S410: For any stable crystalline structure, determine the formation probability of any stable crystalline structure based on the formation energy corresponding to all stable crystalline structures, the formation energy corresponding to any stable crystalline structure, and the thermodynamic environment temperature.

[0103] Step S412: The formation probability and performance parameters corresponding to each stable crystalline structure are weighted and calculated to obtain the predicted performance parameters of the glass to be predicted.

[0104] It should be noted that the specific limitations of the above steps can be found in the specific limitations of a glass performance prediction method described above.

[0105] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0106] Based on the same inventive concept, this application also provides a glass performance prediction device for implementing the glass performance prediction method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more glass performance prediction device embodiments provided below can be found in the limitations of the glass performance prediction method described above, and will not be repeated here.

[0107] In one exemplary embodiment, such as Figure 5 As shown, a glass performance prediction device is provided, comprising: an information acquisition module 510, a structure search module 520, a relationship establishment module 530, and a performance prediction module 540, wherein:

[0108] The information acquisition module 510 is used to acquire target glass composition information; the target glass composition information is used to indicate the composition of the glass to be predicted.

[0109] The structure search module 520 is used to search for the potential crystalline structures of the constituent components in the space group using a particle swarm optimization algorithm and a Monte Carlo method, and to determine all stable crystalline structures of the constituent components.

[0110] The relationship establishment module 530 is used to establish the compositional relationship between the stable crystalline structure and the glass to be predicted based on the principle of thermodynamic statistics; the compositional relationship is used to indicate the content of each of the stable crystalline structures required to form the glass to be predicted.

[0111] The performance prediction module 540 is used to obtain the performance parameter prediction result of the glass to be predicted based on the content of each of the stable crystalline structures and the performance parameters corresponding to each of the stable crystalline structures.

[0112] In one embodiment, the structure search module 520 is specifically used to search for the potential crystalline structures of the constituent components using a particle swarm optimization algorithm and a Monte Carlo method, determine the potential crystalline structures corresponding to the constituent components, obtain the formation energy corresponding to each potential crystalline structure, and take each potential crystalline structure with a formation energy less than 0 as the stable crystalline structure of the constituent component.

[0113] In one embodiment, the relationship establishment module 530 is specifically used to obtain the formation energy corresponding to each of the stable crystalline structures, and the thermodynamic environment temperature of the thermodynamic system composed of each of the stable crystalline structures; for any stable crystalline structure, the formation probability corresponding to the any stable crystalline structure is determined based on the formation energy corresponding to all the stable crystalline structures, the formation energy corresponding to the any stable crystalline structure, and the thermodynamic environment temperature; the formation probability corresponding to any stable crystalline structure represents the probability that the any stable crystalline structure appears in the glass structure space within the scope of statistical thermodynamics; and the formation probability corresponding to each of the stable crystalline structures is used as the content of each of the stable crystalline structures required to form the glass to be predicted.

[0114] In one embodiment, the formula for the formation probability is as follows:

[0115]

[0116] in, Indicating the components The first stable crystalline structure The formation energy corresponding to a stable crystalline structure Indicating the components The first stable crystalline structure The formation energy corresponding to a stable crystalline structure Represents Boltzmann's constant. This represents the temperature of the thermodynamic environment. Indicates the first The probability of formation corresponding to a stable crystalline structure.

[0117] In one embodiment, the performance prediction module 540 is specifically used to perform a weighted calculation of the formation probability and performance parameters corresponding to each of the stable crystalline structures to obtain the performance parameter prediction result corresponding to the glass to be predicted; the calculation formula of the performance parameter prediction result is as follows:

[0118]

[0119] in, This indicates the predicted results of the performance parameters. Indicates the first Performance parameters corresponding to a stable crystalline structure.

[0120] In one embodiment, the performance parameters include at least one of optical performance parameters and dielectric performance parameters; the optical performance parameters include a reflection spectrum parameter; and the dielectric performance parameters include an absorption spectrum parameter and a dielectric constant.

[0121] Each module in the aforementioned glass performance prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0122] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for predicting the performance of glass. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0123] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0124] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0125] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0126] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0127] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0128] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0129] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0130] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for predicting the performance of glass, characterized in that, The method includes: Obtain target glass composition information; the target glass composition information is used to indicate the composition of the glass to be predicted; In the space group, the potential crystalline structures of the constituent components are searched using particle swarm optimization algorithm and Monte Carlo method to determine all stable crystalline structures of the constituent components; The compositional relationship between the stable crystalline structure and the glass to be predicted is established based on the principles of thermodynamic statistics; the compositional relationship is used to indicate the content of each of the stable crystalline structures required to form the glass to be predicted. Based on the content of each stable crystalline structure and the performance parameters corresponding to each stable crystalline structure, the prediction results of the performance parameters corresponding to the glass to be predicted are obtained.

2. The method according to claim 1, characterized in that, The process of searching for potential crystalline structures of the constituent components using particle swarm optimization and Monte Carlo methods to determine all stable crystalline structures of the constituent components includes: The potential crystalline structures of the constituent components are searched using particle swarm optimization and Monte Carlo methods to determine the potential crystalline structures corresponding to the constituent components. Obtain the formation energy corresponding to each potential crystalline structure, and take each potential crystalline structure with a formation energy less than 0 as the stable crystalline structure of the constituent component.

3. The method according to claim 1, characterized in that, The establishment of the compositional relationship between the stable crystalline structure and the glass to be predicted based on thermodynamic statistical principles includes: The formation energy corresponding to each of the stable crystalline structures and the thermodynamic environment temperature of the thermodynamic system composed of each of the stable crystalline structures are obtained. For any stable crystalline structure, the formation probability of any stable crystalline structure is determined based on the formation energy corresponding to all the stable crystalline structures, the formation energy corresponding to any stable crystalline structure, and the thermodynamic environment temperature. The formation probability of any stable crystalline structure represents the probability that any stable crystalline structure appears in the glass structure space within the scope of statistical thermodynamics. The formation probability of each of the aforementioned stable crystalline structures is taken as the content of each of the aforementioned stable crystalline structures required to form the glass to be predicted.

4. The method according to claim 3, characterized in that, The formula for the formation probability is as follows: in, Indicating the components The first stable crystalline structure The formation energy corresponding to a stable crystalline structure Indicating the components The first stable crystalline structure The formation energy corresponding to a stable crystalline structure Represents the Boltzmann constant. This represents the temperature of the thermodynamic environment. Indicates the first The probability of formation corresponding to a stable crystalline structure.

5. The method according to claim 4, characterized in that, The process of obtaining the performance parameter prediction results for the glass to be predicted based on the content of each of the stable crystalline structures and the performance parameters corresponding to each of the stable crystalline structures includes: The formation probability and performance parameters corresponding to each of the aforementioned stable crystalline structures are weighted and calculated to obtain the predicted performance parameters of the glass to be predicted; the calculation formula for the predicted performance parameters is as follows: in, This indicates the predicted results of the performance parameters. Indicates the first Performance parameters corresponding to a stable crystalline structure.

6. The method according to claim 1, characterized in that, The performance parameters include at least one of optical performance parameters and dielectric performance parameters; the optical performance parameters include reflection spectrum parameters; the dielectric performance parameters include absorption spectrum parameters and dielectric constant.

7. A glass performance prediction device, characterized in that, The device includes: An information acquisition module is used to acquire target glass composition information; the target glass composition information is used to indicate the composition of the glass to be predicted. The structure search module is used to search for the potential crystalline structures of the constituent components in the space group using the particle swarm optimization algorithm and the Monte Carlo method, and to determine all stable crystalline structures of the constituent components. A relationship establishment module is used to establish a compositional relationship between the stable crystalline structure and the glass to be predicted based on thermodynamic statistical principles; the compositional relationship is used to indicate the content of each of the stable crystalline structures required to form the glass to be predicted; The performance prediction module is used to obtain the performance parameter prediction results of the glass to be predicted based on the content of each of the stable crystalline structures and the performance parameters corresponding to each of the stable crystalline structures.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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

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

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