Static optical refractive index quantitative prediction method and model based on crystal structure

Abstract

The invention provides a static optical refractive index quantitative prediction method and model based on a crystal structure, and the model comprises the following steps: 1, obtaining a lattice structure of a sample material, and deriving intrinsic physical parameters and key parameters of the sample material based on the crystal structure as model input data; 2, calculating prior parameters (effective valence electron number gamma mu and chemical bond proportionality coefficient F mu) of crystallography; 4, calculating element bonding parameters (total bond volume zeta, bond energy factor sigma mu, d electron correction factor D mu and Penn correction factor A mu) of the material in combination with intrinsic physical quantity, key parameters and crystallography prior parameters; 5, outputting an optical static refractive index through a formula # imgabs0 #; according to the method, the multi-chemical bond coupling effect and d-orbit electron polarization are fully considered, the influence of temperature and element doping is further considered in detail, the prediction precision is remarkably improved, and the method is suitable for optical performance optimization and high-throughput screening of multi-element oxide systems such as perovskite, pyrochlore and spinel.

Description

Technical Field

[0001] The present invention belongs to the field of dielectric optical material design and computational materials science, and in particular relates to a quantitative prediction method and prediction model for static optical refractive index based on crystal structure. Background Art

[0002] In the visible to near-infrared wavelength range, the static optical refractive index of most dielectric oxides remains constant. This key parameter is highly dependent on the type and crystallographic properties of the material and is a core element in revealing the physical nature of the static interaction between light and matter. The static optical refractive index not only provides important guidance for the design of optical components, but also promotes our in-depth understanding of the electronic structure and bonding properties of materials. With the widespread application of static optical refractive index in optoelectronics, optical communications, optical sensors, and infrared detection, its research value is becoming increasingly prominent. Therefore, the ability to accurately and effectively predict the optical static refractive index of materials under different temperature and composition conditions has become the key to quickly screening and identifying candidate materials that meet the needs of specific optical applications.

[0003] Currently, researchers have made great efforts to predict the optical static refractive index of materials using quantum computing and semi-empirical models. Quantum computing methods, such as density functional perturbation theory (DFPT), Bethe-Salpeter equation (BSE) and time-dependent density functional theory (TD-DFT), although they can provide high accuracy, are extremely computationally expensive, limiting the feasibility of rapid screening in a vast material space. Therefore, the practicality of these methods in high-throughput screening of new materials with target optical properties is greatly limited. On the other hand, semi-empirical methods such as the Moss and Ravindra models, although they have improved computational efficiency, usually ignore the electronic transitions and specific structural factors of the material (such as structural complexity and impurities), resulting in weakened accuracy and universality of the prediction. In contrast, models based on chemical bond theory achieve efficient conversion from structural parameters to optical properties by dielectrically describing chemical bonds in the crystal structure. However, these methods still face challenges in their application to complex systems. In particular, significant deviations still exist between the predictions of existing models and experimental observations when considering overcorrections in systems without d-orbital electrons, empty d-orbital electron bands, temperature effects, and composition effects. Furthermore, there is currently no mature model that can fully elucidate the relationship between the static optical refractive index and structural parameters in complex space group structures, as well as their influencing mechanisms. Therefore, developing a predictive optical static refractive index model that is widely applicable, highly accurate, and efficient, with few input parameters and a clear physical image, has become an urgent need to avoid high experimental costs and complex calculations. Summary of the Invention

[0004] In response to the above technical problems, the present invention provides a quantitative prediction method and prediction model for the static optical refractive index based on crystal structure. By combining crystal structure, intrinsic physical quantities and key parameters, the model can efficiently and accurately predict the optical static refractive index of complex oxides.

[0005] To achieve the above object, the technical solution of the present invention is:

[0006] In one aspect, the present invention provides a method for quantitatively predicting static optical refractive index based on crystal structure, comprising the following steps:

[0007] S1: Obtain the crystal structure of the sample and calculate the intrinsic physical quantities and key parameters of the sample as input data:

[0008] Key parameters: unit molecular formula volume V, bond length d of the μth anion and cation chemical bond μ , cation valence

[0009] Intrinsic physical quantity: coordination number and The number of anions closest to the cation The number of elements corresponding to cations in the unit molecular formula a μ , cation valence The number of elements corresponding to anions in the unit molecular formula b μ , the average coordination number of the chemical bond of the μth anion and cation

[0010] Calculate the stoichiometric ratio m of the binary bond subformula μ and n μ , and Then calculate the average coordination number of the μth chemical bond

[0011] Where A is a cation, B is an anion, and μ is the type of chemical bond between anions and cations in the sample;

[0012] S2: Calculate the crystallographic prior parameters of the sample: the number of effective valence electrons γ on the μth chemical bond μ , the coefficient F of the μth chemical bond ratio μ

[0013]

[0014] S3: Calculate the element bonding parameters of the sample: total chemical bond volume ζ, bond energy factor σ μ , d-orbital electron correction factor D μ and Penn correction factor A μ:

[0015] Total chemical bond volume ζ

[0016]

[0017] Bond energy factor σ μ

[0018]

[0019] Among them, the Thomas-Fermi screening factor a B =0.529×10 ；8 cm;

[0020] d-orbital electron correction factor D μ

[0021] D μ =1+Γ μ

[0022]

[0023] is the number of d orbital holes, determined according to the periodic table. For transition elements,

[0024]

[0025] If the sample has no d-orbital electrons, then Γ = 0, D = 1;

[0026] Penn Correction Factor A μ

[0027]

[0028] where α is a constant, Electron mass m e =9.1×10 ；28 g, Planck's constant

[0029] S4: Calculate the static optical refractive index:

[0030]

[0031] in, Electron mass m e =9.1×10 ；28 g, Planck's constant Electron charge e = 4.8 × 10 ；10 esu.

[0032] Preferably, it also includes a quantitative prediction of the static optical refractive index at temperature T, introducing the thermal expansion coefficient α T Corrected anion-cation chemical bond length d μ (T) and calculate the unit molecular formula volume V(T), and then repeat steps S1-S4 to calculate the static optical refractive index at temperature T.

[0033] Preferably, the thermal expansion coefficient α is calculated T Corrected anion-cation chemical bond length d μ (T) and calculate the unit molecular formula volume V(T)

[0034] d(T)=d0+Δd=d0+α T ΔT

[0035] V(T)=V0+ΔV=V0+α T ΔT

[0036] in

[0037]

[0038] Where d0 is the bond length at the initial temperature, V0 is the unit molecular volume at the initial temperature, ΔT = T-T0, T0 is the initial temperature, k is the Boltzmann constant, N AV is Avogadro's constant, the superscript Δ is the correction parameter related to the cation period in the periodic table, U μ is the lattice energy of the crystal, which can be divided into the ionic part and covalent moieties

[0039]

[0040] in is the covalency of the crystal, and The ionicity of the crystal.

[0041] Preferably, it also includes the quantitative prediction of the static optical refractive index of the sample after doping, the introduction of the doping degree x, and the calculation of the chemical bond length d of the anion and cation after doping. μ′ (x), volume V(x), repeat steps S1-S4 to calculate the static optical refractive index of the sample after doping.

[0042] Preferably, the crystal structure in step S1 is obtained by first-principles calculation or X-ray diffraction.

[0043] Based on the same inventive concept, the second aspect of the present invention provides a static optical refractive index quantitative prediction model based on crystal structure, comprising the following steps:

[0044] A1: Obtain the crystal structure of the sample and calculate the intrinsic physical quantities and key parameters of the sample as input data:

[0045] Key parameters: unit molecular formula volume V, bond length d of the μth anion and cation μ , cation valence

[0046] Intrinsic physical quantity: coordination number and The number of anions closest to the cation The number of elements corresponding to cations in the unit molecular formula a μ , cation valence The number of elements corresponding to anions in the unit molecular formula b μ , the average coordination number of the chemical bond of the μth anion and cation

[0047] Calculate the stoichiometric ratio m of the binary bond subformula μ and n μ , and Then calculate the average coordination number of the μth bond

[0048] Where A is a cation, B is an anion, and μ is the type of chemical bond between anions and cations in the sample;

[0049] A2: Calculate the crystallographic prior parameters and element bonding parameters of the sample

[0050] Crystallographic prior parameters: the effective number of valence electrons γ on any μ chemical bond μ , the coefficient F of any μ chemical bond ratio μ

[0051]

[0052] Element bonding parameters: total chemical bond volume Bond energy factor σ μ , d-orbital electron correction factor D μ , Penn correction factor A μ

[0053] Total chemical bond volume ζ

[0054]

[0055] Bond energy factor σ μ

[0056]

[0057] Among them, the Thomas-Fermi screening factor a B =0.529×10 ；8 cm;

[0058] d-orbital electron correction factor D μ

[0059] D μ =1+Γ μ

[0060]

[0061] is the number of d orbital holes, determined according to the periodic table. For transition elements, If the sample has no d-orbital electrons, then Γ = 0, D = 1;

[0062] Penn Correction Factor A μ

[0063]

[0064] Where α is a constant, Electron mass m e =9.1×10 ；28 g, Planck's constant

[0065] A3: Calculate the static optical refractive index of the sample:

[0066]

[0067] in, Electron mass m e =9.1×10 ；28 g, Planck's constant Electron charge e = 4.8 × 10 ；10 esu.

[0068] Preferably, the optical refractive index at temperature T is predicted by introducing the linear thermal expansion coefficient α T , calculate the chemical bond length d of anion and cation at different temperatures μ and unit molecular formula volume V(T), other intrinsic physical quantities and key parameters remain unchanged.

[0069] Preferably, intrinsic physical quantities and key parameters of the sample are obtained based on the crystal structure after doping, and steps A1-A3 are repeated to obtain the refractive index after doping.

[0070] Based on the same inventive concept, the third aspect of the present invention provides a dielectric optical material design system, which uses the above-mentioned crystal structure-based static optical refractive index quantitative prediction method to screen high-throughput materials, obtain materials with target refractive index, and achieve optical performance optimization.

[0071] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0072] The prediction model of the present invention has been validated by experimental data from 41 complex oxides and five doping systems, demonstrating high accuracy and broad applicability. Compared to existing models such as the Moss and Ravindra models, the model of the present invention has significant advantages in prediction accuracy.

[0073] The present invention fully considers the multi-bond coupling effect and d-orbital electron polarization, significantly improves the prediction accuracy, and is suitable for optical property optimization and high-throughput screening of multi-component oxide systems such as perovskite, pyrochlore, and spinel. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 Schematic diagram of the workflow of the method for quantitatively predicting static optical refractive index based on crystal structure of the present invention;

[0075] Figure 2 Comparison between the prediction model of the present invention and other prediction methods;

[0076] Figure 3 The crystal structure of cubic pyrochlore Gd2Zr2O7 used in the model verification of the embodiment of the present invention, wherein (a) is the crystal structure of cubic pyrochlore Gd2Zr2O7, purple: Gd, green: Zr, flesh color: O 48f , Pink: O 8b , (b) Gd6O 48f 2O 8b , (c) Gd6O 48f , (d)O 48f 2Gd2Zr, (e)O 8b 4Gd. DETAILED DESCRIPTION

[0077] The present invention establishes a prediction model for the optical static refractive index based on the Phillips, Van Vechten, and Levine (PVL) chemical bond theory. This model quantifies the effect of chemical bonds on dielectric properties and combines parameters such as the chemical bond type, bond length, and volume per unit molecular formula in the crystal structure to establish an expression for the optical static refractive index:

[0078]

[0079] Where n is the optical static refractive index, V is the volume per unit molecular formula, F μ is the proportional coefficient of the chemical bond, D μ is the d orbital electron correction factor, γ μ is the effective valence electron number, is the total chemical bond volume, A μ is the Penn correction factor, d μ is the length of the anion-cation chemical bond, σ μ is the bond energy factor,

[0080] The following is a further detailed description of a method and model for quantitatively predicting static optical refractive index based on crystal structure proposed by the present invention in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description.

[0081] 1. See Figure 1 , a method for quantitatively predicting static optical refractive index based on crystal structure, comprising the following steps:

[0082] S1: Obtain the crystal structure of the sample and calculate the intrinsic physical quantities and key parameters of the sample as input data:

[0083] Key parameters: Unit molecular formula volume V (Unit molecular formula volume is the ratio of the unit cell volume to the number of chemical formulas contained in the unit cell. For example, the smallest unit cell of NaCl is a cube, where Na + The ions are located at the vertices and face centers of the cube, and the Cl- ions are located at the edge centers and body centers of the cube, or vice versa. Each NaCl unit cell has 4 "NaCl" molecular formulas. The unit molecular formula volume of NaCl is the volume of the NaCl unit cell divided by 4). The bond length d of the μth anion-cation chemical bond μ , cation valence

[0084] Intrinsic physical quantity: coordination number and The number of anions closest to the cation The number of elements corresponding to cations in the unit molecular formula a μ , cation valence The number of elements corresponding to anions in the unit molecular formula b μ , the average coordination number of the chemical bond of the μth anion and cation

[0085] Calculate the stoichiometric ratio m of the binary bond subformula μ and n μ , and Then calculate the average coordination number of the μth chemical bond

[0086] Among them, A is the cation, B is the anion, and μ is the type of chemical bond between the anion and the cation in the sample.

[0087] The crystal structure of a sample can be obtained in two ways:

[0088] Through first-principles calculations: using density functional theory (DFT) and other calculation methods, the crystal structure of the material can be optimized to obtain its most stable lattice structure, atomic coordination and chemical bond information. For example, using software such as VASP and QuantumESPRESSO, the unit molecular formula volume V and bond length d of the crystal structure can be calculated. μ etc., as input data for our model;

[0089] Or experimental measurements: If the crystal structure has been obtained by experimental techniques such as X-ray diffraction (XRD), these experimental data can also be used directly.

[0090] S2: Based on the known parameters, calculate the crystallographic prior parameters of the sample: the number of effective valence electrons γ on the μth chemical bond μ , the coefficient F of the μth chemical bond ratio μ

[0091]

[0092] S3: Calculate the element bonding parameters of the sample based on known intrinsic physical quantities, key parameters and crystallographic prior parameters: total chemical bond volume ζ, bond energy factor σ μ , d-orbital electron correction factor D μ and Penn correction factor A μ :

[0093] Total chemical bond volume ζ

[0094]

[0095] Bond energy factor σ μ

[0096]

[0097] Among them, the Thomas-Fermi screening factor a B =0.529×10 ；8 cm;

[0098] d-orbital electron correction factor D μ

[0099] D μ =1+Γ μ

[0100]

[0101] is the number of d orbital holes, determined according to the periodic table. For transition elements,

[0102]

[0103] If the sample has no d-orbital electrons, then Γ = 0, D = 1;

[0104] Penn Correction Factor A μ

[0105]

[0106] in Electron mass m e =9.1×10 ；28 g, Planck's constant

[0107] S4: Calculate the static optical refractive index:

[0108]

[0109] in, Electron mass m e =9.1×10 ；28 g, Planck's constant Electron charge e = 4.8 × 10 ；10 esu.

[0110] 2. Considering the influence of temperature on the refractive index, the quantitative prediction of the static optical refractive index at temperature T is also included, and the thermal expansion coefficient α is introduced. T Corrected anion-cation chemical bond length d μ (T) and calculate the unit molecular formula volume V(T), and then repeat steps S1-S4 to calculate the static optical refractive index at temperature T.

[0111] Calculate the thermal expansion coefficient α T Modified bond length d μ (T) and calculate the unit molecular formula volume V(T)

[0112] d(T)=d0+Δd=d0+α T ΔT

[0113] V(T)=V0+ΔV=V0+α T ΔT

[0114] in

[0115]

[0116] Where d0 is the bond length at the initial temperature, V0 is the unit molecular volume at the initial temperature, ΔT = T-T0, T0 is the initial temperature, k is the Boltzmann constant, N AV is Avogadro's constant, the superscript Δ is the correction parameter related to the cation period in the periodic table, U μ is the lattice energy of the crystal, which can be divided into the ionic part and covalent moieties

[0117]

[0118] in is the covalency of the crystal, and The ionicity of the crystal.

[0119] Specifically, the refractive index at temperature T is:

[0120]

[0121] in, is the temperature-dependent total chemical bond volume,

[0122]

[0123] is the temperature-dependent Penn correction factor,

[0124] 3. The model also considers the effect of element doping on the optical static refractive index. The doping elements will occupy the position of the original atoms in the crystal structure, causing the crystal structure of the doped material to change. Therefore, the intrinsic physical quantities and key parameters calculated based on the crystal structure may change. Therefore, it is necessary to obtain the crystal structure of the sample after doping, calculate the intrinsic physical quantities and key parameters, and repeat steps S1-S4 to obtain the refractive index, or introduce the doping degree x. The doping degree is the molar ratio of the number of doped atoms to a certain type of atoms in the parent crystal structure. For example, when La is doped into cubic pyrochlore Gd2Zr2O7, La will occupy the Gd position in the cubic pyrochlore Gd2Zr2O7 crystal structure. The chemical formula after doping can be written as Gd 2-x La x Zr2O7;

[0125] Calculate the bond length d after doping μ′ (x), volume V(x), and chemical bond ratio F μ′(x), and then predict the refractive index after doping.

[0126] Derivation of the refractive index formula after doping:

[0127]

[0128] in,

[0129]

[0130] 4. Model Validation

[0131] In order to illustrate the model of the present invention, cubic pyrochlore Gd2Zr2O7 (such as Figure 3 As shown, the cubic symmetric structure, the space group is No.227) is used as an example. The unit cell of this crystal structure contains 8 molecular formulas, Z=8. If the lattice position where the M atom is located is taken as the coordinate origin, the +3 valent rare earth element Gd and the +4 valent metal Zr in the unit cell occupy the 16d (1 / 8, 1 / 8, 1 / 8) and 16c (0, 0, 0) symmetric positions respectively. According to the different lattice positions occupied, O atoms can be divided into O 48f and O 8b Two types. The rare earth element Gd forms a polyhedron with 8 coordinated O atoms, 6 of which are O 48f , 2 are O 8b , M and 6 coordinated O atoms form a distorted octahedron, and the remaining 6 O atoms are all O 48f ;O 48f coordinate with two Gd atoms and two Zr atoms respectively, while O 8b It is coordinated with only four Gd atoms. Since the position occupied by each atom is highly symmetrical, two parameters (the unit cell parameter a and the O' coordinate parameter x of the 48f lattice position) can determine the atomic coordinates within the unit cell.

[0132] Oxygen atoms (O) occupy two different lattice positions: O 48f Located at 48f(x,1 / 8,1 / 8), O 8b Located at 8b (3 / 8,3 / 8,3 / 8). 48f With O 8b The ratio is 6:1. 48f -Gd and O 48f The number of oxygen atoms in the -Zr bond is b μ =6. For O 8b -Gd key, b μ =1.

[0133] Based on the above analysis, cubic pyrochlore Gd2Zr2O7 can be rewritten as the following formula, in which Gd2Zr2O7 is decomposed into a series of bond types.

[0134] Gd2Zr2O7=2Gd2Zr6O 48f 1O 8b =m1Gdn1O 48f +m2Gdn2O 8b +m3Gdn3O 48f Table 1 List of intrinsic physical parameters and key parameters of the optical static refractive index prediction model of Gd2Zr2O7 (cations: Gd, Zr; anions: O)

[0135]

[0136] here It is the stoichiometric ratio of the binary bond subformula of the μ-type chemical bond. and They are the number of A atoms closest to B atoms and the coordination number of A atoms and B atoms, respectively. Input the parameters in Table 1 and get

[0137]

[0138]

[0139] Therefore, the bonding formula of cubic pyrochlore Gd2Zr2O7 is as follows:

[0140]

[0141] According to the intrinsic physical quantities and key parameter data in Table 1 and the bonding formula, the following parameters are obtained according to the model of the present invention. See Table 2 for details.

[0142] Table 2 Parameters of Gd2Zr2O7

[0143]

[0144] The cubic pyrochlore Gd2Zr2O7 is doped with La, and the chemical formula after doping is Gd 1.894；x La x Zr 1.894 O 6.629 (x=0,0.02,0.06,0.1),Gd 1.894；x La x Zr 1.894 O 6.629 The crystal structure of (x=0,0.02,0.06,0.1) is cubic Space group. There are two inequivalent O sites (O 48f and O 8b ), O48f With two equivalent Gd 3+ atom and two equivalent Zr 4+ The atoms bond to form a hybrid structure consisting of OGd2Zr2 tetrahedra with twisted edges and shared corners. 8b With four equivalent Gd 3+ The atoms bond to form a mixture of OGd4 that shares edges and corners.

[0145] The binary bonding formula after doping shows:

[0146]

[0147] The predicted refractive index of undoped and doped materials was obtained according to the refractive index formula. Then, the refractive index of cubic pyrochlore with the same doping as the predicted refractive index was measured by ellipsometry and spectroscopy. The predicted and experimental values are listed in Table 3.

[0148] Table 3Gd 1.894；x La x Zr 1.894 O 6.629 Experimental and predicted values of (x=0,0.02,0.06,0.1)

[0149]

[0150] It can be concluded from Table 3 that the experimental values are not much different from the predicted values, which is basically the same, indicating that the prediction model of this application has a high accuracy. Then, the predicted data of 41 complex oxides were compared with the predictions of quantum calculation and semi-empirical model. The results are as follows Figure 2 As shown, the prediction model of the present invention shows high accuracy and wide applicability. Compared with existing models such as Moss and Ravindra, the model of the present invention has significant advantages in prediction accuracy.

[0151] Based on chemical bond theory, this paper integrates the intrinsic physical quantities of a material, its electronic configuration, temperature, and dopant element information to develop an autonomous model for predicting the optical static refractive index. This model can accurately and efficiently predict the optical static refractive index of a material under a variety of temperature and composition conditions. It quantitatively reveals the intrinsic physical quantities that influence the static optical refractive index of a material from a physical mechanism perspective. Compared with traditional prediction models that rely on fitting parameters, the model of this paper does not require any structural fitting parameters, significantly improving prediction efficiency and applicability. For materials with known crystal structure and elemental composition, the model only requires the crystal volume and chemical bond length to efficiently and reliably predict the optical static refractive index. Experimental validation in 41 different intrinsic materials and 5 doped material systems demonstrates the model's high accuracy, reliability, and broad applicability, filling a research gap in the prediction of the optical static refractive index of complex oxide systems at finite temperatures. Compared with traditional chemical bond dielectric theory, this paper makes corrections to address d-orbital electron vacancy bands, temperature effects, and doping effects, significantly improving the model's prediction performance under various temperature and composition conditions.

[0152] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, if these changes fall within the scope of the claims of the present invention and their equivalents, they still fall within the scope of protection of the present invention.

Claims

1. A method for quantitatively predicting static optical refractive index based on crystal structure, characterized in that: The following steps are involved: S1: Obtain the crystal structure of the sample and calculate the intrinsic physical quantities and key parameters of the sample as input data: Key parameters: unit molecular formula volume V, bond length d of the μth anion and cation chemical bond μ , cation valence Intrinsic physical quantity: coordination number and The number of anions that are the nearest neighbors of a cation The number of elements corresponding to cations in the unit molecular formula a μ , the number of elements corresponding to anions in the unit molecular formula b μ , cation valence The average coordination number of the chemical bond of the μth anion and cation Calculate the stoichiometric ratio m of the binary bond subformula μ and n μ , and Then calculate the average coordination number of the μth chemical bond Where A is a cation, B is an anion, and μ is the type of chemical bond between anions and cations in the sample; S2: Calculate the crystallographic prior parameters of the sample: the number of effective valence electrons γ on the μth chemical bond μ , the coefficient F of the μth chemical bond ratio μ S3: Calculate the element bonding parameters of the sample: total chemical bond volume ζ, bond energy factor σ μ , d-orbital electron correction factor D μ and Penn correction factor A μ : Total chemical bond volume ζ Bond energy factor σ μ Among them, the Thomas-Fermi screening factor a B =0.529×10 -8 cm; d-orbital electron correction factor D μ D μ =1+C μ is the number of d orbital holes, determined according to the periodic table. For transition elements, If the sample has no d-orbital electrons, then Γ = 0, D = 1; Penn Correction Factor A μ k μ =ag μ -2 / 3 g -2 / 3 s μ 1 / 2 where α is a constant, Electron mass m e =9.1×10 -28 g, Planck's constant S4: Calculate the static optical refractive index: in, Electron charge e = 4.8 × 10 -10 esu.

2. The method for quantitatively predicting static optical refractive index based on crystal structure according to claim 1, characterized in that: It also includes the quantitative prediction of the static optical refractive index at temperature T, introducing the thermal expansion coefficient α T Modify the μth anion and cation chemical bond length d μ (T) and calculate the unit molecular formula volume V(T), and then repeat steps S1-S4 to calculate the static optical refractive index at temperature T.

3. The method for quantitatively predicting static optical refractive index based on crystal structure according to claim 2, characterized in that: Calculate the thermal expansion coefficient α T Modify the μth anion and cation chemical bond length d μ (T) and unit molecular formula volume V(T) d(T)=d0+Δd=d0+α T ·ΔT V(T)=V0+ΔV=V0+α T ·ΔT Where d0 is the chemical bond length of anion and cation at the initial temperature, V0 is the unit molecular volume at the initial temperature, ΔT = T-T0, T0 is the initial temperature, k is the Boltzmann constant, N AV is Avogadro's constant, the superscript Δ is the correction parameter related to the cation period in the periodic table, U μ is the lattice energy of the crystal, which can be divided into the ionic part and covalent moieties in is the covalency of the crystal, and The ionicity of the crystal.

4. The method for quantitatively predicting static optical refractive index based on crystal structure according to claim 1, characterized in that: It also includes the quantitative prediction of the static optical refractive index of the sample after doping, the introduction of the doping degree x, and the calculation of the chemical bond length d of the μth anion and cation after doping. μ′ (x), unit molecular formula volume V(x), repeat steps S1-S4 to calculate the static optical refractive index of the sample after doping.

5. The method for quantitatively predicting static optical refractive index based on crystal structure according to claim 1, characterized in that: The crystal structure in step S1 is obtained by first-principles calculation or X-ray diffraction.

6. A static optical refractive index quantitative prediction model based on crystal structure, characterized in that: The following steps are involved: A1: Obtain the crystal structure of the sample and calculate the intrinsic physical quantities and key parameters of the sample as input data: Key parameters: unit molecular formula volume V, bond length d of the μth anion and cation μ , cation valence Intrinsic physical quantity: coordination number and The number of anions closest to the cation The number of elements corresponding to cations in the unit molecular formula a μ , cation valence The number of elements corresponding to anions in the unit molecular formula b μ , the average coordination number of the chemical bond of the μth anion and cation Calculate the stoichiometric ratio m of the binary bond subformula μ and n μ , and Then calculate the average coordination number of the μth bond Where A is a cation, B is an anion, and μ is the type of chemical bond between anions and cations in the sample; A2: Calculate the crystallographic prior parameters and element bonding parameters of the sample Crystallographic prior parameters: the effective number of valence electrons γ on any μ chemical bond μ , the coefficient F of any μ chemical bond ratio μ Element bonding parameters: total chemical bond volume ζ, bond energy factor σ μ , d-orbital electron correction factor D μ , Penn correction factor A μ Total chemical bond volume ζ Bond energy factor σ μ Among them, the Thomas-Fermi screening factor a B =0.529×10 -8 cm; d-orbital electron correction factor D μ D μ =1+C μ is the number of d orbital holes, determined according to the periodic table. For transition elements, If the sample has no d-orbital electrons, then Γ = 0, D = 1; Penn Correction Factor A μ k μ =ag μ -2 / 3 g -2 / 3 s μ 1 / 2 where α is a constant, Electron mass m e =9.1×10 -28 g, Planck's constant A3: Calculate the static optical refractive index of the sample: in, Electron charge e = 4.8 × 10 -10 esu.

7. The static optical refractive index quantitative prediction model based on crystal structure according to claim 6, characterized in that: Predict the optical refractive index at temperature T by introducing the linear thermal expansion coefficient α T , calculate the chemical bond length d of anion and cation at different temperatures μ and unit molecular formula volume V(T), other intrinsic physical quantities and key parameters remain unchanged.

8. The static optical refractive index quantitative prediction model based on crystal structure according to claim 6, characterized in that: The intrinsic physical quantities and key parameters of the sample are obtained based on the crystal structure after doping, and steps A1-A3 are repeated to obtain the refractive index after doping.

9. A dielectric optical material design system, characterized in that: The method for quantitatively predicting static optical refractive index based on crystal structure described in claims 1-5 is used to screen high-throughput materials, obtain materials with target refractive index, and achieve optimization of optical performance.

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