High-throughput simulation method for entropy-driven environmental protection and optoelectronic performance regulation of lead halide perovskite materials

Abstract

The present invention discloses a high-throughput simulation method for entropy-driven environmental protection and photoelectric performance regulation of halide lead-based perovskite materials. The method comprises: (1) calculation and classification of mixed configuration entropy, determining the range of mixed configuration entropy and the content of lead element when more than five cationic perovskite structures are formed; (2) establishment of an ideal model, constructing perovskite structures with all possible proportions of lead substitution; (3) optimization of the ideal model, screening out all structures that are more stable than the initial perovskite structure; (4) calculation and analysis of electronic structure properties, obtaining structures with excellent photoelectric performance; (5) construction of a database, forming a database of materials with stable lead substitution and excellent photoelectric performance. The present invention reveals the regulation of entropy through simulation calculation, which can be used as a feasible means to resolve the contradiction between perovskite toxicity and photoelectric performance. The required material properties can be obtained through entropy regulation, which can avoid the inefficiency of trial and error in experiments.

Description

Technical Field

[0001] The present invention belongs to the technical field of theoretical simulation of new materials, and relates to a high-throughput simulation method for entropy-driven environmental protection and photoelectric performance regulation of lead halide-based perovskite materials. Background Art

[0002] Lead halide perovskite materials are a new type of optoelectronic material developed in recent years. Although these materials have excellent optoelectronic properties, the toxicity and instability of lead have proven to be a huge challenge. Many countries around the world have established strict cadmium and lead content standards and bans, such as the EU 2015-RoHS Cadmium Ban Directive and the US Health and Safety Code: Electronic Equipment. Therefore, the environmentally friendly development of lead-based perovskite materials has become imperative.

[0003] In actual experiments, compared to the synthesis of high-entropy metals, ceramics and semiconductors, which usually require extremely high temperatures (greater than 1000°C) and will cause incompatibility with other device materials, halide perovskite materials have a soft and easily reconstructed lattice structure, making it possible to process at lower temperatures. This provides a new method for environmentally friendly lead-based perovskite materials and may bring more novel optoelectronic properties. Lu et al. introduced a variety of different types of atoms in equal proportions to form a high-entropy structure based on CsPbBr3. The results showed that not only the lead content was reduced, but also the long-range phase separation diffusion was slow, and the difficulty in substitution between elements effectively hindered the movement of atoms, which was conducive to maintaining the stability of the structure and luminescence (Mao H, Wang L, Li J, et al. High-Entropy Cs(Pb 1 / 3 Mn 1 / 3 Ni 1 / 3 )Br3 Perovskite Nanocrystals Prepared by HighEnergy Ball Milling and their Luminescence Properties[J].Particle&ParticleSystems Characterization,2022,39(9):2200073.). Shih et al.'s research shows that under high entropy alloying conditions, the lead content of MAPbBr3 can be reduced by about 55%, and the mixed configuration entropy introduced by multiple elements increases the short-range order of the perovskite lattice, and the introduced mixed configuration entropy can effectively passivate surface defects, thereby improving the stability and luminescence performance of the perovskite (Solari SF,Poon LN,Wo ··rle M, et al. Stabilization of lead-reduced metal halide perovskite nanocrystals by high-entropy alloying [J]. Journal of the American Chemical Society, 2022, 144 (13): 5864-5870.). Unfortunately, both studies clearly stated in the article that the theoretical mechanism of the regulation of hybrid configuration entropy on optoelectronic properties is unclear.

[0004] In theoretical simulation, Zhang et al. systematically studied the effect of B-site multi-element alloying on halide double perovskite (with the general structural formula A2B Ⅰ B Ⅱ X6, where A is a +1 cation, B Ⅰ and B Ⅱ Represents different types of +2-valent cations, X is a halogen ion) and its influence on thermodynamic stability and photoelectric properties, mainly proving that the configurational entropy of alloying is an important driving force for improving the thermodynamic stability of halide double perovskite at finite temperature (Wang X, Yang J, Wang X, et al. Entropy-driven stabilization of multielement halide double-perovskite alloys [J]. The Journal of Physical Chemistry Letters, 2022, 13 (22): 5017-5024.). However, there is currently a lack of systematic theoretical simulations for the study of halide lead-based perovskites, and the theoretical mechanism of the regulation of their thermodynamic stability and photoelectric properties by the mixed configurational entropy is also unclear.

[0005] Given that the research on atomic-scale high-entropy perovskites is still in its early stages. With the enrichment of element types, the complexity of material synthesis and optoelectronic properties with different ratios has greatly increased. If we only rely on the trial-and-error method, the development of high-entropy perovskites will be slow and inefficient. Therefore, simulation methods based on high-throughput entropy regulation of the environmental protection and optoelectronic properties of perovskite materials and the construction of a database are of great significance to guide the progress of existing experimental research and accelerate the development of high-entropy perovskites in the field of optoelectronics. Summary of the invention

[0006] In order to solve the problems of high cost and low efficiency of the trial-and-error method used in existing experiments, the present invention provides a high-throughput simulation method for entropy-driven environmental protection and photoelectric performance regulation of halide lead-based perovskite materials.

[0007] The technical solution of the present invention is:

[0008] A high-throughput simulation method for entropy-driven environmental protection and photoelectric performance regulation of lead halide perovskite materials, comprising the following steps:

[0009] Step 1, calculation and classification of mixed configuration entropy: construct the initial structure of ABX3 type halide lead-based perovskite material, where the A position is a +1 valent cation, the B position is four or more +2 valent cations including Pb, and the X position is a halogen ion. Determine the mixed configuration entropy and the content of lead element when forming a perovskite structure with more than five cations. Calculate the lead content of the mixed configuration according to formula (1):

[0010] C Pb =N Pb / N B-site (1)

[0011] Among them C Pb Represents the lead content, N Pb Represents the number of lead ions, N B-site represents the total number of cations at the B site;

[0012] The mixed configuration entropy of the mixed configuration is calculated according to formula (2):

[0013]

[0014] where ΔS mix represents the configurational entropy of mixing, n represents the number of anions or cations, c represents the concentration of anion or cation i, and R represents the gas constant;

[0015] And they are classified according to the size of the mixed configuration entropy;

[0016] Step 2, establishing an ideal model: selecting a +2 valent element to replace the site where Pb is located in the initial structure, using the "special quasi-random structure (SQS)" method to establish an ideal model, and establishing a perovskite structure with lead replacing all possible proportions by changing the concentration of the site replacement element, wherein the +2 valent element is selected from three or more elements of the main group metals, transition metals and rare earth metals;

[0017] Step 3, optimize the ideal model: use the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional of generalized gradient approximation to optimize the perovskite structure obtained in step 2 through atomic relaxation and electronic iteration process to achieve a relatively stable structure. After the optimization is completed, according to the "convex hull structure" principle, the convex hull energy of each structure is obtained respectively, and all structures with lower or equal convex hull energy than the initial ABX3 structure are screened out, that is, all structures that are more stable than the initial ABX3 structure are screened out;

[0018] Step 4, calculation and analysis of electronic structure properties: self-consistent calculation is performed on the structure selected in step 3 to achieve the lowest energy of the system, obtain the transition dipole moment value of the high symmetry point, select the structure whose transition dipole moment value is more than 80% of the initial structure of ABX3, calculate the projected state density and band structure of the selected structure, and obtain the band type and band gap value;

[0019] Step 5, database construction: construct a database containing the above-mentioned hybrid configuration entropy and its classification, lead element content, convex hull energy, transition dipole moment, projected state density, energy band type and band gap value.

[0020] In the present invention, the halide lead-based ABX3-type high entropy perovskite is defined as having at least five different element substitutions, but since the contribution of the anion site to the mixed configuration entropy is limited, step 1 requires the formation of more than five cationic perovskite structures, wherein the A position is a +1 valent cation, and the B position is composed of Pb and three or more other cations.

[0021] Furthermore, in step 1, A is an element such as Cs, Ba, etc., and X is Cl, Br or I. In a specific embodiment of the present invention, the ABX3 type halide lead-based perovskite material takes CsPbBr3 as an example.

[0022] Further, in step 1, the initial structure is a supercell structure of a cubic phase or a tetragonal phase of an ABX3 type lead halide perovskite material, and the specifications of the cubic phase supercell structure are 2×2×2, 3×3×3 or 4×4×4, etc. In a specific embodiment of the present invention, a 2×2×2 ABX3 type lead halide perovskite material cubic phase supercell structure is taken as an example.

[0023] Furthermore, in step 1, the classification criteria of the mixed configuration entropy are: High entropy: ΔS mix ≥1.5R, medium entropy: 1.0R≤ΔS mix ≤1.5R, low entropy: ΔS mix ≤1.0R.

[0024] Furthermore, the specific process of step 2 is as follows: select a +2 valent element to replace the site where Pb is located in the initial structure. After the element is determined, the "special quasi-random structure" method is used to establish an ideal model. The ATAT software package is used to prepare the rndstr.in input file, which is divided into three parts: unit cell, periodic unit and site. The longest distance or cutoff distance when calculating the correlation function and the number of atoms in the SQS are set, and the perovskite structure of all possible element ratios is obtained by changing the concentration of a certain element at the site.

[0025] Furthermore, the specific process of step 3 is:

[0026] Step 3-1, using the Perdew-Burke-Ernzerho exchange-correlation functional with generalized gradient approximation, the k-points are generated using the Monkhorst-Pack scheme, and the perovskite structure obtained in step 2 is optimized step by step using the VASP software package through atomic relaxation and electronic iteration processes to accelerate the acquisition of a relatively stable structure;

[0027] Step 3-2, after the structural optimization is completed, according to the "convex hull structure" principle, run the Python script of the convex hull energy function of the Pymatgen library in the working directory of different scale structures to obtain the convex hull energy of each structure, and screen out all structures with lower or equal convex hull energy than the initial ABX3 structure.

[0028] Furthermore, the specific process of step 4 is:

[0029] Step 4-1, for all structures selected in step 4 whose convex hull energy is lower than or equal to that of the initial structure of ABX3, use the VASP software package to perform self-consistent calculations to achieve the minimum energy of the system;

[0030] Step 4-2, using the function of the optical property module in Vaspkit to obtain the transition dipole moment value of the high symmetry point, and screen out the structure whose transition dipole moment value is more than 80% of the initial structure of ABX3;

[0031] Step 4-3, calculating the projected state density of the structure selected in step 4-2;

[0032] Step 4-4, calculate the band structure of the structure selected in step 4-2.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] (1) The present invention provides a high-throughput simulation method for entropy-driven environmental protection and photoelectric performance regulation of lead halide perovskite materials. The entropy regulation is revealed through simulation calculations, which can be used as a feasible means to resolve the contradiction between perovskite toxicity and photoelectric performance, and the desired material properties can be obtained through entropy regulation.

[0035] (2) Based on the complexity of the components of multinary perovskite materials, the present invention can avoid the inefficiency of trial and error in experiments through high-throughput calculations based on first principles, save experimental costs, and speed up the experimental process.

[0036] (3) The present invention adopts the SQS method as a structural search method, which can generate a well-representative atomic arrangement while maintaining the atomic component ratio, which helps to cover a variety of possible atomic interactions in the simulation. By introducing a random-like arrangement, the complex interactions between multi-element structures in high-entropy alloys can be better reflected. Compared with traditional global optimization methods, the SQS method can usually generate a high-entropy alloy model with suitable structural features in a shorter time. This high efficiency enables researchers to perform large-scale calculations and simulations more quickly, thereby exploring the alloy space more extensively.

[0037] (4) The high-throughput database constructed by the present invention can provide a theoretical basis for the experimental synthesis and design of high-entropy perovskites, accurately provide a microscopic theoretical mechanism for complex optoelectronic properties, and accelerate the development of high-entropy perovskites in the field of optoelectronics. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the high-throughput simulation method for the entropy-driven environmental protection and photoelectric performance regulation of halide lead-based perovskite materials of the present invention.

[0039] Figure 2 This is the ideal model structure diagram constructed in the embodiment, where Figure (a) is Cs8Pb8Br 24 Structure, Figure (b) is Cs8Pb2Sn2Sr2Ge2Br 24 structure.

[0040] Figure 3 It is the convex hull energy diagram after all structures in the embodiment are optimized.

[0041] Figure 4 It is a numerical percentage diagram of the transition dipole moments of the four structures screened out in the embodiment relative to the initial CsPbBr3 structure.

[0042] Figure 5 The projected state density comparison diagrams of the elements with more stable structures than the original CsPbBr3 selected in the embodiment, wherein (a), (b), (c) and (d) are respectively Cs8Pb8Br 24 、Cs8Pb3Sn1Sr3Ge1Br 24 、Cs8Pb2Sn1Sr4Ge1Br 24 and Cs8Pb1Sn1Sr5Ge1Br 24 Elemental projected density of states of the structure.

[0043] Figure 6 The energy band comparison diagrams of the stable structures of CsPbBr3 screened out in the embodiment are shown in Figures (a), (b), (c) and (d) respectively. 24、Cs8Pb3Sn1Sr3Ge1Br 24 、Cs8Pb2Sn1Sr4Ge1Br 24 and Cs8Pb1Sn1Sr5Ge1Br 24 Energy band diagram of the structure. DETAILED DESCRIPTION

[0044] The simulation method of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiment is only one of the embodiments of the present invention and can be extended to many embodiments. All other embodiments based on the idea of ​​the present invention should be protected by the present invention.

[0045] Example

[0046] Taking five main group cation perovskite structures as examples, combined with Figure 1 The high-throughput simulation method for entropy-driven environmental protection and photoelectric performance regulation of lead halide perovskite materials of the present invention comprises the following steps:

[0047] Step 1, calculation and classification of mixed configuration entropy: construct the initial structure of ABX3-type lead halide-based perovskite material, determine the mixed configuration entropy and the content of lead element when more than five cationic perovskite structures are formed, and classify them according to the size of the mixed configuration entropy;

[0048] Step 2, establishing an ideal model: selecting a +2 valent element to replace the site where Pb is located in the initial structure, using the "special quasi-random structure (SQS)" method to establish an ideal model, and establishing a perovskite structure with lead replacing all possible proportions by changing the concentration of the site replacement element, wherein the +2 valent element is selected from three or more elements of the main group metals, transition metals and rare earth metals;

[0049] Step 3, optimize the ideal model: use the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional of generalized gradient approximation to optimize the perovskite structure obtained in step 2 through atomic relaxation and electronic iteration process to achieve a relatively stable structure. After the optimization is completed, according to the "convex hull structure" principle, the convex hull energy of each structure is obtained respectively, and all structures with lower or equal convex hull energy than the initial ABX3 structure are screened out, that is, all structures that are more stable than the initial ABX3 structure are screened out;

[0050] Step 4, calculation and analysis of electronic structure properties: self-consistent calculation is performed on the structure selected in step 3 to achieve the lowest energy of the system, obtain the transition dipole moment value of the high symmetry point, select the structure whose transition dipole moment value is more than 80% of the initial structure of ABX3, calculate the projected state density and band structure of the selected structure, and obtain the band type and band gap value;

[0051] Step 5, database construction: construct a database containing the above-mentioned hybrid configuration entropy and its classification, lead element content, convex hull energy, transition dipole moment, projected state density, energy band type and band gap value.

[0052] Further, in step 1, the 2×2×2CsPbBr3 cubic phase is used as the initial structure to determine the range of the mixed configuration entropy when the pentacation perovskite structure is formed. The specific process includes:

[0053] Step 1-1, using 2×2×2CsPbBr3 cubic phase as the initial structure, CsPbBr3 type high entropy perovskite is defined as having at least five different element substitutions, so it is required to form a five-cation structure, where the A position is Cs, the B position is Pb (B1) and the other three cations (B2, B3 and B4);

[0054] Step 1-2, after determining the number of cations at the B position, calculate the lead content of each configuration according to formula (1), and calculate the mixed configuration entropy of each configuration according to formula (2):

[0055] C Pb =N Pb / N B-site (1)

[0056] Among them C Pb Represents the lead content, N Pb Represents the number of lead ions, N B-site represents the total number of B-site ions;

[0057]

[0058] Where ΔSmix represents the mixing configuration entropy, n represents the number of anions or cations, c represents the concentration of anion or cation i, and R represents the gas constant;

[0059] And according to the size of the mixed configuration entropy, the classification standard is: High entropy: ΔS mix ≥1.5R, medium entropy: 1.0R≤ΔS mix ≤1.5R, low entropy: ΔS mix ≤1.0R.

[0060] Illustratively, the calculated mixing configuration entropies and lead contents at all ratios of the four elements at the B position are shown in Table 1 below.

[0061] Table 1 Calculated mixing configuration entropy, lead content and classification

[0062]

[0063]

[0064] It can be seen from Table 1 that the range of the mixing configuration entropy when forming the pentacation perovskite structure is between 1.16R and 1.23R, which is 20%-30% higher than the 0.95R mixing configuration entropy of the original CsPbBr3 structure, and the lowest lead content is only 12.5%.

[0065] Furthermore, in step 2, +2 valent ions are selected to replace lead according to the main group metals to establish a perovskite structure in which lead replaces all possible proportions. The specific process includes:

[0066] Step 2-1, based on the similarity between the main group metal ions and the +2-valent Pb ion radius (119 pm), the +2-valent Sr ion (118 pm) with a similar ion radius was selected; based on the similarity between the main group metal ions and the +2-valent Pb ion electronic structure, the +2-valent Sn and Ge ions of the same group were selected to replace the Pb site in the initial structure;

[0067] Step 2-2, after determining the elements, use the "special quasi-random structure (SQS)" method to establish an ideal model, use the ATAT software package, prepare the rndstr.in input file, which is divided into three parts: unit cell, periodic unit and site. By changing the concentration of a certain element at the site, the perovskite structure of all possible element ratios can be obtained;

[0068] Step 2-3, based on step 2-2, two commands in the ATAT package are required to generate a series of quasi-random structures: corrdump and mcsqs. The corrdump command defines the longest distance or cutoff distance when calculating the correlation function. We set this parameter to -2 and 1.1. The specific command of corrdump is corrdump-l=rndstr.in-ro-noe-nop-clus-2=1.1. The mcsqs command must appropriately determine the number of atoms in SQS. Here we study the cubic supercell of 2×2×2ABX3, so it is set to 40. The specific command of mcsqs is mcsqs-n 40. Here are two quasi-random structures generated based on main group elements, combined with Figure 2 Middle figure (a), initial Cs8Pb8Br 24 structure; combination Figure 2 Middle picture (b), Cs8Pb2Sn2Sr2Ge2Br 24 structure.

[0069] Furthermore, in step 3, the structure is optimized, and then the convex hull energy of the structure is calculated to screen out all structures that are more stable than the original CsPbBr3. The specific process includes:

[0070] Step 3-1, using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional with generalized gradient approximation, the k-points were generated using the Monkhorst-Pack scheme, and the VASP software package was used for step-by-step optimization to accelerate the acquisition of a reasonable structure; in the first step, a slightly lower precision parameter was used to quickly obtain an initial optimized structure, and in the second step, a higher precision parameter was used for optimization again until a structure with the set accuracy was obtained; the plane wave cutoff energy for the first step optimization was 350 eV, the k-point density was 0.05, and the convergence criterion was to achieve a force on each atom less than Energy less than 10 -3 eV; the plane wave cutoff energy of the second step optimization is 450eV, the k-point density is 0.03, and the convergence criterion is to achieve that the force on each atom is less than Energy less than 10 -5 eV;

[0071] Step 3-2, after the structure optimization is completed, according to the principle of "convex hull structure", the Python script of the convex hull energy function of the Pymatgen library is run in the working directory of different scale structures to obtain the convex hull energy of each structure, and all structures with lower convex hull energy than the cubic supercell convex hull of 2×2×2CsPbBr3 are screened out. The convex hull energy distribution of all structures with different scales is shown in the figure below. Figure 3 As shown, it was found that the convex hull energies of the four structures were less than or equal to the initial CsPbBr3 structure;

[0072] Furthermore, the specific process of calculating the electronic structure properties in step 4 includes:

[0073] Step 4-1: For the structures selected in step 3-2, continue to use the VASP software package to perform self-consistent calculations to achieve the lowest energy of the system. The self-consistent energy convergence standard is increased to 10 -7 eV;

[0074] Step 4-2, after the self-consistent calculation is completed, the function of the optical property module in Vaspkit is used to obtain the transition dipole moment value of the high symmetry point, and the structure with the transition dipole moment value of more than 80% of the initial ABX3 structure is screened out. The percentage of the transition dipole moment of the four structures screened out in step 3-2 relative to the initial CsPbBr3 structure is as follows Figure 4 As shown, two of them are greater than or equal to the value of the transition dipole moment of the initial CsPbBr3 structure, and one also reaches 80% of the value of the transition dipole moment of the initial CsPbBr3 structure; so far, three structures with excellent photoelectric properties have been screened;

[0075] Step 4-3, the projected state density calculation is also performed after the self-consistent calculation is completed. The k-point density is 0.02. In INCAR, ISMEAR = -5 is set, LORBIT = 11 and NEDOS = 3000 are added. The projected state density of the three structures selected in step 4-2 and the initial CsPbBr3 structure is as follows: Figure 5 As shown, it is found that Sr has no contribution to the band edge, while Sn and Ge contribute to both the conduction band and the valence band, and can play a role in regulating the optoelectronic properties;

[0076] Step 4-4, the band structure calculation is performed based on the charge density obtained by self-consistent calculation, involving the ISTART and ICHARG parameters in the INCAR file, which are set to 1 and 11 respectively; the high symmetry points of the material need to be written in the KPOINTS input file; the band diagrams of the three structures selected in step 4-2 and the original CsPbBr3 structure are shown in Figure 6 As shown, it was found that the conduction band energy dispersion of the three structures was similar to that of the initial CsPbBr3 structure, but the valence band energy dispersion changed significantly, which is crucial for understanding the entropy regulation of the optoelectronic properties of perovskite materials.

[0077] Further, in step 5, a database of main group metal element lead substitution with stable performance and excellent photoelectric performance is formed, and the specific process includes:

[0078] The database is constructed based on the main group metal elements. The parameters of the database include the hybrid configuration entropy (ΔS mix ) and its classification, lead content, convex hull energy (E hull ), band gap size (E g ), projected state density and transition dipole moment (TDM), providing reliable reference data for experimental work.

[0079] Exemplarily, the PBE database parameters of the three main group metal elements Sr, Sn and Ge that are selected to have stable lead substitution and excellent photoelectric performance are shown in Table 2 below.

[0080] Table 2 PBE database parameters for three main group metal elements Sr, Sn and Ge that are stable and have excellent photoelectric properties

[0081]

[0082] In summary, the present invention can avoid the inefficiency of trial and error in experiments and save experimental costs by entropy-regulating the environmental protection of perovskite materials and the first-principles high-throughput calculation of optoelectronic properties; it proves that entropy regulation can be used as a solution to the contradiction between perovskite toxicity and optoelectronic properties, and can obtain the required material properties. The construction of the database provides a theoretical basis for the experimental synthesis and design of high-entropy perovskites, provides a microscopic theoretical mechanism for the precise complex optoelectronic properties, and accelerates the development of high-entropy perovskites in the field of optoelectronics.

Claims

1. A high-throughput simulation method for entropy-driven environmental protection and photoelectric performance regulation of lead halide perovskite materials, characterized in that: The following steps are involved: Step 1, calculation and classification of mixed configuration entropy: construct the initial structure of ABX3 type halide lead-based perovskite material, where the A position is a +1 valent cation, the B position is four or more +2 valent cations including Pb, and the X position is a halogen ion. Determine the mixed configuration entropy and the content of lead element when forming a perovskite structure with more than five cations. Calculate the lead content of the mixed configuration according to formula (1): C Pb =N Pb / N B-site (1), Among them C Pb Represents the lead content, N Pb Represents the number of lead ions, N B-site represents the total number of cations at the B site; The mixed configuration entropy of the mixed configuration is calculated according to formula (2): （2）， Where ΔS mix represents the configurational entropy of mixing, n represents the number of anions or cations, c represents the concentration of anion or cation i, and R represents the gas constant; And they are classified according to the size of the mixed configuration entropy; Step 2, establishing an ideal model: selecting a +2 valent element to replace the site where Pb is located in the initial structure, using the "special quasi-random structure" method to establish an ideal model, and by changing the concentration of the site replacement element, establishing a perovskite structure with lead replacing all possible proportions, wherein the +2 valent element is selected from at least three elements of the main group metals, transition metals and rare earth metals; Step 3, optimize the ideal model: use the Perdew-Burke-Ernzerhof exchange-correlation functional of the generalized gradient approximation to optimize the perovskite structure obtained in step 2 through atomic relaxation and electronic iteration process to achieve a relatively stable structure. After the optimization is completed, according to the "convex hull structure" principle, the convex hull energy of each structure is obtained respectively, and all structures with lower or equal convex hull energy than the initial ABX3 structure are screened out, that is, all structures that are more stable than the initial ABX3 structure are screened out; Step 4, calculation and analysis of electronic structure properties: self-consistent calculation is performed on the structure selected in step 3 to achieve the lowest energy of the system, obtain the transition dipole moment value of the high symmetry point, select the structure whose transition dipole moment value is more than 80% of the initial structure of ABX3, calculate the projected state density and band structure of the selected structure, and obtain the band type and band gap value; Step 5, database construction: construct a database containing the above-mentioned hybrid configuration entropy and its classification, lead element content, convex hull energy, transition dipole moment, projected state density, energy band type and band gap value.

2. The high-throughput simulation method according to claim 1, characterized in that: In step 1, A is Cs or Ba, and X is Cl, Br or I.

3. The high-throughput simulation method according to claim 1, characterized in that: In step 1, the ABX3 type halide lead-based perovskite material is CsPbBr3.

4. The high-throughput simulation method according to claim 1, characterized in that: In step 1, the initial structure is a supercell structure of a cubic phase or a tetragonal phase of an ABX3 type lead halide-based perovskite material.

5. The high-throughput simulation method according to claim 4, characterized in that: The specifications of the cubic phase supercell structure are 2×2×2, 3×3×3 or 4×4×4.

6. The high-throughput simulation method according to claim 1, characterized in that: In step 1, the classification criteria for the entropy of the mixed configuration are: High entropy: ΔS mix ≥1.5R, medium entropy: 1.0R≤ΔS mix ≤1.5R, low entropy: ΔS mix ≤1.0R.

7. The high-throughput simulation method according to claim 1, characterized in that: The specific process of step 2 is: select a +2 valent element to replace the site where Pb is located in the initial structure. After determining the element, use the "special quasi-random structure" method to establish an ideal model. Use the ATAT software package to prepare the rndstr.in input file, which is divided into three parts: unit cell, periodic unit, and site. Set the longest distance or cutoff distance when calculating the correlation function and the number of atoms in the "special quasi-random structure". By changing the concentration of a certain element at the site, the perovskite structure of all possible element ratios can be obtained.

8. The high-throughput simulation method according to claim 1, characterized in that: The specific process of step 3 is: Step 3-1, using the Perdew-Burke-Ernzerho exchange-correlation functional with generalized gradient approximation, the k-points are generated using the Monkhorst-Pack scheme, and the perovskite structure obtained in step 2 is optimized step by step using the VASP software package through atomic relaxation and electronic iteration processes to accelerate the acquisition of a relatively stable structure; Step 3-2, after the structural optimization is completed, according to the "convex hull structure" principle, run the Python script of the convex hull energy function of the Pymatgen library in the working directory of different scale structures to obtain the convex hull energy of each structure, and screen out all structures with lower or equal convex hull energy than the initial ABX3 structure.

9. The high-throughput simulation method according to claim 1, characterized in that: The specific process of step 4 is: Step 4-1, for all structures selected in step 4 whose convex hull energy is lower or equal to that of the initial structure of ABX3, a self-consistent calculation is performed using the VASP software package to achieve the minimum energy of the system; Step 4-2, using the function of the optical property module in Vaspkit, obtain the transition dipole moment value of the high symmetry point, and screen out the structure whose transition dipole moment value is more than 80% of the initial structure of ABX3; Step 4-3, calculating the projected state density of the structure selected in step 4-2; Step 4-4, calculate the band structure of the structure selected in step 4-2.

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