A method and system for predicting carrier mobility in polymer insulating materials

Abstract

This invention discloses a method and system for predicting the carrier mobility of polymer insulating materials. The method includes the following steps: obtaining an amorphous polymer insulating molecular dynamics model; calculating the recombination energy and free energy difference parameters of each polymer molecule; calculating the charge transfer integral parameters between each pair of adjacent polymer molecules; calculating the electron and hole transfer rates between each pair of adjacent polymer molecules according to classical Marcus theory; performing a dynamic Monte Carlo simulation on the polymer insulating molecular dynamics model, and calculating the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules. This invention solves the technical problems of existing technologies, such as susceptibility to external interference, the need for precision instruments, high testing costs, and the inability to distinguish the contributions of electrons and holes to mobility.

Description

Technical Field

[0001] This invention belongs to the field of electrical equipment insulation technology, and specifically relates to a method and system for predicting the carrier mobility of polymer insulation materials. Background Technology

[0002] Polymer insulating materials are widely used as the main insulation for high-voltage power equipment such as power capacitors, power cables, and power transformers due to their excellent insulation, mechanical properties, and heat resistance. However, in recent years, with the continuous increase in power system voltage levels, the charge migration phenomenon within polymer insulating materials under strong electric fields can cause electrical aging and even electrical breakdown in the insulation of power equipment, severely shortening its service life.

[0003] Carrier mobility represents the average drift velocity of charge carriers under a unit electric field. As an intrinsic property of polymer materials, it is a key parameter affecting the charge migration process and insulation performance inside polymer insulation. Accurately obtaining the magnitude of carrier mobility in polymer insulation materials is of great value for understanding, predicting and avoiding charge migration phenomena in polymer insulation and improving the lifespan of power equipment.

[0004] In existing technologies, the main method for measuring the carrier mobility of polymer insulating materials is the time-of-flight method. This method involves applying a DC voltage to both sides of a polymer sample with a thickness of d, and measuring the time (ToF) it takes for charge carriers inside the polymer insulation to travel from one electrode to the other at a given voltage V to obtain the carrier mobility μ = d. 2 / (ToF·V). However, the order of magnitude of the carrier mobility of polymer insulators is generally less than 10. -9 m 2 The process of measuring carrier mobility by time-of-flight method is highly susceptible to interference from external conditions such as experimental instrument errors and human operation errors. In addition, the above method requires precise measuring instruments, resulting in high testing costs. Furthermore, the time-of-flight method has difficulty distinguishing the respective contributions of electrons and holes to mobility within polymer materials. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting the carrier mobility of polymer insulating materials, thereby solving one or more of the aforementioned technical problems. The technical solution provided by this invention, by comprehensively using molecular dynamics simulations and Monte Carlo simulations, can accurately predict the magnitude of carrier mobility (electron and hole mobility) within polymer insulation. This solves the technical problems of existing technologies, such as susceptibility to external interference, the need for precision instruments, high testing costs, and the inability to distinguish the contributions of electrons and holes to mobility.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This invention provides a method for predicting carrier mobility in polymer insulating materials, comprising the following steps:

[0008] Obtain a molecular dynamics model of amorphous polymer insulating materials;

[0009] Based on the obtained polymer insulating molecular dynamics model, the recombination energy and free energy difference parameters of each polymer molecule during the processes of gaining and losing electrons, gaining and losing holes were calculated.

[0010] Based on the obtained polymer insulating molecular dynamics model, the coordinates of each pair of adjacent polymer molecules are obtained, and the charge transfer integral parameters between each pair of adjacent polymer molecules are calculated.

[0011] Based on the obtained recombination energy, free energy difference parameters, and charge transfer integral parameters, the electron and hole transfer rates between each pair of adjacent polymer molecules are calculated according to the classical Marcus theory.

[0012] Under set temperature and electric field conditions, a dynamic Monte Carlo simulation was performed on the molecular dynamics model of the polymer insulation material. Based on the obtained electron and hole transfer rates between adjacent polymer molecules, the carrier mobility of the polymer insulation material was calculated.

[0013] A further improvement of the present invention is that the step of obtaining the amorphous polymer insulating molecular dynamics model of the polymer insulating material includes:

[0014] Based on the chemical formula of polymer molecules, polymer molecular models with polymerization degrees between 2 and n are established, and the edges are saturated with hydrogen. The obtained polymer molecular models are optimized based on first principles to obtain stable polymer molecular structures.

[0015] Based on the obtained stable structure of the polymer molecules, a unit cell model containing m polymer molecules is established;

[0016] Molecular dynamics simulations of more than 2 nanoseconds were performed on the established unit cell model containing m polymer molecules to obtain a stable amorphous polymer insulating molecular dynamics model.

[0017] A further improvement of the present invention is that the step of calculating the recombination energy and free energy difference parameters of each polymer molecule during the processes of gaining and losing electrons, gaining and losing holes, based on the obtained polymer insulating molecular dynamics model, includes:

[0018] Based on the obtained polymer insulating molecular dynamics model, the stable nuclear coordinates R0, R1, and R2 of each polymer molecule in neutral, positively charged, and negatively charged states are obtained. +1 R -1The system energy E of each polymer molecule in the neutral, positively charged, and negatively charged states under the neutral stable nuclear coordinate R0 was calculated. 0，0 E 0，+1 E 0，-1 In the coordinates R of a stable kernel containing a positive charge +1 The system energy E when it is in a neutral state or a positively charged state +1，0 E +1，+1 In the coordinates R of a stable kernel containing a negative charge -1 The system energy E when it is in a neutral state or a negatively charged state -1，0 E -1，-1 ;

[0019] The recombination energy and free energy difference of each polymer molecule were calculated; the formulas for calculating the recombination energy λ1 and free energy difference ΔG1 when a polymer molecule loses electrons are λ1 = E 0,-1 -E -1,-1 ΔG1=E 0,0 -E -1,-1 The formulas for calculating the recombination energy λ2 and the free energy difference ΔG2 when polymer molecules gain electrons are λ2=E -1,0 -E 0,0 ΔG2=E -1,-1 -E 0,0 The formulas for calculating the recombination energy λ3 and the free energy difference ΔG3 when a polymer molecule loses a hole are λ3 = E 0,+1 -E +1,+1 ΔG3=E 0,0 -E +1,+1 The formulas for calculating the recombination energy λ4 and the free energy difference ΔG4 when a polymer molecule gains a hole are λ4 = E +1,0 -E 0,0 ΔG4=E +1,+1 -E 0,0 .

[0020] A further improvement of the present invention is that, in the step of obtaining the coordinates of each pair of adjacent polymer molecules based on the obtained polymer insulating molecular dynamics model, and calculating the charge transfer integral parameters between each pair of adjacent polymer molecules,

[0021] When obtaining the coordinates of each pair of adjacent polymer molecules, the criterion for determining that polymer molecules are adjacent is that the distance between the centroid coordinates of the two molecules is less than a preset value.

[0022] The lattice energy correction method is used to calculate the charge transfer integral parameters between each pair of adjacent polymer molecules.

[0023] A further improvement of the present invention lies in the step of calculating the electron and hole transfer rates between each pair of adjacent polymer molecules based on the obtained recombination energy, free energy difference parameters, and charge transfer integral parameters according to classical Marcus theory.

[0024] The expression for calculating the electron transfer rate or hole transfer rate k is:

[0025]

[0026] In the formula, V is the charge transfer integral parameter; λ is the sum of the recombination energies of the two polymer molecules involved in the charge transfer; ΔG is the sum of the free energy differences of the two polymer molecules involved in the charge transfer. k is the reduced Planck constant. b is Boltzmann's constant; T is temperature.

[0027] A further improvement of the present invention is that the step of performing a dynamic Monte Carlo simulation on the polymer insulating molecular dynamics model under set temperature and electric field conditions, and calculating the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules, includes:

[0028] Based on the aforementioned polymer insulating molecular dynamics model, a polymer molecule is randomly selected as the charge transfer starting point.

[0029] Given a selected polymer molecule surrounded by j neighboring polymer molecules, the probability expression for charge transfer to the i-th neighboring polymer molecule is:

[0030]

[0031] In the formula, P i k is the probability that charge is transferred from the starting molecule to the i-th neighboring molecule around it; i k j These represent the magnitudes of charge transfer rates from the starting molecule to the i-th and j-th adjacent molecules surrounding that molecule;

[0032] Randomly generate a decimal u with values ​​in [0, 1]. If u satisfies It is assumed that the charge is eventually transferred to the i-th polymer molecule, and the simulation time is denoted as:

[0033] Repeat the above simulation process until the charge transfer distance is greater than a preset multiple of the simulation lattice length. The charge mobility under the electric field is then expressed as:

[0034]

[0035] In the formula, v is the charge velocity during the entire dynamic Monte Carlo simulation, F is the applied electric field, r is the displacement vector from the starting point to the ending point of the charge during the simulation, and t is the total simulation time.

[0036] A further improvement of the present invention is that, in the step of performing a dynamic Monte Carlo simulation on the polymer insulating molecular dynamics model under set temperature and electric field conditions, and calculating the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules,

[0037] Multiple dynamic Monte Carlo simulations were performed to calculate the average carrier mobility; the average carrier mobility was then used as the final carrier mobility of the polymer insulating material.

[0038] This invention provides a system for predicting carrier mobility in polymer insulating materials, comprising:

[0039] The model acquisition module is used to acquire amorphous polymer insulating molecular dynamics models of polymer insulating materials;

[0040] The first parameter acquisition module is used to calculate the recombination energy and free energy difference parameters of each polymer molecule during the processes of gaining and losing electrons, gaining and losing holes, based on the obtained polymer insulating molecular dynamics model.

[0041] The second parameter acquisition module is used to obtain the coordinates of each pair of adjacent polymer molecules based on the obtained polymer insulating molecular dynamics model, and to calculate the charge transfer integral parameters between each pair of adjacent polymer molecules.

[0042] The third parameter acquisition module is used to calculate the electron and hole transfer rates between each pair of adjacent polymer molecules based on the acquired recombination energy, free energy difference parameters, and charge transfer integral parameters, according to the classical Marcus theory.

[0043] The carrier mobility acquisition module is used to perform dynamic Monte Carlo simulation on the polymer insulating molecular dynamics model under set temperature and electric field conditions, and calculate the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] To address the shortcomings and deficiencies in existing technologies for obtaining carrier mobility in polymer insulating materials, this invention discloses a novel method for predicting carrier mobility in polymer insulating materials. The method first constructs a molecular dynamics model of an amorphous polymer insulating material. Then, it calculates the recombination energy, free energy difference parameters, and electronic coupling parameters between adjacent polymer molecules within the molecular dynamics model. Further, based on these parameters, it calculates the electron and hole transfer rates between adjacent polymer molecules using classical Marcus theory. Finally, it performs a dynamic Monte Carlo simulation of the entire system under a set temperature and electric field to obtain the carrier mobility of this type of polymer insulating material. The significant advantage of this method lies in the fact that many types of polymer insulating materials exist in an amorphous and non-crystalline state, resulting in a lack of definite structure. This invention, by constructing an amorphous polymer molecular dynamics model, provides a realistic bulk molecular structure of the polymer, allowing for accurate calculation of the corresponding parameters based on this bulk structure, ultimately leading to the correct carrier mobility. In summary, the advantages of the method provided by this invention include: 1) it is not affected by external interference factors such as human operation errors; 2) it is completed by computer simulation, without the need for precision experimental instruments, and the testing cost is low; 3) it can obtain the electron mobility and hole mobility of polymer insulating materials respectively. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art are briefly introduced below; obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating a method for predicting carrier mobility in polymer insulating materials according to an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of the molecular dynamics model structure of silicone rubber polymer insulation in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram illustrating the predicted electron and hole carrier mobility in silicone rubber at different temperatures, as shown in an embodiment of the present invention.

[0050] Figure 4 This is a schematic diagram of a system for predicting the carrier mobility of polymer insulating materials provided in an embodiment of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0052] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0053] The present invention will now be described in further detail with reference to the accompanying drawings:

[0054] Please see Figure 1 The present invention provides a method for predicting the carrier mobility of polymer insulating materials, comprising the following steps:

[0055] Step S1: Construct a large-scale molecular dynamics model of the polymer insulating material.

[0056] In a specific exemplary embodiment of the present invention, step S1 specifically includes:

[0057] Step S1.1: Based on the polymer molecular chemical formula, use Gauss View software to establish a polymer molecular model with a degree of polymerization between 2 and n, and saturate the edges with hydrogen; perform first-principles optimization calculations on the molecular model to obtain a stable structure; save the stable structure information of the polymer molecule as a .pdb file.

[0058] Step S1.2: Using the polymer molecular stable structure information in .pdb file format obtained in step S1.1 as the input file, use Packmol software to build a large-size unit cell model containing m polymer molecules;

[0059] Step S1.3: Use Gromacs software to perform molecular dynamics simulations for more than 2 nanoseconds (ns) on the large-size unit cell model containing m polymer molecules obtained in step S1.2, in order to obtain a stable molecular dynamics model of the amorphous polymer insulation.

[0060] Step S2: Calculate the recombination energy and free energy difference parameters of each polymer molecule in the molecular dynamics model in step S1.

[0061] In a specific exemplary embodiment of the present invention, step S2 specifically includes:

[0062] Step S2.1: Extract the structural information of n polymer molecules within the large-scale molecular dynamics model of polymer insulation. Through structural optimization calculations, obtain the stable core coordinates R0, R1, and R2 of each polymer molecule under neutral, positively charged, and negatively charged conditions. +1 R -1 Calculate the system energy E for each polymer molecule in the neutral, positively charged, and negatively charged states at the neutral stable nuclear coordinate R0. 0，0 E 0，+1 E 0，-1 In the coordinates R of a stable kernel containing a positive charge +1 The system energy E when it is in a neutral state or a positively charged state +1，0 E +1，+1 ; and in the coordinates R of a stable kernel containing a negative charge -1 The system energy E when it is in a neutral state or a negatively charged state -1，0 E -1，-1 ;

[0063] Step S2.2: Calculate the recombination energy and free energy difference of each polymer molecule;

[0064] The formulas for calculating the recombination energy λ1 and the free energy difference ΔG1 when polymer molecules lose electrons are as follows:

[0065] λ1=E 0,-1 -E -1,-1 ; ΔG1=E 0,0 -E -1,-1 ;

[0066] The formulas for calculating the recombination energy λ2 and the free energy difference ΔG2 when polymer molecules gain electrons are as follows:

[0067] λ2=E -1,0 -E 0,0 ; ΔG2=E -1,-1 -E 0,0 ;

[0068] The formulas for calculating the recombination energy λ3 and the free energy difference ΔG3 when polymer molecules lose holes are as follows:

[0069] λ3=E 0,+1 -E +1,+1 ; ΔG3=E 0,0 -E +1,+1 ;

[0070] The formulas for calculating the recombination energy λ4 and the free energy difference ΔG4 when polymer molecules acquire holes are as follows:

[0071] λ4=E +1,0 -E 0,0 ; ΔG4=E +1,+1 -E 0,0 .

[0072] Step S3: Extract the coordinates of each pair of adjacent polymer molecules from the molecular dynamics model in step S1; calculate the charge transfer integral parameter V between each pair of adjacent polymer molecules using the lattice energy correction method.

[0073] In a specific and exemplary embodiment of the present invention, the criterion for determining the adjacency of polymer molecules is that the distance between the centroid coordinates of two molecules is less than a preset value, which can be 1 nm.

[0074] Step S4: The microscopic electron and hole transfer rates between each pair of adjacent polymer molecules in step S3 are calculated based on the classical Marcus theory.

[0075] The expression for the electron (hole) transfer rate is:

[0076]

[0077] In the formula, V is the charge transfer integral obtained in step S3; λ is the sum of the recombination energies of the two molecules involved in the charge transfer obtained in step S2; ΔG is the sum of the free energy differences of the two molecules involved in the charge transfer obtained in step S2. k is the reduced Planck constant. b is Boltzmann's constant; T is temperature.

[0078] Step S5: Perform a dynamic Monte Carlo simulation on the stable molecular dynamics model of the polymer insulation obtained in step S1.3 under a set temperature and electric field to obtain the macroscopic carrier mobility of this type of polymer insulation.

[0079] In a specific exemplary embodiment of the present invention, step S5 specifically includes:

[0080] Step S5.1: Randomly select a polymer molecule from the large-scale molecular dynamics model of polymer insulation obtained in step S1 as the charge transfer starting point; assuming that there are j neighboring molecules around this molecule, based on the charge transfer rate calculation results in step S4, the probability of transferring charge to the i-th neighboring molecule is expressed as: In the formula, P i k is the probability that charge is transferred from the starting molecule to the i-th neighboring molecule around it; i k j These represent the charge transfer rates from the starting molecule to the i-th and j-th adjacent molecules around that molecule, respectively.

[0081] Step S5.2: Randomly generate a decimal u with a value in [0,1] using a computer. If u satisfies: It is assumed that the charge is eventually transferred to the i-th molecule; the simulation time is denoted as:

[0082] Step S5.3, repeat this process until the charge transfer distance is greater than a preset multiple (for example, 100 times) of the simulated lattice length, then the charge mobility under the electric field can be expressed as:

[0083]

[0084] In the formula, v is the charge velocity during the entire dynamic Monte Carlo simulation, F is the applied electric field, r is the displacement vector from the starting point to the ending point of the charge during the simulation, and t is the total simulation time.

[0085] In this embodiment of the invention, considering the influence of randomness, the final calculated macroscopic charge mobility should be the average value of the carrier mobility obtained from multiple (e.g., 2000) dynamic Monte Carlo simulations.

[0086] The exemplary application of this invention is as follows: The carrier mobility of polymer insulation is an important intrinsic property, serving as a quantitative characterization of charge migration within the polymer insulation. The magnitude of the mobility is also closely related to the insulation strength of the polymer insulation. For example, a decrease in carrier mobility typically increases the short-term breakdown field strength; while an increase in carrier mobility can suppress the accumulation of space charge within the material, thereby improving the material's long-term lifespan. Depending on the application of the insulation material, different requirements are placed on the carrier mobility of the polymer insulation. Only by correctly obtaining the magnitude of the carrier mobility can suitable polymer insulation types be selected, thereby ensuring the proper operation of electrical equipment. Furthermore, under negative voltage, charge migration within polymer insulation is primarily electron migration; under positive voltage, charge migration within polymer insulation is primarily hole migration. Only by correctly distinguishing the magnitudes of electron and hole mobility can the charge migration process within polymer insulation be reasonably described under different conditions.

[0087] In this embodiment of the invention, for silicone rubber insulating materials commonly used in power systems, the specific steps for calculating the electron and hole carrier mobilities of silicone rubber insulating materials using the method described above are as follows:

[0088] First, a large-scale molecular dynamics model containing 100 silicone rubber molecules with a degree of polymerization of 5 is constructed. A schematic diagram of the molecular dynamics model is shown below. Figure 2 As shown; each silicone rubber molecule in this model is composed of four types of atoms: hydrogen, oxygen, carbon, and silicon; calculate the recombination energy, free energy difference parameters, and electronic coupling parameters of each pair of adjacent silicone rubber molecules in the molecular dynamics model during the gain and loss of electrons (holes) of each silicone rubber molecule.

[0089] Further calculations were performed on the charge transfer rate between each pair of adjacent silicone rubber molecules within the molecular dynamics model;

[0090] Based on the above parameters, a dynamic Monte Carlo simulation was performed on the entire silicone rubber system under set temperature and electric field conditions to obtain the macroscopic carrier mobility of the silicone rubber insulation. The results are as follows: Figure 3 As shown. Figure 3 The specific magnitudes of electron mobility and hole mobility of silicone rubber materials under different temperature conditions are presented. The results show that the electron and hole mobility of silicone rubber materials increase with increasing temperature. Under the same temperature conditions, the electron mobility of silicone rubber materials is higher than that of hole mobility.

[0091] In this embodiment, all steps of the process of obtaining the carrier mobility of silicone rubber are based on a defined formula and are not affected by the user's subjectivity. Therefore, there are no interference factors such as human operation errors. From the establishment of the molecular dynamics model of amorphous silicone rubber to the calculation of parameters such as recombination energy and free energy difference of silicone rubber molecules, and finally to the calculation of the carrier mobility of silicone rubber through Monte Carlo simulation, the entire process is completed by computer simulation without the use of any precision experimental instruments. The testing cost is only a small amount of electricity and labor costs, which is extremely low. In addition, different types of carriers (electrons or holes) correspond to different formulas for calculating free energy difference and recombination energy according to their actual physical processes. Therefore, the electron mobility and hole mobility of silicone rubber can be obtained separately.

[0092] Existing techniques for measuring the carrier mobility of polymer insulating materials suffer from several drawbacks: susceptibility to external interference; the need for precision instruments, resulting in high testing costs; and the inability to distinguish between electron and hole carrier mobility. The technical solution provided in this invention, by combining molecular dynamics simulations, first-principles calculations, and Monte Carlo simulations, predicts the magnitude of carrier mobility (electron and hole mobility) in polymer insulation from a theoretical perspective. The entire process utilizes computer simulation, is unaffected by external factors such as personnel or the environment, and requires no precision instruments, resulting in low testing costs. Furthermore, this method employs different parameter calculation formulas for electrons and holes, directly yielding the corresponding carrier mobility.

[0093] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0094] Please see Figure 4 The present invention provides a system for predicting the carrier mobility of polymer insulating materials, comprising:

[0095] The model acquisition module is used to acquire amorphous polymer insulating molecular dynamics models of polymer insulating materials;

[0096] The first parameter acquisition module is used to calculate the recombination energy and free energy difference parameters of each polymer molecule during the processes of gaining and losing electrons, gaining and losing holes, based on the obtained polymer insulating molecular dynamics model.

[0097] The second parameter acquisition module is used to obtain the coordinates of each pair of adjacent polymer molecules based on the obtained polymer insulating molecular dynamics model, and to calculate the charge transfer integral parameters between each pair of adjacent polymer molecules.

[0098] The third parameter acquisition module is used to calculate the electron and hole transfer rates between each pair of adjacent polymer molecules based on the acquired recombination energy, free energy difference parameters, and charge transfer integral parameters, according to the classical Marcus theory.

[0099] The carrier mobility acquisition module is used to perform dynamic Monte Carlo simulation on the polymer insulating molecular dynamics model under set temperature and electric field conditions, and calculate the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules.

[0100] In another embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used to operate a method for predicting the carrier mobility of polymer insulating materials.

[0101] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for predicting the carrier mobility of polymer insulating materials in the above embodiments.

[0102] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0104] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0105] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the carrier mobility of polymer insulating materials, characterized in that, Includes the following steps: Obtain a molecular dynamics model of amorphous polymer insulating materials; Based on the obtained polymer insulating molecular dynamics model, the recombination energy and free energy difference parameters of each polymer molecule during the processes of gaining and losing electrons, gaining and losing holes were calculated. Based on the obtained polymer insulating molecular dynamics model, the coordinates of each pair of adjacent polymer molecules are obtained, and the charge transfer integral parameters between each pair of adjacent polymer molecules are calculated. Based on the obtained recombination energy, free energy difference parameters, and charge transfer integral parameters, the electron and hole transfer rates between each pair of adjacent polymer molecules are calculated according to the classical Marcus theory. Under set temperature and electric field conditions, a dynamic Monte Carlo simulation was performed on the molecular dynamics model of the polymer insulation material. The carrier mobility of the polymer insulation material was calculated based on the obtained electron and hole transfer rates between adjacent polymer molecules. in, The step of performing a dynamic Monte Carlo simulation of the polymer insulating molecular dynamics model under set temperature and electric field conditions, and calculating the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules, includes: Based on the aforementioned polymer insulating molecular dynamics model, a polymer molecule is randomly selected as the charge transfer starting point. The selected polymer molecules are surrounded by j The charge transfers to the adjacent polymer molecules, the first one i The probability expression for adjacent polymer molecules is: ； In the formula, For the charge transferred from the starting molecule to the molecule surrounding it, the first charge is... i The probability of a neighboring molecule; , These are the charges transferred from the starting molecule to the molecule surrounding it. i , j The magnitude of the charge transfer rate of each adjacent molecule; Randomly generate decimals with values ​​in the range [0, 1]. u ,like u satisfy Then it is assumed that the charge is eventually transferred to the first... i The simulation time is recorded for each polymer molecule as follows: ; Repeat the above simulation process until the charge transfer distance is greater than a preset multiple of the simulation lattice length. The charge mobility under the electric field is then expressed as: ； In the formula, v The charge movement rate during the entire dynamic Monte Carlo simulation. F For an external electric field, r This represents the displacement vector from the charge's starting point to its ending point during the simulation. t This represents the total simulation duration.

2. The method for predicting carrier mobility in polymer insulating materials according to claim 1, characterized in that, The steps for obtaining the amorphous polymer insulating molecular dynamics model of the polymer insulating material include: Based on the chemical formula of polymer molecules, polymer molecular models with polymerization degrees between 2 and n are established, and the edges are saturated with hydrogen. The obtained polymer molecular models are optimized based on first principles to obtain stable polymer molecular structures. Based on the obtained stable structure of the polymer molecules, a unit cell model containing m polymer molecules is established; Molecular dynamics simulations of more than 2 nanoseconds were performed on the established unit cell model containing m polymer molecules to obtain a stable amorphous polymer insulating molecular dynamics model.

3. The method for predicting carrier mobility in polymer insulating materials according to claim 1, characterized in that, The step of calculating the recombination energy and free energy difference parameters of each polymer molecule during the processes of gaining and losing electrons, gaining and losing holes, based on the obtained polymer insulating molecular dynamics model, includes: Based on the obtained polymer insulating molecular dynamics model, the stable nuclear coordinates of each polymer molecule in neutral, positively charged, and negatively charged states are obtained. R 0、 R +1 , R -1 The coordinates of each polymer molecule in the neutral stable core were calculated. R Energy of the system in neutral, positively charged, and negatively charged states at 0 E 0，0 , E 0，+1 , E 0，-1 In coordinates of a stable nuclear core containing a positive charge R +1 Energy of the system in the neutral state and the positively charged state E +1，0 , E +1，+1 In a stable nuclear coordinate system containing negative charges R -1 Energy of the system in the neutral state or the negatively charged state E -1，0 , E -1，-1 ; The recombination energy and free energy difference of each polymer molecule were calculated; where the recombination energy is the energy of a polymer molecule when it loses electrons. and free energy difference The calculation expressions are as follows: , Recombination energy when polymer molecules gain electrons and free energy difference The calculation expressions are as follows: , Recombination energy when polymer molecules lose holes and free energy difference The calculation expressions are as follows: , Recombination energy when polymer molecules gain holes and free energy difference The calculation expressions are as follows: , .

4. The method for predicting carrier mobility in polymer insulating materials according to claim 1, characterized in that, In the step of obtaining the coordinates of each pair of adjacent polymer molecules based on the obtained polymer insulating molecular dynamics model, and calculating the charge transfer integral parameters between each pair of adjacent polymer molecules, When obtaining the coordinates of each pair of adjacent polymer molecules, the criterion for determining that polymer molecules are adjacent is that the distance between the centroid coordinates of the two molecules is less than a preset value. The lattice energy correction method is used to calculate the charge transfer integral parameters between each pair of adjacent polymer molecules.

5. The method for predicting carrier mobility in polymer insulating materials according to claim 1, characterized in that, In the step of calculating the electron and hole transfer rates between each pair of adjacent polymer molecules based on the acquired recombination energy, free energy difference parameters, and charge transfer integral parameters, according to classical Marcus theory, Electron transfer rate or hole transfer rate The calculation expression is, ； In the formula, V These are the charge transfer integral parameters; It is the sum of the recombination energies of the two polymer molecules involved in charge transfer; ∠ is the sum of the free energy differences between the two polymer molecules involved in charge transfer; ℏ is the reduced Planck constant; k b is Boltzmann's constant; T is temperature.

6. The method for predicting carrier mobility in polymer insulating materials according to claim 1, characterized in that, In the step of performing a dynamic Monte Carlo simulation of the polymer insulating molecular dynamics model under set temperature and electric field conditions, and calculating the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules, the following steps are described. Multiple dynamic Monte Carlo simulations were performed to calculate the average carrier mobility; the average carrier mobility was then used as the final carrier mobility of the polymer insulating material.

7. A system for predicting carrier mobility in polymer insulating materials, characterized in that, include: The model acquisition module is used to acquire amorphous polymer insulating molecular dynamics models of polymer insulating materials; The first parameter acquisition module is used to calculate the recombination energy and free energy difference parameters of each polymer molecule during the processes of gaining and losing electrons, gaining and losing holes, based on the obtained polymer insulating molecular dynamics model. The second parameter acquisition module is used to obtain the coordinates of each pair of adjacent polymer molecules based on the obtained polymer insulating molecular dynamics model, and to calculate the charge transfer integral parameters between each pair of adjacent polymer molecules. The third parameter acquisition module is used to calculate the electron and hole transfer rates between each pair of adjacent polymer molecules based on the acquired recombination energy, free energy difference parameters, and charge transfer integral parameters, according to the classical Marcus theory. The carrier mobility acquisition module is used to perform dynamic Monte Carlo simulation on the polymer insulating molecular dynamics model under set temperature and electric field conditions, and calculate the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules. in, The carrier mobility acquisition module performs a dynamic Monte Carlo simulation of the polymer insulating molecular dynamics model under set temperature and electric field conditions, and calculates the carrier mobility of the polymer insulating material based on the obtained electron and hole transfer rates between adjacent polymer molecules. The steps include: Based on the aforementioned polymer insulating molecular dynamics model, a polymer molecule is randomly selected as the charge transfer starting point. The selected polymer molecules are surrounded by j The charge transfers to the adjacent polymer molecules, the first one i The probability expression for adjacent polymer molecules is: ； In the formula, For the charge transferred from the starting molecule to the molecule surrounding it, the first charge is... i The probability of a neighboring molecule; , These are the charges transferred from the starting molecule to the molecule surrounding it. i , j The magnitude of the charge transfer rate of each adjacent molecule; Randomly generate decimals with values ​​in the range [0, 1]. u ,like u satisfy Then it is assumed that the charge is eventually transferred to the first... i The simulation time is recorded for each polymer molecule as follows: ; Repeat the above simulation process until the charge transfer distance is greater than a preset multiple of the simulation lattice length. The charge mobility under the electric field is then expressed as: ； In the formula, v The charge movement rate during the entire dynamic Monte Carlo simulation. F For an external electric field, r This represents the displacement vector from the charge's starting point to its ending point during the simulation. t This represents the total simulation duration.

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