Simulation calculation method for electron blocking capability of complex compound

Abstract

The invention provides a simulation calculation method for the electron blocking capability of a complex compound, which comprises the following steps: S1, according to the molecular composition of a compound target material, simulating and constructing a microscopic all-atomic molecular structure of the target material by using a computer; s2, randomly generating more than ten thousands of ion movement tracks, and counting a distribution function group of closest distances between the ions and various atoms in the target material when the ions pass through the target material; s3, 10-100 short tracks capable of enabling the comprehensive overlap index to reach 95% or above are screened out from the ion movement tracks; and S4, simulating the process that the ions pass through the target material along the selected trajectories by using a time-dependent density functional method, wherein the ratio of the sum of energy changes of each simulation system to the sum of the lengths of the ion trajectories is the electron blocking capability of the compound on the ions. According to the scheme provided by the invention, the electron blocking capability of a complex compound on ions can be quickly and accurately calculated, and complex and high-cost experimental measurement is avoided. Wherein the calculation cost is reduced to 10% of the calculation cost of the random incidence method through the screening track method.

Description

Technical Field

[0001] The invention relates to the field of ionizing radiation calculation simulation of materials, and in particular to a simulation calculation method for the electron stopping power of complex compounds. Background Art

[0002] Complex compounds refer to polyatomic compounds, such as water and biomolecules, that have more atoms and multiple bonding and intermolecular interactions (such as hydrogen bonds) than chemical elements. Complex compounds usually have complex molecular configurations and amorphous disordered molecular arrangements. When irradiating complex compounds, flying ions continuously transfer energy to the electrons and nuclei of the compound by colliding with the electrons and nuclei in the complex compound. The total energy transferred per unit length of the ion trajectory is called the stopping power of the compound for the incident ions. The stopping power caused by the interaction with electrons and nuclei is called the electron stopping power and the nuclear stopping power, respectively. Among them, in the Bragg peak region where the radiation effect is significant, the electron stopping power is the main component. The electron stopping power is crucial for most ion irradiation-related applications, including but not limited to radiation tumor treatment, radiation material modification, next-generation power technology, and the damage and destructive effects of space radiation on astronauts, spacecraft components, and onboard equipment.

[0003] At present, the mainstream calculation methods of electron stopping power in the field of ionizing radiation dose, such as SRIM software, mainly start from simplified theoretical approximation, based on the existing single-element electron stopping power data, use empirical rules such as linear combination, supplemented by empirical correction terms, and use adjustable fitting parameters to strive to match the limited stopping power experimental data. On the one hand, due to the high experimental cost, the measurement data of electron stopping power for a variety of complex target materials and ion combinations are limited; on the other hand, the physical meaning and application range of the combination parameters and adjustable parameters in the empirical model are still controversial.

[0004] In recent years, with the development of various ab initio simulation techniques, real-time time-dependent density functional theory (rt-TDDFT) has been successfully applied to simulate electron excitation and ionization in different energy ranges. rt-TDDFT is expected to become an effective means to accurately calculate the electron stopping power of materials. However, in order to obtain the ensemble average of the electron stopping power with observational and dose calculation significance, it is necessary to ensure that the ions in the simulation fully and correctly traverse the electronic structure of the target material, and the ions need to experience a micron-level penetration trajectory. Considering the computational cost of rt-TDDDFT, to obtain the stopping power curve of the electron stopping power of a certain target material for a certain ion in the Bragg peak region, the required computing resources are only available in very few super laboratories. The huge demand for computing resources is the bottleneck that hinders the widespread application of rt-TDDFT in accurately calculating the electron stopping power of compound materials for ions. Therefore, seeking a general, high-precision, low-cost, and observationally meaningful calculation method for the electron stopping power of complex compound systems for ions has become one of the focuses of attention in related theoretical and application fields. Summary of the invention

[0005] In view of the prior art and existing problems, the present invention provides a simulation calculation method for the electron stopping power of complex compounds.

[0006] The present invention adopts the following technical scheme: A simulation calculation method for the electronic stopping power of a complex compound comprises the following steps: S1: According to the molecular composition of the compound target, the microscopic all-atom molecular structure of the target is constructed by computer simulation to obtain a simulated sample of the equilibrium state of the compound target; S2: randomly generate more than 10,000 nanoscale ion motion trajectories that pass through the compound target equilibrium state simulation sample described in S1, and record and count the target distribution function group of the closest distance to various atoms in the target during the ion crossing process; S3: 10-100 short trajectories are selected from the nanoscale ion motion trajectories described in S2, and the comprehensive overlap index of the closest distance distribution function corresponding to the short trajectories and the target distribution function group in S2 reaches more than 95%; S4: Use the time-dependent density functional method to simulate the process of ions passing through the compound target along the short trajectories selected in S3, record the energy changes of the system, and calculate and obtain the electron stopping power of the target for ions.

[0007] Preferably, the molecular composition of the compound target material in step S1 includes: component molecular formula, chemical bond information, macroscopic density, representative sample size parameters, etc.

[0008] Preferably, the method for constructing the microscopic all-atomic molecular structure of the target material described in step S1 is: first construct an orthogonal simulation unit of a compound molecule with a single-side size of 20 nanometers, calculate the number of target molecules required to fill the simulation unit according to the simulation unit volume, target density and molecular weight, randomly place a corresponding number of molecules in the simulation unit, and apply periodic boundary conditions to the simulation unit to simulate the macroscopic compound target material; use classical or ab initio molecular dynamics methods to geometrically optimize and balance the material to obtain a simulated sample of the equilibrium state of the compound target material.

[0009] The method for generating the nanoscale ion motion trajectory in step S2 is as follows: a large number of nanoscale trajectories are generated in random directions in the simulation unit (so that the total length of the trajectory reaches the macroscopic scale) as candidate samples, and the closest distances between the ions and the target component atoms at different positions on the sample trajectory are calculated, and the 2 nanoscale trajectories at the beginning and end of the trajectory are discarded. There is a collision parameter less than 0.8 in the range of samples to exclude computational uncertainties caused by sharp collisions at the beginning and end of a single trajectory, to eliminate the effects of mirror excitations due to periodic boundary conditions, and to eliminate the effects of initial transients in subsequent simulations.

[0010] The method for statistically analyzing the target distribution function group in step S2 is as follows: discretize each straight line trajectory of the ion crossing the target material along a random direction into recording points with a spacing of 0.1 nm, and calculate the different component elements of the ion and the target material molecule at each position point. The closest distance ,by Plot for the independent variable The histogram of the cumulative number of occurrences is normalized to obtain the target distribution function group of the closest distance between the ion and various atoms of the target molecule. .

[0011] The calculation method of the comprehensive distribution overlap index in S3 is as follows: based on the statistical results of the target distribution function group described in S2, firstly, the distribution function corresponding to each selected short trajectory and each element in the target material is calculated respectively. With the target distribution function group The overlap index : ; in, is the distribution function of the closest distance between the ion and the X atom corresponding to each screened short trajectory, is the target distribution function group of the closest distance between ion and atom X.

[0012] Secondly, the total number of electrons of each element X in the compound , that is, the number of atoms Multiply by the number of electrons in a single atom , is the weight, calculate the comprehensive overlap index : .

[0013] Therefore, for complex compounds containing multiple important component atoms, the number of electrons of each atom is used to characterize their importance to trajectory sampling, and the realization weight of the distribution function is assigned, so as to obtain the ion collision probability optimized for the compound as a whole, so as to improve the statistical correctness of the selected ion trajectory for the simulation calculation of the electron stopping power of the compound. The trajectory screening method using this comprehensive overlap index as a quantitative standard can achieve the optimal and correct sampling of multi-element collision distribution.

[0014] The selection method of short trajectories in step S3 includes: any algorithm or mathematical programming method that can achieve the optimization goal of maximizing the comprehensive overlap index or reaching more than 95% (close to 100%), such as dynamic programming, greedy algorithm, and various types of heuristic optimization algorithms. The core principle of the screening algorithm should be: as the number of selected trajectories increases, the matching degree of the selected trajectory combination and the distribution function of the closest distance to the target (quantified by the comprehensive overlap index) keeps increasing, and the currently selected trajectory is the one that contributes the most to the overlap index among all candidate samples.

[0015] Preferably, the time-dependent density functional method described in step S4 includes: using the real-time time-dependent density functional (rt-TDDFT) method, combining the target equilibrium model described in S1 and the selected ion trajectory combination described in S3, assuming that the ion has a certain incident velocity , select the all-electron basis set and the generalized gradient (GGA) exchange-correlation function, and select the reversible evolution algorithm to perform the microcanonical ensemble evolution simulation of the systematic all-electron time-dependent density functional.

[0016] Preferably, the method for analyzing the electron stopping power of the target material for the ion in step S4 comprises: obtaining a length of The selected trajectory Increased energy of the ion-target system , that is, the work done by the external force required to enable the flying ions to overcome the electron blocking and maintain their kinetic energy, and to move the compound target to a speed of The electron stopping power of the ion Denoted as: All selected short trajectories correspond to The arithmetic mean of the target material is the electron stopping power of the ions. The simulation results can be considered to be in effective correspondence with the experimental observations.

[0017] The combination of the constraint of constant ion flight speed and the microcanonical ensemble in the simulation system can solve the errors caused by the change of speed and energy independent variables due to the decrease of ion speed in general unconstrained ion simulations, and also cleverly convert the ion energy loss into an increase in system energy, facilitating calculations.

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention provides a simulation calculation method for the electron stopping power of complex compound materials, and proposes a fast-converging ab initio calculation scheme for the electron stopping power of complex target materials to ions by screening short trajectories. Compared with the current mainstream empirical calculation methods for the electron stopping power of complex materials in the industry, such as SRIM, the present invention avoids the reliance on the fitting parameters with unclear meanings in the calculation of empirical methods, and provides an ab initio calculation scheme for the electron stopping power of electron dynamics simulation that is independent of experiments and experience.

[0019] The present invention provides a representative ion trajectory sampling and screening method that promotes efficient convergence of ab initio calculation of electron stopping power. Based on the molecular structure geometry information of the target material, the ion trajectory is pre-sampled and screened before simulation, which greatly reduces the computational cost required to accurately calculate the stopping power of material electrons using the time-dependent density functional theory (TDDFT) method.

[0020] The present invention provides a method for evaluating the effectiveness of sample trajectories for calculating the electron stopping power of complex compound materials. The method uses the total number of electrons in each atom in the compound as a weight, calculates the comprehensive overlap index between the distance distribution of ions colliding with target atoms along the trajectory and the macroscopic statistical distribution, and screens out the sample trajectory set with the fastest comprehensive overlap index. The method provides a short trajectory screening combination required for the electron stopping power calculation of complex disordered compounds (such as organic biomaterials), and ensures that the electron stopping power calculation converges accurately to the statistical average and observed values. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for simulating and calculating the electron stopping power of a complex compound provided by the present invention; Figure 2 A microstructure diagram of a target material constructed by simulation provided by an embodiment of the present invention; Figure 3 A flow chart of an ion trajectory screening algorithm provided by an embodiment of the present invention; Figure 4 A trajectory distribution diagram of the screening trajectory combination results provided by an embodiment of the present invention; Figure 5 A group diagram of distribution functions of the closest distances between ions and target atoms of the screening results provided by an embodiment of the present invention; Figure 6The overlap index convergence diagram of the closest distance distribution of each element and the target distribution provided by the embodiment of the present invention; Figure 7 This is a graph showing the calculation results of the electron stopping power curve of the target material provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below. The specific embodiments are only used to explain the present invention and are not used to limit the present invention.

[0023] See also Figure 1 , is a flow chart of a method for simulating and calculating the electron stopping power of a complex compound provided by an embodiment of the present invention, comprising the following steps: S1: According to the molecular composition of the compound target material, the microscopic all-atom molecular structure of the target material is constructed by computer simulation to obtain a simulated sample of the compound target material in equilibrium state; S2: randomly generate more than 10,000 nanoscale ion motion trajectories that pass through the compound target equilibrium state simulation sample described in S1, and record and count the target distribution function group of the closest distances to various atoms in the target during the ion crossing process; S3: 10-100 short trajectories are selected from the nanoscale ion motion trajectories described in S2, and the comprehensive overlap index of the shortest distance distribution function corresponding to the short trajectories and the target distribution function group in S2 reaches more than 95%; S4: Use the time-dependent density functional method to simulate the process of ions passing through the compound target along the short trajectories selected in S3, record the energy changes of the system, and calculate and obtain the electron stopping power of the target for ions.

[0024] This embodiment takes an amorphous glycine target as an example to calculate its electron stopping power for protons and alpha particles in the Bragg peak region.

[0025] Specifically, the molecular composition of the compound target material described in step S1 includes: component molecular formula, chemical bond information, macroscopic density, representative sample size parameters, etc. What is required in this embodiment is the glycine molecular configuration parameters, density and side length of the rectangular parallelepiped simulation unit.

[0026] The method for simulating and constructing the microscopic all-atomic molecular structure of the target material described in step S1 includes: first constructing a cubic simulation unit under the Cartesian coordinate system xyz, and applying periodic boundary conditions to determine the number of molecules contained in the simulation unit according to its density.

[0027] Specifically, this embodiment sets 24×12.6×12.6 According to the molecular weight and density of glycine, it is calculated that the simulation unit should include 36 molecules. The molecules that meet the initial molecular configuration of the target compound, including the atomic species and the determined number of relative coordinates, are randomly arranged in the simulation unit to ensure that the atoms do not overlap. In this way, an orthogonal simulation unit of a representative compound molecule with a disordered structure is constructed, and a macroscopic compound target is simulated using periodic boundary conditions.

[0028] Use classical or ab initio molecular dynamics methods to perform geometric optimization and balance of the material until dynamic energy balance is achieved at room temperature, and obtain a simulated sample of the equilibrium state of the target compound target material.

[0029] Specifically, this embodiment uses the classic CHARMM force field to perform preliminary optimization on the sample. Then, a 6-picosecond adiabatic DFT-MD simulation was performed, and isomer changes dominated by proton transfer between glycine were observed. When dynamic equilibrium is reached, the system has free protons (P) and four glycine-based molecular states: classic glycine (Gly-C), zwitterionic glycine (Gly-Z), deprotonated glycine (Gly-P), and protonated glycine (Gly+P). Under equilibrium conditions, the average number of molecules of the four glycine groups is equal, and the average number of free protons is 1.

[0030] See also Figure 2 , is a microstructure diagram of the target material simulated and constructed in this embodiment, that is, the prepared amorphous glycine target material sample unit model.

[0031] The method for generating nanoscale ion motion trajectories described in step S2 includes: generating a large number of short trajectories of nanoscale length as samples along random directions in a simulation box, and excluding large error samples that cause violent collisions at the beginning and end of the trajectory due to too small collision parameters.

[0032] Specifically, the method for excluding large error trajectory samples includes: calculating the distance between the starting point and the end point of the trajectory 2 The trajectory part and the collision parameters of each element are discarded, and all collision parameters less than At the same time, in order to eliminate the influence of mirror excitation caused by periodic boundary conditions, in this embodiment, arrive Length The energy variation of the system over a single trajectory length.

[0033] The method of statistically analyzing the target distribution function group described in step S2 includes: for the target material component atom X, in this embodiment, C, H, O and N represent carbon, hydrogen, oxygen and nitrogen respectively, discretize the straight line trajectory of the ion crossing the target material along a random direction into recording points with a spacing of 0.1nm, and calculate the relationship between the ion and different elements at each position point The closest distance ,by Plot for the independent variable The histogram of the cumulative number of occurrences is normalized to obtain the target distribution function group of the closest distance between ions and various types of atoms. .

[0034] Specifically, in this embodiment, the statistics In the process of the target distribution function group, the large number of randomly generated ion trajectories is sufficient to ensure that the statistical distribution functions are smooth enough, thereby ensuring that the ions collide with possible local electronic structures in the target material with a reasonable probability, and can replace actual observation as the target function for subsequent short trajectory screening.

[0035] Specifically, the calculation method of the comprehensive overlap index is as follows: based on the statistical results of the target distribution function group of the closest distance described in S2, firstly calculate the distribution function corresponding to each selected short trajectory and each element in the target material respectively. With the target distribution function group The overlap index : ; in, is the distribution function of the closest distance between the ion and the X atom corresponding to each screened short trajectory, is the target distribution function group of the closest distance between ion and atom X.

[0036] Then take the total number of electrons of each element X in the compound , that is, the number of atoms Multiply by the number of electrons in a single atom , is the weight, calculate the comprehensive overlap index : .

[0037] Therefore, for complex polyatomic compounds, the comprehensive traversal degree of ion collisions with each element can be quantified by using the total electrons of each atom as weights, and the short trajectory combination that is most favorable for the accurate calculation of the overall electron stopping power of the compound can be obtained. The trajectory screening method using this comprehensive overlap index as a quantitative standard can achieve the optimal and correct sampling of multi-element collision distribution.

[0038] The method of screening short trajectories in step S3 includes: any optimization algorithm or mathematical programming method that can achieve the optimization goal of maximizing the comprehensive overlap index or reaching more than 95% (close to 100%), such as dynamic programming, greedy algorithm, and various types of heuristic optimization algorithms. The core principle of the screening algorithm should be: as the number of selected trajectories increases, the matching degree between the distribution function of the closest distance between the ion and the target corresponding to the selected trajectory combination and the target function (quantified by the comprehensive overlap index) keeps increasing, and the currently selected trajectory is the one that contributes the most to the match among the candidate samples.

[0039] See also Figure 3 , is a flowchart of the screening algorithm used in this embodiment. Specifically, for each candidate trajectory, this embodiment calculates the comprehensive overlap index , first select The second selected trajectory is the largest single trajectory. The trajectory with the greatest contribution. And so on, by continuously screening short trajectories, the total trajectory corresponding to Continue to increase as fast as possible until the cumulative overlap index of all selected short trajectories is Very close to 100%.

[0040] See also Figure 4 , which is a trajectory distribution diagram of the screening trajectory combination results provided in this embodiment. The rectangular parallelepiped in the figure represents the rectangular parallelepiped simulation unit constructed in this embodiment, that is, the simulated target microstructure unit space. Arrows of the same length but different grayscales represent the first 20 short trajectories selected in this embodiment.

[0041] See also Figure 5 , which is a distribution function group diagram of the closest distance between ions and target atoms on the screening ion trajectory combination provided in this embodiment, and the horizontal axis is the closest distance The length of the above figure is the screening result (i.e. the sampling trajectory) Distribution function, specifically the four elements C, H, O, and N and Comparison chart, where the smooth solid line is the target distribution function , the fluctuation line is the distribution function corresponding to the screening trajectory The figure below shows the situation where the same number of short paths are randomly selected without screening sampling. By comparing the upper and lower figures, it can be concluded that the trajectory combination screened by the algorithm has a significantly better approximation effect on the target distribution function than the randomly selected trajectory combination.

[0042] See also Figure 6 , which is the overlap index convergence diagram provided in this embodiment, the horizontal axis is the number of trajectory combinations, and the vertical axis is the cumulative overlap index , the solid line corresponds to the short trajectory selected by the algorithm of the present invention, the dotted line corresponds to the randomly selected short trajectory, and the four colors of red, blue, green and black correspond to the four elements C, H, O and N respectively. It can be observed that only 10 nanoscale screening short trajectory combinations are needed to achieve the target overlap index of the closest distance distribution between ions and the four atoms. More than 90% of them, and oxygen atoms with more electrons If a random short trajectory of the same length is used, the ions need to move nearly 100 trajectories to achieve a collision traversal effect close to that of 10 screening trajectories.

[0043] The time-dependent density functional method in step S4 includes: using the real-time time-dependent density functional (rt-TDDFT) method, combining the target equilibrium model in step S1 and the selected ion trajectory combination in step S3, assuming that the ions have a certain incident velocity , select the exchange-correlation function of the all-electron basis set and the generalized gradient approximation, choose the reversible evolution method to simulate the microcanonical ensemble evolution of the system's all-electron time-dependent density functional, and then calculate the electron stopping power corresponding to the selected trajectory group.

[0044] Specifically, this embodiment uses the Gaussian function plus plane wave method to represent the Kohn-Sham orbit of the electron. Compared with the full plane wave method, the advantage of this method is that it has high computational efficiency and can easily capture the behavior of electrons being excited to high energy states and ionized. Since both valence electrons and core electrons can significantly affect the electron stopping power, this embodiment uses the 6-311G** (triple zeta + bipolarization) basis set to explicitly include valence electrons and core electrons. The plane wave expansion energy cutoff of the electron density is 500 Ry, and Γ The electron Brillouin zone is sampled at 100 points. Since the kinetic energy of the ions in the Bragg peak region is large enough, the movement of the target nuclei can be ignored during the movement of the ions along the nanoscale orbit. Therefore, during the simulation, the target nuclei are restricted to their initial positions. The specific evolution simulation is implemented using the rt-TDDFT module in the open source quantum simulation software CP2K.

[0045] Specifically, the calculation method of the electron stopping power described in step S4 includes: calculating the energy deposition of ions in the target material. In this embodiment, before simulating ion irradiation of the target material, an ion is first placed at the initial position of the ion trajectory, and the electrical neutrality of the system is maintained by a uniform background charge, and then the ground state electronic structure of the system is calculated, and then the initial kinetic energy is assigned to the ion, and the time-dependent evolution of the all-electron Kohn-Sham orbit is started. According to the ion velocity and the ion trajectory step length d Determine the time step of evolution dtFor example, if the ion velocity is 0.25 au, the time step is 1.83 attoseconds. Because the ions have a constant velocity and the target nuclei are fixed, the total energy change of the microcanonical ensemble simulated by rt-TDDFT is the energy growth of the electronic system. , the selected trajectory corresponding to the ion Long , then the stopping power of electrons against ions is: Target electron stopping power corresponding to experimental observations is the corresponding value on all selected short trajectories The arithmetic mean of .

[0046] See also Figure 7 , is the Bragg peak region of the amorphous glycine target for protons and The dependence curve of the electron stopping power of a particle on the ion velocity (or kinetic energy), and its comparison with the mainstream calculation tool SRIM. The upper horizontal axis is the velocity of the incident ion, the lower horizontal axis is the energy of the incident ion, and the red line is the curve of the electron stopping power of glycine calculated in this embodiment. The error is represented by the root mean square of the electron stopping power corresponding to each short trajectory, which shows that the results of the ab initio calculation of the screening trajectory have good convergence characteristics. The blue line is the calculation result of SRIM. It can be observed that the results of this embodiment are basically consistent with the empirical method, but there is a slight red shift of the peak position to the low energy region compared with the SRIM curve, and the uncertainty of the empirical method cannot be evaluated. In the absence of experimental observation comparison at present, the efficient convergence ab initio calculation method that does not rely on empirical parameters adopted in this embodiment provides a more reliable new method that can effectively replace empirical calculations.

Claims

1. A method for simulating and calculating the electron stopping power of a complex compound, characterized in that: The following steps are involved: S1: According to the molecular composition of the compound target, the microscopic all-atom molecular structure of the target is constructed by computer simulation to obtain a simulated sample of the equilibrium state of the compound target; S2: randomly generate more than 10,000 nanoscale ion motion trajectories that pass through the compound target equilibrium state simulation sample described in S1, and record and count the target distribution function group of the closest distance to various atoms in the target during the ion crossing process; S3: 10-100 short trajectories are selected from the nanoscale ion motion trajectories described in S2, and the comprehensive overlap index of the shortest distance distribution function corresponding to the short trajectories and the target distribution function group in S2 reaches more than 95%; S4: Use the time-dependent density functional method to simulate the process of ions passing through the compound target along the short trajectories selected in S3, record the energy change of the system, and calculate and obtain the electron stopping power of the target for ions; The method of statistical target distribution function group in S2 is: discretize each straight line trajectory of ions crossing the target material along a random direction into recording points with a spacing of 0.1nm, and calculate the different component elements of ions and target molecules at each position point The closest distance ,by Plot for the independent variable The histogram of the cumulative number of occurrences is normalized to obtain the target distribution function group of the closest distance between the ion and various atoms of the target molecule. ; The method for selecting short trajectories in S3 is any optimization algorithm or mathematical programming method that can achieve an optimization goal of achieving a comprehensive overlap index of more than 95%; The calculation method of the comprehensive overlap index described in S3 is: based on the statistical results of the target distribution function group described in S2, first calculate the distribution function corresponding to each selected short trajectory and each element in the target material With the target distribution function group The overlap index : ,in, is the closest distance distribution function between the ions corresponding to each screened short trajectory and the X atom, is the target distribution function group of the closest distance between ion and X atom; Secondly, the total number of electrons of each element X in the compound , that is, the number of atoms Multiply by the number of electrons in a single atom , is the weight, calculate the comprehensive overlap index : .

2. The method for simulating and calculating the electron stopping power of a complex compound according to claim 1, characterized in that: The method described in S1 for simulating and constructing the microscopic all-atomic molecular structure of the target material is as follows: first, construct an orthogonal simulation unit of a compound molecule with a single-side size of 20 nanometers, calculate the number of target molecules required to fill the simulation unit based on the volume of the simulation unit, the density of the target material and the molecular weight, randomly place a corresponding number of molecules in the simulation unit, and apply periodic boundary conditions to the simulation unit to simulate the macroscopic-sized compound target material; secondly, use classical or ab initio molecular dynamics methods to geometrically optimize and balance the material to obtain a simulated sample of the equilibrium state of the compound target material.

3. The method for simulating and calculating the electron stopping power of a complex compound according to claim 1, characterized in that: The method for generating nanoscale ion motion trajectories described in S3 is as follows: more than 10,000 nanoscale trajectories are generated along random directions in the simulation unit as candidate samples, the closest distances between different positions on the sample trajectories and the target atoms are calculated, and the 2 nanoscale trajectories at the beginning and end of the trajectories are discarded. There is a collision parameter less than 0.8 in the range to exclude computational uncertainties caused by sharp collisions at the beginning and end of a single trajectory.

4. The method for simulating and calculating the electron stopping power of a complex compound according to claim 1, characterized in that: The time-dependent density functional method described in S4 includes: combining the compound target equilibrium state simulation sample described in S1 and the short trajectory of the ion screened in S3, setting the ion to have a certain incident speed , select the all-electron basis set of each element X, use the generalized gradient type exchange-correlation function, and select the reversible evolution algorithm to simulate the microcanonical ensemble evolution of the system's all-electron time-dependent density functional, realize the simulation of the physical process of the multi-electron system, and then perform the computational analysis of the electron stopping power.

5. The method for simulating and calculating the electron stopping power of a complex compound according to claim 4, characterized in that: The analysis method of the electron stopping power of compound target materials for ions in S4 is as follows: In the time-dependent density functional simulation, the ions are set to be incident at a fixed speed and kinetic energy, and the length is obtained. The short track Increased energy of the ion-target system , that is, the work done by the external force required to enable the flying ions to overcome the electron blocking and maintain their kinetic energy, and the compound target material is calculated to have a velocity of The electron stopping power of the ions for: , all selected short trajectories correspond to The arithmetic mean of the target material is the electron stopping power of the ions. .

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