Electrolyte system analysis method based on low-scale Monte Carlo simulation of electrostatic interactions

Abstract

This application discloses an analytical method for electrolyte systems based on low-scaling Monte Carlo simulations of electrostatic interactions, relating to the field of molecular simulation technology. The method includes: constructing an initial model of the target system and configuring simulation parameters; initializing local electric displacement auxiliary field variables; using these variables to describe the local electric displacement state of the target system; randomly selecting and updating an object; the updated object being the particle configuration and / or the local electric displacement auxiliary field variables; determining the equivalent local electric field strength at the target spatial location after updating the object; updating the local dielectric constant at the target spatial location based on the equivalent local electric field strength; and after updating the local dielectric constant, determining the various electrostatic interaction energies in the target system and evaluating the total electrostatic energy. This method can accurately describe the complex electrostatic interactions between components caused by local electric fields, high charge density, and the dielectric response properties of the components while ensuring computational efficiency.

Description

Technical Field

[0001] This application relates to the field of molecular simulation technology, and in particular to an analytical method for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions. Background Technology

[0002] Molecular simulation is an important technique for studying the relationship between the microscopic structure and macroscopic properties of matter using computer numerical methods. By modeling the positions, interactions, and statistical laws of particles in a system, molecular simulation can reveal the structural features, energy distribution, and statistical properties of matter at the atomic or molecular scale. Monte Carlo simulation is one type of molecular simulation method. It is a numerical simulation method based on the idea of ​​random sampling. By randomly perturbing the system state and judging whether the perturbation is acceptable according to a pre-defined probability criterion, it achieves the sampling and calculation of the statistical properties of the system. Monte Carlo simulation does not explicitly introduce the time evolution process and is suitable for studying the thermodynamic equilibrium properties and statistical distribution characteristics of the system. Traditional molecular simulations of complex fluids can be mainly divided into two categories according to model resolution: all-atom Monte Carlo simulation and coarse-grained Monte Carlo simulation. All-atom molecular Monte Carlo simulation explicitly models the molecules in the system at the atomic scale, and by preserving the spatial positions, local charges, and interaction parameters of each atom, it can finely characterize the microscopic structure and interactions of the system. Coarse-grained molecular Monte Carlo simulations group multiple atoms or molecules into a single coarse-grained particle, simplifying atomic-scale charge details and redundant degrees of freedom, and describing the macroscopic effects of the system by constructing an effective interaction potential.

[0003] However, in the analysis of electrostatic interactions in electrolyte systems, both of the above methods struggle to accurately describe the complex electrostatic interactions between components caused by local electric fields, high charge densities, and the dielectric response properties of the components while ensuring computational efficiency.

[0004] Therefore, how to solve the above-mentioned technical defects has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide an analytical method, apparatus, device, and storage device for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions, which can accurately describe the complex electrostatic interactions between components caused by local electric fields, high charge densities, and the dielectric response properties of components while ensuring computational efficiency.

[0006] To address the aforementioned technical problems, this application provides an analytical method for electrolyte systems based on low-scale Monte Carlo simulations of electrostatic interactions, comprising:

[0007] Construct an initial model of the target system and configure simulation parameters;

[0008] Initialize the local electric displacement auxiliary field variable; the local electric displacement auxiliary field variable is used to describe the local electric displacement state of the target system;

[0009] Randomly select an update object and update the update object; the update object is the particle configuration and / or the local electric displacement auxiliary field variable;

[0010] After the updated object is updated, the equivalent local electric field strength of the target spatial location is determined;

[0011] The local dielectric constant of the target spatial location is updated based on the equivalent local electric field strength.

[0012] After updating the local dielectric constant of the target spatial location, the electrostatic interaction energies of various types in the target system are determined and the total electrostatic energy is evaluated.

[0013] In some embodiments, updating the updated object includes:

[0014] If the updated object is a discrete particle configuration in the particle configuration, then the position coordinates or molecular orientation angle of the updated object in the simulation space are adjusted according to the preset probability distribution rules.

[0015] If the object to be updated is a structural unit in a polymer chain segment in a particle configuration, then, while maintaining the overall topological connectivity and structural integrity of the object to be updated, a local-scale configuration update operation is performed, and geometric constraints and energy limits are applied.

[0016] If the object to be updated is a local electric displacement auxiliary field variable, then the electric displacement vector at a preset spatial location in the target system is randomly perturbed.

[0017] In some embodiments, Gaussian constraints are applied during the updating of the local electric displacement auxiliary field variables.

[0018] In some embodiments, determining the equivalent local electric field strength at the target spatial location includes:

[0019] Based on the updated microstate of the target system after the update object, calculate the local electric displacement distribution at the target spatial location;

[0020] The equivalent local electric field strength at the target spatial location is derived based on the local electric displacement distribution.

[0021] In some embodiments, it also includes:

[0022] Acceptance is determined based on the change in total electrostatic energy compared to the previous state;

[0023] If the update is accepted, then update the status parameters;

[0024] If the update is refused, the state will revert to the original data before the update.

[0025] In some embodiments, the acceptance determination based on the change in total electrostatic energy compared to the previous state includes:

[0026] If the total electrostatic energy is lower than the energy in the previous state, then an update is accepted;

[0027] If the total electrostatic energy increases compared to the energy of the previous state, the Boltzmann factor is used as the probability to randomly determine whether to accept the update.

[0028] In some embodiments, it also includes:

[0029] Determine whether the preset termination condition has been met;

[0030] If the preset termination condition is met, statistical analysis and post-processing will be performed based on the sampled data;

[0031] If the preset termination condition is not met, return to the step of randomly selecting an update object to perform the next round of state update and sampling.

[0032] To address the aforementioned technical problems, this application also provides an analytical device for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions, comprising:

[0033] The building block is used to construct the initial model of the target system and configure the simulation parameters;

[0034] An initialization module is used to initialize local electric displacement auxiliary field variables; the local electric displacement auxiliary field variables are used to describe the local electric displacement state of the target system.

[0035] The selection module is used to randomly select an update object and update the update object; the update object is a particle configuration and / or a local electric displacement auxiliary field variable.

[0036] The first determining module is used to determine the equivalent local electric field strength of the target spatial location after the updated object is updated.

[0037] The update module is used to update the local dielectric constant of the target spatial location based on the equivalent local electric field strength.

[0038] The second determining module is used to update the local dielectric constant of the target spatial location, determine the electrostatic interaction energies of various types in the target system, and evaluate the total electrostatic energy.

[0039] To address the aforementioned technical problems, this application also provides an electronic device, comprising:

[0040] Memory, used to store computer programs;

[0041] A processor is used to execute the computer program to implement the steps of the electrolyte system analysis method based on low-scale Monte Carlo simulation of electrostatic interactions as described above.

[0042] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the electrolyte system analysis method based on low-scale Monte Carlo simulation of electrostatic interactions as described above.

[0043] The electrolyte system analysis method based on low-scale Monte Carlo simulation of electrostatic interactions provided in this application includes: constructing an initial model of the target system and configuring simulation parameters; initializing local electric displacement auxiliary field variables; the local electric displacement auxiliary field variables are used to describe the local electric displacement state of the target system; randomly selecting an update object and updating the update object; the update object is a particle configuration and / or local electric displacement auxiliary field variables; after the update object is updated, determining the equivalent local electric field strength at the target spatial location; updating the local dielectric constant at the target spatial location according to the equivalent local electric field strength; after updating the local dielectric constant at the target spatial location, determining various electrostatic interaction energies in the target system and evaluating the total electrostatic energy.

[0044] As can be seen, the electrolyte system analysis method based on low-scale Monte Carlo simulation of electrostatic interactions provided in this application introduces auxiliary field variables to characterize the local electric displacement state within the Monte Carlo simulation framework. This transforms the long-range electrostatic interactions in the target system into a locally updatable form, and calculates the local dielectric constant of the target system in real time based on the auxiliary field during the simulation, allowing the dielectric properties to dynamically change with the local electric field strength. By coupling particle position updates (including ions, small molecules, and polymer chain segments), auxiliary field updates, and the local dielectric constant calculation process, the method can accurately characterize the dielectric response properties of polarized components such as ions, small molecules, and polymer chain segments under strong local electric fields, as well as the resulting changes in electrostatic interactions of ionic components, while ensuring computational efficiency. This accurately describes the complex electrostatic interactions between components caused by the local electric field, high charge density, and component dielectric response properties.

[0045] The electrolyte system analysis apparatus, equipment, and computer-readable storage medium based on low-scale Monte Carlo simulation of electrostatic interaction provided in this application all have the aforementioned technical effects. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the prior art and embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A schematic flowchart illustrating an analytical method for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions, provided for an embodiment of this application;

[0048] Figure 2 A schematic flowchart illustrating a specific method for analyzing electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions, provided as an embodiment of this application;

[0049] Figure 3 A schematic diagram of an electrolyte system analysis device based on low-scale Monte Carlo simulation of electrostatic interaction provided in an embodiment of this application;

[0050] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0051] The core of this application is to provide an analytical method, apparatus, device, and storage medium for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions, which can accurately describe the complex electrostatic interactions between components caused by local electric fields, high charge densities, and the dielectric response properties of components while ensuring computational efficiency.

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] For molecular simulations of complex electrostatic interactions in electrolyte systems, existing techniques mainly employ all-atom Monte Carlo simulations and coarse-grained Monte Carlo simulations.

[0054] All-atom Monte Carlo simulations, by explicitly modeling various molecules within a system at the atomic scale, fully preserve atomic-level spatial positions, local charge distributions, and interaction parameters, thus enabling detailed depictions of electrostatic interactions and local structural features between ions, small molecules, and polymer chain segments. This type of method possesses high physical accuracy in describing microstructures, local electric field distributions, and charge-related structural features. However, given that all-atom simulations require explicit handling of a large number of atomic degrees of freedom and are typically paired with high-precision long-range electrostatic interaction solving algorithms, their computational complexity increases dramatically with system size. Especially in complex systems containing high ion concentrations, strong local electric fields, or long-chain polymers, the computational cost of electrostatic interactions increases approximately quadratically with the number of particles, significantly increasing the computation time and resources required for the simulation. This significantly limits the application scenarios of this type of method in large-scale system simulations, long-term simulations, and parametric space system scans, making it difficult to balance physical accuracy with computational efficiency and scalability.

[0055] Coarse-grained Monte Carlo simulations significantly reduce the system's degrees of freedom by grouping multiple atomic or molecular units into a single coarse-grained particle. They describe the statistical mechanical behavior of the system by constructing effective interaction potentials, thus greatly reducing computational complexity and improving simulation efficiency while maintaining a reasonable characterization of macroscopic structure and thermodynamic properties. This type of method has significant advantages in studying large-scale polymer-containing systems, long-term dynamic behavior, and parameter space scanning. However, because the atomic-scale charge distribution and polarization degrees of freedom are averaged during coarse-grained modeling, the system is usually treated as a dielectric continuum with a fixed or spatially uniform dielectric constant, replacing the local charge response of the components with the dielectric constant. This approach makes it difficult to accurately characterize effects such as enhanced local electric fields, dielectric inhomogeneities, and field-induced dielectric responses during simulation. Especially under conditions of high ion concentration, strong local electric fields, or strong ion-polymer coupling, the modulation of the system structure and properties by complex electrostatic interactions is difficult to reflect effectively, thus limiting the applicability of this type of method in resolving microscopic interaction mechanisms and establishing high-precision structure-property correlations.

[0056] To address the technical shortcomings of existing technologies, this application provides an analytical method for electrolyte systems based on low-scale Monte Carlo simulations of electrostatic interactions. This method introduces auxiliary field variables that accurately describe the local electrical states within the system, enabling the dielectric response behavior of the system to be dynamically and adaptively adjusted during the simulation process according to the real-time changes in the local electric field state. This auxiliary field variable mechanism can achieve efficient and self-consistent coupling with the configuration update process of particles in the system, thereby accurately describing the complex electrostatic interactions between components caused by the local electric field, high charge density, and dielectric response properties of the components while ensuring computational efficiency.

[0057] Please refer to Figure 1 , Figure 1 A schematic flowchart of an analytical method for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions, provided in an embodiment of this application, is shown below. Figure 1 As shown, the method includes:

[0058] S101: Construct the initial model of the target system and configure the simulation parameters.

[0059] The target system is the system to be analyzed. The target system can be an electrolyte system, encompassing various complex material systems capable of generating migratable ions, such as ionic solutions, ionic liquids, and polymeric electrolytes. The polymeric component can be charged polymers or non-ionized polymeric materials, such as polyelectrolytes, block polyelectrolytes, and ionomers. The ionic component can originate from polymers and added salt ions, ionic liquids, or other charged small molecules. Electrolyte systems can exist in solution, melt, gel, or complex states, and often exhibit significant physicochemical characteristics such as ion-small molecule-polymer electrostatic coupling, enhanced local electric fields, and dielectric nonlinear responses. The electrolyte system analysis method based on low-scale Monte Carlo simulation of electrostatic interactions provided in the embodiments of this application can analyze the complex electrostatic interactions and dielectric response behavior in electrolyte systems.

[0060] Construct an initial model of the target system and configure the basic parameters required for the simulation. Building the initial model includes defining the spatial dimensions of the simulation chamber, setting appropriate periodic or fixed boundary conditions, defining all particle types in the model (e.g., ions, small molecules, and polymer chain segments), determining the specific quantity of each type of particle, and rationally arranging their initial spatial distribution. If the target system contains polymer chains, it is also necessary to define the topological connections of the chains, the number of each chain segment, and its initial configuration. Simulation parameter settings cover the target temperature of the target system, the selected statistical ensemble (e.g., a canonical ensemble or an isothermal-isobaric ensemble), the total number of Monte Carlo sampling steps, the step size range for each trial update, and various control variables related to the medium response (e.g., dielectric constant, relaxation time). These parameters collectively ensure the accuracy and efficiency of the simulation.

[0061] The initial model can be generated fully automatically via a computer program. This program can efficiently construct initial systems (initial models) containing various types of particles, such as charged particles, molecules with dipole moments, or units described using equivalent dielectric models. Specifically, the initial model supports modeling polymer chains composed of multiple repeating monomers, where adjacent monomers within each polymer chain are connected by stable chemical bonds, forming continuous segments with specific topological structures. This allows for accurate description of the molecular architecture of complex systems such as polymers. The computer program boasts excellent cross-platform compatibility and language support, and can be implemented using any general-purpose computer programming language such as Python, Fortran, C / C++, Java, or Perl, facilitating integration into various simulation workflows. During the initial model generation process, users can flexibly set multiple key parameters according to specific research objectives and physical scenarios, including the spatial dimensions and boundary conditions of the simulation system, the total number of particles in the system, the value and distribution pattern of charge, the vector properties of the dipole moment, and its spatial orientation rules. Furthermore, this computer program supports various initial spatial distribution methods for particles. In addition to conventional regular lattice arrangements and completely random distributions, it can also employ non-uniform distribution algorithms designed based on specific spatial correlation functions or structural features to better match the microstructure of real materials. This highly customizable initial model generation strategy provides a robust and controllable starting point for subsequent Monte Carlo simulations, effectively ensuring the convergence of the simulation process and the reliability of the results.

[0062] S102: Initialize the local electric displacement auxiliary field variable; the local electric displacement auxiliary field variable is used to describe the local electric displacement state of the target system.

[0063] Within the simulation space, the target system is divided into a series of discrete units, and auxiliary field variables for local electric displacement are initialized to describe the local electric displacement state of the target system. These auxiliary field variables are key physical quantities used to quantitatively characterize the local electrical environment formed by the combined effects of charged particle distribution and polymer chain conformation, such as local polarization intensity or electric field distribution. Step S102 lays the foundation for subsequent bidirectional coupling calculations between particle configuration changes and medium response, ensuring that the interaction between electrical properties and structural evolution can be dynamically reflected during the simulation.

[0064] Local electric displacement auxiliary field variables refer to field variables introduced into the simulation space to characterize the local electrical state of the system. They are typically defined on discrete lattices or lattice bonds to describe electric displacement or equivalent electric field information. By locally updating the local electric displacement auxiliary field variables, long-range electrostatic interactions in the system can be transformed into a locally tractable form, thereby improving computational efficiency.

[0065] This application embodiment constructs a set of local electric displacement auxiliary field variables to characterize the physical response of a polarized medium under the action of a local electric field in real time. In modeling, this local electric displacement auxiliary field variable is defined as a set of spatially continuous or discrete physical quantities, specifically mapped to an electric displacement vector field, a local polarization intensity field, or an equivalent electric field variable. In the calculation process, the system divides the simulation space into discrete grid cells and projects the microscopic polarization contributions generated by the polar components onto the grid nodes, thereby constructing an auxiliary field distribution that reflects the local electrical environment of the system.

[0066] S103: Randomly select an update object and update the update object; the update object is the particle configuration and / or the local electric displacement auxiliary field variable;

[0067] Based on the principle of random sampling, the update object for this iteration is selected from the system. For the selected update object in the current simulation, a trial update is performed to achieve local updates. The selection range of update objects includes particle configuration objects (e.g., single ions, monomers / segments in small molecules or polymer chains) and local electric displacement auxiliary field variable objects. This random selection mechanism aims to ensure that the spatial positional freedom and electric field freedom of particles in the target system can be effectively sampled according to the basic rules of statistical mechanics, thereby achieving equilibrium exploration of configuration space and field space during the simulation and promoting the evolution of the target system towards an equilibrium state.

[0068] Local updates refer to updating only the auxiliary field variables of the local electric displacement within the current position of a particle or a local lattice region during the simulation, without calculating the inter-particle distances, energies, and forces of the entire system. Local updates can significantly improve computational efficiency and are suitable for simulating large-scale systems.

[0069] In some embodiments, updating the updated object includes:

[0070] If the updated object is a discrete particle configuration in the particle configuration, then the position coordinates or molecular orientation angle of the updated object in the simulation space are adjusted according to the preset probability distribution rules.

[0071] If the object to be updated is a structural unit in a polymer chain segment in a particle configuration, then, while maintaining the overall topological connectivity and structural integrity of the object to be updated, a local-scale configuration update operation is performed, and geometric constraints and energy limits are applied.

[0072] If the object to be updated is a local electric displacement auxiliary field variable, then the electric displacement vector at a preset spatial location in the target system is randomly perturbed.

[0073] If the updated object is a discrete particle configuration, its position coordinates or molecular orientation angle in the simulation space are adjusted according to a preset probability distribution rule. If the updated object is a specific structural unit in a polymer chain segment, a local-scale configuration update operation is performed while strictly maintaining its overall topological connectivity and structural integrity. Geometric constraints and energy limits are simultaneously applied to ensure that the updated configuration meets physical rationality and thermodynamic acceptability. During this process, the bond vector length between adjacent monomers is explicitly limited to a series of discrete values ​​(e.g., based on the simulated lattice unit length, a value of 1 is allowed). , (etc.), thus allowing reasonable local conformational fluctuations in chain segments while strictly ensuring the continuity and structural consistency of polymer chains. If the updated object is a local electric displacement auxiliary field variable object, then a small-amplitude random perturbation is applied to the electric displacement vector at a specific spatial location in the target system to generate a new candidate state to be determined, and prepares it for subsequent energy calculation and acceptance criterion steps.

[0074] The electric displacement vector is a physical quantity used to describe the electric field response in a medium, comprehensively reflecting the influence of free charges and medium polarization. In the embodiments of this application, the electric displacement vector is represented on a discrete lattice through an electric displacement auxiliary field variable, and serves as an important intermediate quantity for calculating the local electric field and dielectric constant.

[0075] The dielectric constant is a physical parameter characterizing the ability of a dielectric medium to respond to an electric field, used to describe the shielding effect of dielectric polarization on the electric field. In the embodiments of this application, the dielectric constant is not a fixed value, but a function calculated based on the local electric field strength during the simulation process, to reflect the response changes of the dielectric under different electric field conditions.

[0076] In some embodiments, Gaussian constraints are applied during the updating of the local electric displacement auxiliary field variables.

[0077] Gaussian constraints refer to the charge conservation conditions that auxiliary field variables must satisfy, i.e., the constraint relationship between the local electric displacement flux and the charge at the corresponding location. By maintaining Gaussian constraints during the update of local electric displacement auxiliary field variables, the physical consistency of the electrical description during the simulation can be ensured.

[0078] S104: After the updated object is updated, determine the equivalent local electric field strength of the target spatial location.

[0079] Based on the microscopic state of the target system after the trial update operation, the equivalent local electric field strength at the target spatial location is determined. The target spatial location is a preset area surrounding the update object.

[0080] In some embodiments, determining the equivalent local electric field strength at the target spatial location includes:

[0081] Based on the updated microstate of the target system, calculate the local electric displacement distribution at the target's spatial location;

[0082] The equivalent local electric field strength at the target spatial location is derived based on the local electric displacement distribution.

[0083] Based on the microscopic state of the target system after the trial update operation, the local electric displacement distribution at the target spatial location is recalculated, and the equivalent local electric field strength at the target spatial location is further derived through basic electrodynamic relationships. This step can reflect the changes in the local electric field environment caused by particle position variations or polymer chain conformation adjustments in real time and dynamically, providing key electrical inputs for subsequent energy assessment and dielectric property calculations. Specifically, the system will solve the relationship between the local electric field and macroscopic constraints based on the updated charge distribution and polar moment information, combined with boundary conditions.

[0084] At the modeling level of dielectric response, this application introduces a field-strength-dependent nonlinear response mechanism. The local dielectric constant is no longer set as a fixed constant, but is dynamically calculated based on the local electric field strength fed back by the auxiliary field. By constructing an adaptive response function (such as a nonlinear polarizability model), the system can consistently capture the dielectric saturation effect and spatially non-uniform shielding behavior occurring in strong electric field regions. This modeling approach achieves bidirectional coupling evolution between the local auxiliary field and the particle configuration space, enabling the simulation process to accurately analyze the electrical response characteristics of complex ionic systems and their regulatory mechanisms on structural properties without explicitly tracking microscopic molecular details.

[0085] S105: Update the local dielectric constant of the target spatial location based on the equivalent local electric field strength.

[0086] Based on the obtained equivalent local electric field strength, the local dielectric constant of the target spatial location in the target system is adaptively calculated, so that the dielectric constant can dynamically evolve with the real-time change of the local electric field. This allows for a more accurate characterization of the orientation recombination behavior of molecular dipole moments under a strong external electric field, as well as the resulting nonlinear dielectric response effect.

[0087] The dielectric response effect refers to the physical phenomenon in which the internal charges or dipoles of a dielectric are redistributed and reoriented under the influence of an applied electric field or a local electric field, thereby causing a change in the polarization state of the system and altering the equivalent dielectric constant with respect to the electric field strength, spatial location, or system configuration.

[0088] The calculation of the local dielectric constant is based on the local equivalent electric field strength, auxiliary field state parameters, or a preset nonlinear dielectric response model. During the simulation, the dielectric properties are dynamically adjusted and updated in real time. This embodiment introduces a nonlinear mapping relationship into the region of strong electric field action, which more naturally characterizes the polarization behavior of small molecules under external high electric field conditions, particularly the typical physical phenomenon of their polarization capability gradually saturating with increasing electric field strength—the dielectric saturation effect. The local dielectric constant update mechanism can be implemented using various numerical strategies, such as using analytical functions for efficient calculation, quickly obtaining approximate solutions through high-dimensional lookup tables and interpolation methods, or dynamically approximating the true solution using adaptive iterative algorithms. This effectively improves the numerical stability and overall computational efficiency of the simulation process while strictly ensuring the accuracy of the simulated physics.

[0089] S106: After updating the local dielectric constant of the target spatial location, determine the electrostatic interaction energies of various types in the target system and evaluate the total energy.

[0090] Step S106 aims to perform energy calculations for the simulated system. Energy calculations for the simulated system refer to the process of calculating various interaction energy terms of the system based on information such as particle distribution, local electric displacement auxiliary field variables, and dielectric constant. This is used to evaluate the relative stability of different simulated states and serves as the basis for the Monte Carlo acceptance criterion.

[0091] After dynamically updating the local dielectric constant, based on the corrected dielectric distribution, the electrostatic interaction energies in the target system are calculated, including Coulomb interactions between charged particles, polarization effects between ions and polymer chain segments, and coupling energies between particles and the auxiliary electric field. Integrating the short-range interaction potential energies that have already considered the effects of van der Waals forces, a total energy assessment is performed on the current experimentally updated system configuration to clarify its energy change compared to the previous state, providing a basis for subsequent acceptance criteria.

[0092] In some embodiments, it also includes:

[0093] Acceptance is determined based on the change in total electrostatic energy compared to the previous state;

[0094] If the update is accepted, then update the status parameters;

[0095] If the update is refused, the state will revert to the original data before the update.

[0096] In some embodiments, the acceptance determination based on the change in total electrostatic energy compared to the previous state includes:

[0097] If the total electrostatic energy is lower than the energy in the previous state, then an update is accepted;

[0098] If the total electrostatic energy increases compared to the energy of the previous state, the Boltzmann factor is used as the probability to randomly determine whether to accept the update.

[0099] If the total energy decreases, the update is accepted; if the total energy increases, it is accepted with a probability related to the Boltzmann factor. If the update is accepted, the system state parameters (such as position, dielectric properties, etc.) will be updated accordingly; if the update is rejected, all states will revert to the original data before the update, ensuring that the sampling satisfies the detailed equilibrium condition.

[0100] The acceptance probability criterion is a probabilistic rule used in Monte Carlo simulations to determine whether a particle position update or an auxiliary field update is accepted. This criterion is typically calculated based on the energy change before and after the update to ensure that the simulation results conform to a statistical mechanical distribution. According to the pre-defined energy acceptance criterion in Monte Carlo simulations (usually the Metropolis criterion), the system determines whether to accept the current state update based on the total energy difference before and after the update, and performs corresponding operations based on the determination result.

[0101] The total energy function of the target system is composed of several key energy terms, including the electrostatic interaction energy between charged particles, the coupling energy between particles and the local auxiliary field, the self-energy term of the auxiliary field, and the short-range interaction potential energy describing close-range interactions. During the simulation, after each tentative update of the system state, the total energy change before and after the update must be calculated, and this serves as the basis for logical decision-making. The system decides whether to accept the state update based on the total energy difference. Commonly used acceptance criteria include the classic Metropolis algorithm, but other rules based on statistical mechanics principles, such as the Glauber criterion or the HeatBath Algorithm, can also be used. These methods control the evolution path of the system state in the form of a probability distribution, thereby ensuring that the system tends to equilibrium after multiple samplings, and the results strictly conform to the equilibrium distribution requirements in statistical mechanics.

[0102] In some embodiments, it also includes:

[0103] Determine whether the preset termination condition has been met;

[0104] If the target is reached, statistical analysis and post-processing will be performed based on the sampled data;

[0105] If the target is not reached, return to the step of randomly selecting an update object to perform the next round of state update and sampling.

[0106] The simulation continuously monitors whether the preset termination conditions have been met. Termination criteria typically include, but are not limited to: whether the maximum set number of Monte Carlo steps has been reached, whether the total energy of the system tends to stabilize, and whether fluctuations in local dielectric response or other key physical quantities have converged to statistical equilibrium. If any termination condition is not met, the simulation will continue to run in a loop, returning to the step of randomly selecting an update object to perform the next round of state updates and sampling.

[0107] After the simulation is complete, the system will output all sampled data, including the final particle spatial configuration, the conformational distribution of polymer chains, the spatial evolution of the auxiliary electric field, and the dynamic change of the dielectric constant over time. Based on this data, systematic statistical analysis and post-processing are performed, such as calculating the radial distribution function, dielectric fluctuation spectrum, and correlation function, thereby revealing the correlation mechanism between the system's microstructural characteristics, dielectric response behavior, and macroscopic physicochemical properties.

[0108] Electrolyte systems encompass a wide range of complex material systems capable of generating migratable ions, including ionic solutions, ionic liquids, polymeric electrolytes, and composite electrolytes. Dielectric systems are widely used in new energy, high-end electronics manufacturing, bioengineering, and fine chemicals. Analyzing dielectric systems can provide valuable data for applications in new energy and high-end electronics manufacturing.

[0109] The generated simulated trajectories and data undergo high-precision statistical processing using a dedicated analysis program. This program boasts excellent compatibility and flexibility, supporting interfaces for multiple programming languages, including Fortran, Python, and C / C++. The analysis covers several key physical quantities, such as, but not limited to, the spatial distribution function of particles, the spatial distribution of electric potential and electric field vectors, the spatial variation characteristics of the local dielectric constant, and the statistical analysis of system energy fluctuations. By employing the Markov chain Monte Carlo method to perform time averaging or ensemble averaging based on a large number of independent samples, the macroscopic physical properties of the target system under thermodynamic equilibrium can be effectively extracted, and the intrinsic correlation between these macroscopic properties and microscopic structural features can be deeply revealed.

[0110] Furthermore, the embodiments of this application support flexible visualization of simulation results from multiple dimensions and perspectives. By deeply integrating a high-performance plotting library, a scriptable and customizable visualization toolchain, and various mainstream third-party numerical analysis software, the system can perform high-precision, real-time dynamic visualization characterization of particle instantaneous configuration, auxiliary field spatial distribution cloud maps, dielectric constant gradient changes, and their dynamic response characteristics under applied electric, magnetic, or temperature fields. This function not only supports static image output but also intuitively presents the evolution of physical processes in the form of animations and interactive charts, thereby better meeting the diverse needs for data display accuracy, analysis depth, and explanation of physical mechanisms in different scenarios, from basic research to engineering applications.

[0111] The method proposed in this application is highly flexible and adaptable in design. It can run independently as a standalone simulation tool or be easily integrated into various mainstream open-source or commercial molecular simulation software platforms. The program exhibits excellent cross-platform compatibility, running stably on general-purpose CPU architectures while significantly improving computational performance by fully utilizing GPU hardware acceleration capabilities. It also supports deployment in heterogeneous computing cluster environments composed of multiple computing units. Furthermore, the program is fully compatible with mainstream parallel computing frameworks such as MPI and OpenMP, effectively achieving task-level and thread-level parallelism. This allows it to efficiently handle computationally demanding simulation scenarios such as large-scale systems, high-throughput screening, and complex multi-scale coupling, demonstrating outstanding scalability and powerful parallel computing support capabilities.

[0112] The application scope of the method provided in the above embodiments of this application specifically includes: electrolyte systems, covering various complex material systems capable of generating migratable ions, such as ionic solutions, ionic liquids, and polymeric electrolytes. The polymeric component can be charged polymers or non-ionized polymeric materials such as polyelectrolytes, block polyelectrolytes, and ionomers; the ionic component can originate from polymers and added salt ions, ionic liquids, or other charged small molecules. The system can exist in various physical states such as solution, melt, gel, and composite material states.

[0113] The method provided in the above embodiments of this application can support efficient simulation calculations under various statistical ensembles, including but not limited to canonical ensembles, grand canonical ensembles, or isothermal and isobaric ensembles. Furthermore, depending on the needs of the actual physical problem, the boundary conditions of the simulation system can be flexibly set to various types, such as periodic boundary conditions, fixed boundary conditions, or free boundary conditions. The spatial dimension of the simulation system is also highly adaptable, covering both two-dimensional planar systems and more complex three-dimensional spatial systems. During the sampling process, the system supports various Monte Carlo update operation strategies, which can be executed on single particles, sets of multiple particles, or local electric displacement auxiliary field variables. The update forms are diverse, including particle position displacement, molecular orientation rotation, tentative random perturbations of local electric displacement auxiliary field variables, and combined update modes of the above operations. These diverse update methods not only effectively improve the sampling efficiency of the system but also help to more comprehensively traverse the configuration space of the system during the simulation process, thereby enhancing the accuracy and reliability of the simulation results.

[0114] refer to Figure 2As shown, a specific embodiment is described below. This embodiment introduces an auxiliary field variable to characterize local electric displacement during the Monte Carlo sampling process and tightly couples this variable with the dynamic update process of the system configuration. This allows the dielectric parameters within the system to dynamically evolve and update according to the real-time changes in the local equivalent electric field, effectively reflecting the non-uniformity and dynamic response characteristics of electrostatic interactions in complex environments. This embodiment directly embeds the dielectric response mechanism into the Monte Carlo simulation sampling process, achieving a more accurate description of the physical behavior of the electrolyte system without significantly increasing the system's degrees of freedom and computational complexity.

[0115] This embodiment, while maintaining high computational efficiency, significantly improves the numerical stability of the simulation process under extreme electrostatic conditions, and expands the applicability of the method to various practical and complex systems. The specific process includes the following steps:

[0116] 1. Construct the initial model and set the simulation parameters:

[0117] The initial model of the system under study is constructed, and the basic parameters required for the simulation are configured. The construction of the initial model includes defining the spatial dimensions of the simulation chamber, setting appropriate periodic or fixed boundary conditions, defining all particle types in the system (e.g., ions, small molecules, and polymer chain segments), determining the specific quantity of each type of particle, and arranging their initial spatial distribution. If the system contains polymer chains, the topological connections of the chains, the number of each chain segment, and its initial configuration must also be defined. Simultaneously, the simulation parameter settings cover the target temperature of the system, the selected statistical ensemble (e.g., a canonical ensemble or an isothermal-isobaric ensemble), the total number of Monte Carlo sampling steps, the step size range for each trial update, and various control variables related to the medium response (e.g., dielectric constant, relaxation time). These parameters collectively ensure the accuracy and efficiency of the simulation.

[0118] 2. Initialize the local electric displacement auxiliary field variables:

[0119] Within the simulation space, the system is divided into a series of discrete units, and auxiliary field variables are initialized to describe the local electric displacement state of the system. These auxiliary field variables are key physical quantities used to quantitatively characterize the local electrical environment, such as local polarization intensity or electric field distribution, formed by the interaction of charged particle distribution and polymer chain conformation. This step lays a solid foundation for subsequent bidirectional coupling calculations between particle configuration changes and medium response, ensuring that the interaction between electrical properties and structural evolution can be dynamically reflected during the simulation.

[0120] 3. Randomly select Monte Carlo update objects:

[0121] Following strict random sampling principles, the update objects for this iteration are selected from the system. The selection range includes particle configuration objects (such as single ions, monomers / segments in small molecules or polymer chains) and local auxiliary field variable objects. This random selection mechanism aims to ensure that the spatial positional and electric field degrees of freedom of particles in the system are effectively sampled according to the basic rules of statistical mechanics, thereby achieving equilibrium exploration of configuration space and field space during the simulation process and promoting the evolution of the system towards an equilibrium state.

[0122] 4. Perform particle configuration or auxiliary field probe updates:

[0123] For the selected update target in the current simulation, the system will perform exploratory operations: if the update target is a discrete particle configuration, its position coordinates or molecular orientation angle in the simulation space will be adjusted according to a preset probability distribution rule; if the update target is a specific structural unit in a polymer chain segment, a local-scale configuration update operation will be performed while strictly maintaining its overall topological connectivity and structural integrity, and necessary geometric constraints and energy limits will be applied simultaneously to ensure that the updated configuration meets physical rationality and thermodynamic acceptability. During this process, the bond vector length between adjacent monomers is explicitly limited to a series of discrete values ​​(e.g., based on the simulated lattice unit length, a value of 1 is allowed). , (etc.), thus allowing reasonable local conformational fluctuations in chain segments while strictly ensuring the continuity and structural consistency of polymer chains. If the updated object is an auxiliary field variable, a small-amplitude random perturbation is applied to the electric displacement vector at a specific spatial location in the system to generate a new candidate state to be determined, and prepares it for subsequent energy calculation and acceptance criterion steps.

[0124] 5. Calculate the local electric displacement and equivalent electric field:

[0125] Based on the microscopic state of the system after the exploratory update operation in the above steps, the local electric displacement distribution at the corresponding spatial location is recalculated, and the equivalent local electric field strength in this region is further derived through basic electrodynamic relationships. This step can reflect the changes in the local electric field environment caused by particle position changes or polymer chain conformation adjustments in real time and dynamically, providing key electrical inputs for subsequent energy assessment and dielectric property calculations. Specifically, the system will solve the relationship between the local electric field and macroscopic constraints based on the updated charge distribution and polar moment information, combined with boundary conditions.

[0126] 6. Dynamically calculate the local dielectric constant:

[0127] Based on the equivalent local electric field strength data obtained in step five, the system will adaptively calculate the local dielectric constant at the corresponding spatial location in the system. This allows the dielectric constant to dynamically evolve with the real-time changes in the local electric field, thereby more accurately characterizing the orientational recombination behavior of molecular dipole moments under a strong external electric field, and the resulting nonlinear dielectric response effect.

[0128] 7. Evaluate the electrostatic energy and total energy of the system:

[0129] After dynamically updating the local dielectric constant, based on the corrected dielectric distribution, the system needs to calculate various electrostatic interaction energies, including Coulomb interactions between charged particles, polarization effects between ions and polymer chain segments, and coupling energies between particles and the auxiliary electric field. Subsequently, by integrating the short-range interaction potential energies that have already considered the effects of van der Waals forces, a total energy assessment is performed on the current tentatively updated system configuration to clarify its energy change compared to the previous state, providing a basis for subsequent acceptance criteria.

[0130] 8. Acceptance decision based on energy changes:

[0131] Based on the pre-defined energy acceptance criterion in Monte Carlo simulations—usually the Metropolis criterion—the system determines whether to accept the state update based on the total energy difference before and after the update. Specifically, if the energy decreases, the update is accepted; if the energy increases, it is accepted with a probability related to the Boltzmann factor. If the update is accepted, the system state parameters (such as position, dielectric properties, etc.) will be updated accordingly; if it is rejected, all states revert to the original data before the update, ensuring that the sampling satisfies the detailed equilibrium condition.

[0132] 9. Determine if the termination condition is met:

[0133] The system needs to continuously monitor whether the current simulation has reached the preset termination conditions. Termination criteria typically include, but are not limited to: whether the set maximum number of Monte Carlo steps has been reached, whether the total energy of the system tends to stabilize, and whether fluctuations in local dielectric response or other key physical quantities have converged to statistical equilibrium. If any termination condition is not met, the simulation will continue to loop and return to step three to perform the next round of state updates and sampling.

[0134] 10. Output the simulation results and perform statistical analysis:

[0135] After the simulation process is officially completed, the system will output all sampled data, including the final particle spatial configuration, the conformational distribution of polymer chains, the spatial evolution of the auxiliary electric field, and the dynamic change of the dielectric constant over time. Based on this data, systematic statistical analysis and post-processing are performed, such as calculating the radial distribution function, dielectric fluctuation spectrum, and correlation function, thereby revealing the correlation mechanism between the microstructural characteristics of the system, dielectric response behavior, and its macroscopic physicochemical properties.

[0136] In summary, the method provided in this application transforms long-range electrostatic interactions into a localized update form. Within the framework of Monte Carlo sampling theory based on grid partitioning, this method abandons the traditional explicit solution method that directly calculates the pairwise Coulomb interactions between all charged particles. Instead, it introduces a set of auxiliary field variables, namely localized electric displacement auxiliary variables, defined on discrete spatial units (e.g., grid points or bond structures). These auxiliary field variables are used to dynamically characterize the localized electric displacement distribution state corresponding to each spatial unit in the simulated system. During the sampling process, only localized random perturbation and iterative updates of the auxiliary field variables on the current grid point or adjacent units are needed to implicitly reconstruct the global electrostatic interactions of the system. This transforms the electrostatic interactions, which originally had non-local, long-range decay characteristics, into a localized calculation form that depends only on the information of adjacent discrete units. This reduces the computational complexity of the total electrostatic energy of the system from an order of magnitude (O(n)) to an order of magnitude (O(n)) based on the square of the number of particles in traditional methods. The growth of )) is significantly reduced to the level of an approximately linear scaling (O(N)), which significantly improves the computational efficiency of Monte Carlo sampling processes in large-scale electrolyte systems.

[0137] Furthermore, the method provided in this application proposes a technical solution where the dielectric constant can evolve in real time with the local electric field strength. The core feature of this solution is that the dielectric constant at each location in the system is no longer set as a globally fixed empirical constant, but rather constructed as a function of the local equivalent electric field strength. During the entire physical field numerical simulation process, it is dynamically calculated and adjusted based on the real-time updated auxiliary electric field state. By introducing and establishing a nonlinear mapping relationship, the dielectric saturation effect and its dynamic response behavior caused by polarization components in the environment under strong local electric fields can be accurately characterized. This effectively solves the problem of physical distortion and insufficient accuracy commonly found in describing dielectric environments under highly non-uniform, strong field conditions.

[0138] In addition, the method provided in this application proposes a synchronous iterative calculation process with dual degrees of freedom. Its core feature is that when the spatial arrangement of various particles in the system changes, the electric field distribution and dielectric environment around them can achieve real-time, self-consistent dynamic response and synchronous update. This allows for more accurate capture of the strong electrostatic coupling effect between ions and polymer chains in the simulation, significantly improving the accuracy and reliability of simulating the equilibrium state and dynamic behavior of complex charged systems.

[0139] Furthermore, the method provided in this application proposes an auxiliary field update scheme to maintain physical conservation properties. This scheme introduces constraints to strictly guarantee the conservation laws hold during the numerical simulation process. Its core feature is that a Gaussian constraint must be applied during each tentative update of the auxiliary field variables to ensure that the electric displacement flux at each discrete local grid point and the distribution of free charge at that location always strictly satisfy the law of charge conservation. By embedding this physical constraint into the sampling update strategy, the proposed method not only maintains the efficiency of the computational process and the feasibility of the numerical algorithm, but also significantly enhances the physical consistency and reliability of the simulation results when describing complex systems with strong local electric field distributions and high charge density characteristics, thereby effectively supporting larger-scale, longer-time-scale electromagnetic dynamics simulations.

[0140] Furthermore, the method provided in this application proposes a polymer electrolyte modeling scheme that combines a dynamic dielectric-assisted field method. Its core technical feature lies in the innovative introduction of a bond fluctuation model during the conformational update process of the polymer chain. By restricting the bond vectors between adjacent monomers to a specific discrete set of values, the conformational freedom of the molecular chain is effectively constrained. By combining this polymer model with strict topological connectivity constraints with the aforementioned dynamic dielectric-assisted field method, accurate simulation of the dielectric response and molecular configuration interactions of the electrolyte system is achieved, providing a powerful theoretical tool and computational means for efficiently resolving the intrinsic laws between microstructure and macroscopic properties at the molecular scale.

[0141] Bond fluctuation models are discretized models used in lattice Monte Carlo simulations to describe the configuration of polymer chains. In these models, the polymer chain consists of a series of interconnected segments, with adjacent segments linked by bonds that maintain chain connectivity. These bonds can vary within a finite range of discrete values, allowing for reasonable local conformational fluctuations of the polymer chain in lattice space. Bond fluctuation models improve the degrees of freedom and efficiency of configuration sampling while ensuring the stability of the polymer chain's topology, making them suitable for describing the statistical conformational behavior of polymeric electrolyte systems.

[0142] Lattice Monte Carlo model refers to a Monte Carlo simulation model that performs statistical sampling in a discretized space. The simulation space is divided into regular or irregular lattice units, and the positions, configurations or states of particles, molecules or polymer chain segments are confined to the lattice nodes. The statistical sampling and evolution of the system configuration is achieved through random trial updates combined with energy change acceptance criteria, in order to describe the thermodynamic and structural properties of complex liquid systems.

[0143] The local electrostatic interaction Monte Carlo simulation method refers to a simulation method that introduces an auxiliary field description based on gauge field theory into the lattice Monte Carlo particle sampling framework. This method solves for electric displacement or equivalent electric field constraints consistent with the charge distribution on a discrete lattice, and then updates the auxiliary field variables in tandem with the particle degrees of freedom. By transforming the originally long-range electrostatic interactions between particles into local interactions and local update processes on the auxiliary field variables, this method avoids explicitly calculating pairwise Coulomb interactions between particles. Therefore, the calculations related to electrostatic interactions no longer increase quadratically with the number of particles, but rather scale approximately linearly with the computational scale of the simulated system, significantly reducing computational complexity and improving the computational efficiency and scalability of Monte Carlo simulations of large-scale electrolyte systems.

[0144] Furthermore, the method provided in this application is applicable to electrolyte systems, covering various complex material systems capable of generating migratable ions, such as ionic solutions, ionic liquids, and polymeric electrolytes. The polymeric component can be charged polymers or non-ionized polymeric materials such as polyelectrolytes, block polyelectrolytes, and ionomers; the ionic component can originate from polymers and added salt ions, ionic liquids, or other charged small molecules. The system can exist in various physical states, such as solution, melt, gel, and composite material states.

[0145] This application also provides an analytical apparatus for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions. The apparatus described below corresponds to the method described above. Please refer to... Figure 3 , Figure 3 This is a schematic diagram of an electrolyte system analysis device based on low-scale Monte Carlo simulation of electrostatic interaction, provided in an embodiment of this application. Figure 3 As shown, the device includes:

[0146] Module 10 is used to build the initial model of the target system and configure the simulation parameters;

[0147] Initialization module 20 is used to initialize local electric displacement auxiliary field variables; the local electric displacement auxiliary field variables are used to describe the local electric displacement state of the target system;

[0148] Selection module 30 is used to randomly select an update object and update the update object; the update object is particle configuration and / or local electric displacement auxiliary field variable;

[0149] The first determining module 40 is used to determine the equivalent local electric field strength of the target spatial location after the updated object is updated.

[0150] Update module 50 is used to update the local dielectric constant of the target spatial location based on the equivalent local electric field strength;

[0151] The second determining module 60 is used to update the local dielectric constant of the target spatial location, determine the electrostatic interaction energies of various types in the target system, and evaluate the total electrostatic energy.

[0152] In some embodiments, the selection module 30 includes:

[0153] The first update unit is used to adjust the position coordinates or molecular orientation angle of the update object in the simulation space according to a preset probability distribution rule if the update object is a discrete particle configuration in the particle configuration.

[0154] The second update unit is used to perform a local-scale configuration update operation and apply geometric constraints and energy limits if the update object is a structural unit in a polymer chain segment in a particle configuration, while maintaining the overall topological connectivity and structural integrity of the update object.

[0155] The third update unit is used to randomly perturb the electric displacement vector at a preset spatial location in the target system if the update object is a local electric displacement auxiliary field variable.

[0156] In some embodiments, it also includes:

[0157] The constraint application module is used to apply Gaussian constraints during the update of the local electric displacement auxiliary field variables.

[0158] In some embodiments, the first determining module 40 includes:

[0159] The calculation unit is used to calculate the local electric displacement distribution at the target spatial location based on the microstate of the target system after the update object is updated;

[0160] The derivation unit is used to derive the equivalent local electric field strength of the target spatial location based on the local electric displacement distribution.

[0161] In some embodiments, it also includes:

[0162] The determination module is used to make an acceptance determination based on the change in total electrostatic energy compared to the previous state;

[0163] The status parameter update module is used to update the status parameters if an update is accepted.

[0164] The state rollback module is used to revert the state to the original data before the update if the update is rejected.

[0165] In some embodiments, the determination module is used to:

[0166] If the total electrostatic energy is lower than the energy in the previous state, then an update is accepted;

[0167] If the total electrostatic energy increases compared to the energy of the previous state, the Boltzmann factor is used as the probability to randomly determine whether to accept the update.

[0168] In some embodiments, it also includes:

[0169] The judgment module is used to determine whether the preset termination condition has been met;

[0170] The statistics and post-processing module is used to perform statistical analysis and post-processing on the sampled data if a preset termination condition is met.

[0171] The return module is used to return to the step of randomly selecting an update object to perform the next round of state update and sampling if the preset termination condition is not met.

[0172] This application also provides an electronic device, referenced... Figure 4 As shown, the device includes a memory 1 and a processor 2.

[0173] Memory 1 is used to store computer programs;

[0174] Processor 2 is used to execute computer programs to perform the following steps:

[0175] An initial model of the target system is constructed and simulation parameters are configured; local electric displacement auxiliary field variables are initialized; the local electric displacement auxiliary field variables are used to describe the local electric displacement state of the target system; an update object is randomly selected and updated; the update object is the particle configuration and / or the local electric displacement auxiliary field variables; after the update object is updated, the equivalent local electric field strength at the target spatial location is determined; the local dielectric constant at the target spatial location is updated according to the equivalent local electric field strength; after updating the local dielectric constant at the target spatial location, the various electrostatic interaction energies in the target system are determined and the total electrostatic energy is evaluated.

[0176] For a description of the equipment provided in this application, please refer to the above method embodiments; further details will not be provided here.

[0177] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the following steps:

[0178] An initial model of the target system is constructed and simulation parameters are configured; local electric displacement auxiliary field variables are initialized; the local electric displacement auxiliary field variables are used to describe the local electric displacement state of the target system; an update object is randomly selected and updated; the update object is the particle configuration and / or the local electric displacement auxiliary field variables; after the update object is updated, the equivalent local electric field strength at the target spatial location is determined; the local dielectric constant at the target spatial location is updated according to the equivalent local electric field strength; after updating the local dielectric constant at the target spatial location, the various electrostatic interaction energies in the target system are determined and the total electrostatic energy is evaluated.

[0179] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0180] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0181] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatuses, devices, and computer-readable storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant details can be found in the method section.

[0182] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0183] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0184] The analytical method for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions provided in this application has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. An analytical method for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions, characterized in that, include: Construct an initial model of the target system and configure simulation parameters; Initialize the local electric displacement auxiliary field variables; The local electric displacement auxiliary field variable is used to describe the local electric displacement state of the target system; Randomly select an update object and update the update object; the update object is the particle configuration and / or the local electric displacement auxiliary field variable; After the updated object is updated, the equivalent local electric field strength of the target spatial location is determined; The local dielectric constant of the target spatial location is updated based on the equivalent local electric field strength. After updating the local dielectric constant of the target spatial location, the electrostatic interaction energies of various types in the target system are determined and the total electrostatic energy is evaluated.

2. The method for analyzing electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions according to claim 1, characterized in that, Updating the object includes: If the updated object is a discrete particle configuration in the particle configuration, then the position coordinates or molecular orientation angle of the updated object in the simulation space are adjusted according to the preset probability distribution rules. If the object to be updated is a structural unit in a polymer chain segment in a particle configuration, then, while maintaining the overall topological connectivity and structural integrity of the object to be updated, a local-scale configuration update operation is performed, and geometric constraints and energy limits are applied. If the object to be updated is a local electric displacement auxiliary field variable, then the electric displacement vector at a preset spatial location in the target system is randomly perturbed.

3. The method for analyzing electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions according to claim 2, characterized in that, During the update of the local electric displacement auxiliary field variable, Gaussian constraints are applied.

4. The method for analyzing electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions according to claim 1, characterized in that, The equivalent local electric field strength for determining the spatial location of a target includes: Based on the updated microstate of the target system after the update object, calculate the local electric displacement distribution at the target spatial location; The equivalent local electric field strength at the target spatial location is derived based on the local electric displacement distribution.

5. The method for analyzing electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions according to claim 1, characterized in that, Also includes: Acceptance is determined based on the change in total electrostatic energy compared to the previous state; If the update is accepted, then update the status parameters; If the update is refused, the state will revert to the original data before the update.

6. The method for analyzing electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions according to claim 5, characterized in that, The acceptance determination based on the change in total electrostatic energy compared to the previous state includes: If the total electrostatic energy is lower than the energy in the previous state, then an update is accepted; If the total electrostatic energy increases compared to the energy of the previous state, the Boltzmann factor is used as the probability to randomly determine whether to accept the update.

7. The analytical method for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions according to claim 1, characterized in that, Also includes: Determine whether the preset termination condition has been met; If the preset termination condition is met, statistical analysis and post-processing will be performed based on the sampled data; If the preset termination condition is not met, return to the step of randomly selecting an update object to perform the next round of state update and sampling.

8. An analytical device for electrolyte systems based on low-scale Monte Carlo simulation of electrostatic interactions, characterized in that, include: The building block is used to construct the initial model of the target system and configure the simulation parameters; The initialization module is used to initialize the local electric displacement auxiliary field variables; The local electric displacement auxiliary field variable is used to describe the local electric displacement state of the target system; The selection module is used to randomly select an update object and update the update object; the update object is a particle configuration and / or a local electric displacement auxiliary field variable. The first determining module is used to determine the equivalent local electric field strength of the target spatial location after the updated object is updated. The update module is used to update the local dielectric constant of the target spatial location based on the equivalent local electric field strength. The second determining module is used to update the local dielectric constant of the target spatial location, determine the electrostatic interaction energies of various types in the target system, and evaluate the total electrostatic energy.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for analyzing electrolyte systems based on low-scale Monte Carlo simulations of electrostatic interactions as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the electrolyte system analysis method based on low-scale Monte Carlo simulation of electrostatic interactions as described in any one of claims 1 to 7.

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