CN117012292B - Research simulation method and system for tree-shaped molecular structure in self-driven particle bath - Google Patents
Research simulation method and system for tree-shaped molecular structure in self-driven particle bath Download PDFInfo
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Abstract
The invention provides a research simulation method and a system for a tree-shaped molecular structure in a self-driven particle bath, wherein the method comprises the following steps: step 1: establishing a first target model of a polymer chain with a tree-shaped molecular structure and a second target model of self-driven particles; step 2: setting a first target parameter of a simulation environment; step 3: acquiring an initialization relaxation result according to the first target parameter, the first target model and the second target model; step 4: determining a random model according to different initialized relaxation results; step 5: and adjusting a second target parameter of the random model, and simulating to obtain a simulation result. According to the research simulation method of the dendrimer structure in the self-driven particle bath, the random model is determined according to the different initial relaxation results, the simulation result is determined by introducing the second target parameter, the simulation result is more accurate and reliable, the subsequent physical analysis of the simulation result and the theoretical result is facilitated, and the physical mechanism of the dendrimer is further explored.
Description
Technical Field
The invention relates to the technical field of computational theory chemistry, in particular to a research simulation method of a dendrimer structure in a self-driven particle bath.
Background
At present, the physical intersection of active substances and polymers is a growing research hotspot in recent years. Self-driven particles (self-propel led particles) are a class of microscopic particles capable of autonomous movement, which are capable of autonomous movement without being affected by the outside world. These particles may be cells, bacteria or artificial nanoparticles in the living body, etc. The particles absorb energy from the external environment and convert this energy into their own kinetic energy, and then the particles will exhibit a new motion pattern different from the conventional brownian motion, resulting in the particles exhibiting active brownian motion out of thermodynamic equilibrium. Therefore, the structural transformation and the dynamic behavior characteristics of the polymer chains in the self-driven particle bath become key research problems.
The application number is: the invention patent of CN201510112567.2 discloses a numerical simulation research method for processing abrasive particle flow based on molecular dynamics, wherein the method comprises the following steps: (1) Performing simulation research on the abrasive particle flow processing process based on molecular dynamics; (2) establishing a molecular dynamics model of micro cutting of abrasive particles; (3) The influence of related parameters on energy change in the processing process is discussed, and the quality of the processed surface is analyzed; (4) The effect of abrasive particle stream processing on the grain crystal structure was investigated on a microscopic scale. According to the invention, the micro-cutting process of a single abrasive particle in the abrasive particle flow machining process is researched and analyzed by using a molecular dynamics method, so that the calibration of the atomic displacement of the Newton layer of the workpiece in the abrasive particle micro-cutting process is realized; the simulation of the molecular dynamics of the abrasive particles is realized, the bond angles of the abrasive particles in the processing process are indicated to be changed, and a theoretical basis is laid for the subsequent study of the deformation of the abrasive particle crystal structure in the abrasive particle flow processing process.
However, the previous studies have focused more on the response of molecules to the surrounding environment and the change of the structure of the molecules themselves, and no clear physical mechanism has been obtained at present for the study of the diffusion behavior of dendrimers.
In view of the foregoing, there is a need for a method and system for simulating the structure of dendrimers in self-driven particle baths to solve at least the above-mentioned shortcomings.
Disclosure of Invention
The invention aims to provide a research simulation method and a system for a dendrimer structure in a self-driven particle bath, which are used for carrying out initial relaxation according to a first target model, a second target model and a first target parameter of a set simulation environment, determining a random model according to different initial relaxation results, introducing the second target parameter for further optimizing the accuracy and the reliability of the simulation result, acquiring the simulation result, facilitating the subsequent physical analysis of the simulation result and a theoretical result, and further exploring the physical mechanism of structural transformation and abnormal diffusion of dendrimers in the self-driven particle bath.
The research simulation method of the tree-shaped molecular structure in the self-driven particle bath provided by the embodiment of the invention comprises the following steps:
step 1: establishing a first target model of a polymer chain with a tree-shaped molecular structure and a second target model of self-driven particles;
Step 2: setting a first target parameter of the simulation environment based on LAMMPS;
step 3: initializing relaxation is carried out according to the first target parameter, the first target model and the second target model, and an initializing relaxation result is obtained;
step 4: determining a random model according to different initialized relaxation results;
step 5: and adjusting a second target parameter of the random model, and simulating to obtain a simulation result.
Preferably, step 1: establishing a first target model of a polymer chain of a dendrimer structure and a second target model of a self-driven particle, wherein the first target model comprises:
acquiring a first construction requirement of a polymer chain, and simultaneously acquiring a second construction requirement of a self-driven particle;
based on the LAMMPS, constructing a first target model according to a first construction requirement;
based on LAMMPS, a second target model is constructed according to a second construction requirement.
Preferably, step 3: according to the first target parameter, the first target model and the second target model, carrying out initial relaxation to obtain an initial relaxation result, wherein the method comprises the following steps:
initializing relaxation is carried out according to the first target parameter, the first target model and the second target model, and the overlapping condition of the self-driven particles and the energy overflow condition of the self-driven particles are obtained;
The overlap condition and the energy overflow condition are used together as an initialization relaxation result.
Preferably, step 4: determining a random model based on the difference in the initial relaxation results, comprising:
analyzing an initialization relaxation result, and acquiring an overlapping condition of the self-driven particles and an energy overflow condition of the self-driven particles;
if the overlapping condition is the self-driven particle overlapping or the energy overflow condition is the energy overflow, performing simulation failure attribution;
determining correction parameters and carrying out corresponding correction according to attribution results of simulation failure attribution;
obtaining a random model with corrected parameters subjected to corresponding correction;
if the overlapping condition is that the self-driven particles are not overlapped and the energy overflow condition is that the energy is not overflowed, judging whether a random model can be obtained;
if the random model can be obtained, outputting a corresponding random model;
if the random model cannot be obtained, continuing relaxation until a corresponding random model is obtained.
Preferably, determining the correction parameters and performing corresponding correction according to the attribution result of the simulation failure attribution comprises:
determining model parameters to be corrected according to attribution results;
based on a preset parameter scanning rule, performing parameter scanning on model parameters to acquire a plurality of parameter combinations determined by the parameter scanning;
According to the parameter combination, carrying out correction feasibility verification, obtaining a verification value, and correlating with the corresponding parameter combination;
performing correction simulation according to the parameter combination to obtain a simulation effect evaluation value, and correlating with the corresponding parameter combination;
summing the verification value and the simulation effect evaluation value associated with the calculation parameter combination to obtain a correction preferred value;
determining a parameter combination corresponding to the maximum correction optimal value and taking the parameter combination as a correction parameter;
and carrying out corresponding correction according to the correction parameters.
Preferably, step 5: adjusting a second target parameter of the random model and simulating to obtain a simulation result, wherein the method comprises the following steps:
obtaining a model parameter adjustment scheme library;
analyzing a model parameter adjustment scheme library to obtain a first simulation characteristic corresponding to a first model parameter adjustment scheme;
acquiring a second simulation characteristic of the random model;
performing feature matching on the first simulation feature and the second simulation feature to obtain a first model parameter adjustment scheme conforming to the feature matching, and taking the first model parameter adjustment scheme as a second model parameter adjustment scheme;
determining a second target parameter according to a second model parameter adjustment scheme;
and carrying out parameter adjustment on the random model according to the second target parameter, obtaining track coordinate file information output by the random model after parameter adjustment, and taking the track coordinate file information as a simulation result.
Preferably, obtaining a model parameter adjustment scheme library includes:
acquiring a plurality of first artificial model parameter adjustment records;
analyzing the first artificial model parameter adjustment record, and determining a first simulation environment parameter and a target time step;
setting a rationality analysis model based on a preset time step, and determining a time step setting reasonable value of a first artificial model parameter adjustment record according to a first simulation environment parameter and a target time step;
if the time step setting reasonable value is larger than or equal to a preset first threshold value, taking the corresponding first artificial model parameter adjustment record as a second artificial model parameter adjustment record;
analyzing the second artificial model parameter adjustment record, and determining a second simulation environment parameter and interaction potential energy setting;
based on a preset interaction potential rationality analysis model, determining interaction potential setting reasonable values of a second artificial model parameter adjustment record according to the second simulation environment parameters and the interaction potential setting;
if the interaction potential energy setting reasonable value is larger than or equal to a preset second threshold value, taking the corresponding second artificial model parameter adjustment record as a third artificial model parameter adjustment record;
analyzing a third manual model parameter adjustment record to obtain a third simulation characteristic of the model to be adjusted and a third model parameter adjustment scheme;
And (3) associating and pairing the third simulation feature and a third model parameter adjustment scheme corresponding to the third simulation feature, and storing the third model parameter adjustment scheme and the third model parameter adjustment scheme into a preset blank database to finish the acquisition of the model parameter adjustment scheme library.
The research simulation method of the tree-shaped molecular structure in the self-driven particle bath provided by the embodiment of the invention further comprises the following steps:
step 6: according to the simulation result, carrying out corresponding research;
wherein, step 6: according to the simulation result, carrying out corresponding research, including:
obtaining a comparison simulation result;
based on a preset data analysis tool, acquiring a data analysis result according to the simulation result and a comparison simulation result;
and uploading the data analysis result to a preset research node for corresponding research.
Preferably, based on a preset data analysis tool, obtaining a data analysis result according to the simulation result and a comparison simulation result includes:
acquiring a first result item of a simulation result and a second result item of a comparison simulation result;
determining a first result type of the first result item and a second result type of the second result item;
acquiring first-type description semantics of a first result type, and simultaneously acquiring second-type description semantics of a second result type;
acquiring semantic matching values of the first type description semantics and the second type description semantics, and taking the semantic matching values as comparison necessary values;
If the comparison necessary value is larger than or equal to a preset third threshold value, carrying out data analysis on the corresponding first result item and second result item through a data analysis tool to obtain an analysis sub-result;
obtaining result semantics of the analysis sub-result;
generating an associated literature index according to the result semantics;
acquiring a target abstract of an associated document corresponding to the index of the associated document;
analyzing the target abstract to obtain conclusion semantics;
determining conclusion content after conclusion semantics correspond to target content of the target abstract, and annotating the conclusion content based on a preset annotation rule;
the target abstract marked with the theoretical content is displayed in a related mode in a preset range around the corresponding related literature index;
and integrating all the analysis sub-results, the associated literature indexes corresponding to the analysis sub-results and the target abstracts corresponding to the associated literature indexes and marked with conclusion contents to obtain data analysis results.
The embodiment of the invention provides a research simulation system of a tree-shaped molecular structure in a self-driven particle bath, which comprises the following components:
the target model building subsystem is used for building a first target model of a polymer chain with a dendrimer structure and a second target model of self-driven particles;
the target parameter acquisition subsystem is used for setting a first target parameter of the simulation environment based on the LAMMPS;
The initialization relaxation subsystem is used for performing initialization relaxation according to the first target parameter, the first target model and the second target model to obtain an initialization relaxation result;
the random model determining subsystem is used for determining a random model according to the different initial relaxation results;
and the simulation result acquisition subsystem is used for adjusting the second target parameter of the random model and simulating to acquire a simulation result.
The beneficial effects of the invention are as follows:
according to the invention, based on the LAMMPS, initial relaxation is carried out according to the established first target model, the second target model and the set first target parameters of the simulation environment, and a random model is determined according to different initial relaxation results, so that the second target parameters are introduced for further optimizing the accuracy and reliability of the simulation results, the simulation results are acquired, the simulation results and the theoretical results are subjected to physical analysis, and the physical mechanism of structural transformation and abnormal diffusion of dendrimers in the self-driven particle bath is further explored.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a method for simulating the study of dendrimer structure in a self-driven particle bath in an embodiment of the invention;
FIG. 2 is a schematic diagram showing the evolution of the radius of gyration of dendrimers over time in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a system for simulating the study of dendrimer structure in a self-driven particle bath in accordance with an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The embodiment of the invention provides a research simulation method of a tree-shaped molecular structure in a self-driven particle bath, which is shown in figure 1 and comprises the following steps:
step 1: establishing a first target model of a polymer chain with a tree-shaped molecular structure and a second target model of self-driven particles; wherein the polymer chain is: long chain molecules formed by covalent bonding of a plurality of monomer molecules; the first object model is: a model of polymer chain modeling in molecular dynamics modeling software (e.g., LAMMPS); the self-driven particles are as follows: minute particles capable of autonomously generating motion and moving in an external environment; the second object model is: constructing a model of the self-driven particles in molecular dynamics simulation software;
Step 2: setting a first target parameter of the simulation environment based on LAMMPS; wherein, LAMMPS is a large-scale molecular dynamics simulation software; the simulation environment is as follows: computer environment for researching and simulating tree-shaped molecular structure in self-driven particle bath; the first target parameters are, for example: action potential energy parameters;
step 3: initializing relaxation is carried out according to the first target parameter, the first target model and the second target model, and an initializing relaxation result is obtained; wherein the initialization relaxation is: in molecular dynamics simulation, the initial structure of the molecule is given; the initial relaxation results were: the overlapping condition of the particles and the energy overflow condition of the self-driven particles are as follows: the simulated coincidence condition of the molecules, the energy overflow condition of the self-driven particles is as follows: whether the energy of the self-driven particles exceeds a critical value which can be accommodated in the environment where the self-driven particles are positioned;
step 4: determining a random model according to different initialized relaxation results; wherein, the random model is: in molecular dynamics simulation, reasonable initial structure of the molecule is given, such as: the initial structure of the molecules that do not overlap and energy does not spill over the particles;
step 5: adjusting a second target parameter of the random model and simulating to obtain a simulation result; although the initial relaxation can eliminate instability and unbalance in the initial configuration, so that the molecule reaches a relatively stable state, in actual molecular dynamics simulation, model parameters of the initial configuration still need to be adjusted to further optimize accuracy and reliability of a simulation result, wherein the second target parameters are: optimizing model parameters of an initial structure of the simulation; the simulation results were: final simulation results of dendrimer structure in self-driven particle bath, such as: coordinate information of the particle motion trail.
The working principle and the beneficial effects of the technical scheme are as follows:
the method is based on LAMMPS, performs initial relaxation according to the established first target model, the second target model and the set first target parameters of the simulation environment, determines a random model according to different initial relaxation results, introduces the second target parameters for further optimizing the accuracy and reliability of the simulation results, acquires the simulation results, facilitates the subsequent physical analysis of the simulation results and theoretical results, and further explores the structural transformation of dendrimers in the self-driven particle bath (as shown in fig. 2, wherein R g Represents the radius of gyration of the dendrimer,t represents time, t uf Indicating unfolding time, τ indicates time units) and the physical mechanism of abnormal diffusion.
When the method is specifically applied, simulation requirements of manual input of the LAMMPS software are obtained, the LAMMPS software respectively determines a first target model, a second target model and first target parameters for simulation according to the simulation requirements, and relevant technicians conduct research on physical mechanisms of structural transformation and abnormal diffusion of dendrimers in the self-driven particle bath according to simulation results.
In one embodiment, step 1: establishing a first target model of a polymer chain of a dendrimer structure and a second target model of a self-driven particle, wherein the first target model comprises:
Acquiring a first construction requirement of a polymer chain, and simultaneously acquiring a second construction requirement of a self-driven particle; the first construction requirement is as follows: modeling requirements for polymer chains, such as: a central atom or molecule, a structure of a branching unit, and the like; the second construction requirement is: a second build requirement for a model of self-driven particles, such as: particle type, interaction, etc.;
based on the LAMMPS, constructing a first target model according to a first construction requirement;
based on LAMMPS, a second target model is constructed according to a second construction requirement.
The working principle and the beneficial effects of the technical scheme are as follows:
the method and the device introduce the first construction requirement and the second construction requirement to respectively construct the first target model and the second target model, so that the construction accuracy of the first target model and the second target model is improved.
In one embodiment, step 3: according to the first target parameter, the first target model and the second target model, carrying out initial relaxation to obtain an initial relaxation result, wherein the method comprises the following steps:
initializing relaxation is carried out according to the first target parameter, the first target model and the second target model, and the overlapping condition of the self-driven particles and the energy overflow condition of the self-driven particles are obtained; wherein the initialization relaxation is: in molecular dynamics simulation, the initial structure of the molecule is given; the overlap of the particles is: the coincidence of the simulated molecules; the energy overflow condition of the self-driven particles is as follows: whether the energy of the self-driven particles exceeds a critical value which can be accommodated in the environment where the self-driven particles are positioned;
The overlap condition and the energy overflow condition are used together as an initialization relaxation result.
The working principle and the beneficial effects of the technical scheme are as follows:
the method and the device have the advantages that the overlapping condition and the energy overflow condition are used as the initial relaxation result together, so that the method and the device are more suitable.
In one embodiment, step 4: determining a random model based on the difference in the initial relaxation results, comprising:
analyzing an initialization relaxation result, and acquiring an overlapping condition of the self-driven particles and an energy overflow condition of the self-driven particles;
if the overlapping condition is the self-driven particle overlapping or the energy overflow condition is the energy overflow, performing simulation failure attribution; wherein, the failure of simulation is due to: the method comprises the steps of carrying out reason attribution on an initialization relaxation result of self-driven particle overlapping or energy overflow, inputting a simulation failure attribution model into a simulation failure attribution model during attribution, wherein the simulation failure attribution model is an intelligent model for replacing manual simulation failure reason attribution;
determining correction parameters and carrying out corresponding correction according to attribution results of simulation failure attribution; when the parameters are corrected, the parameters need to be reevaluated and corrected;
obtaining a random model with corrected parameters subjected to corresponding correction;
if the overlapping condition is that the self-driven particles are not overlapped and the energy overflow condition is that the energy is not overflowed, judging whether a random model can be obtained;
If the random model can be obtained, outputting a corresponding random model;
if the random model cannot be obtained, continuing relaxation until a corresponding random model is obtained;
in the stochastic model, the equation of motion of the self-driven particles is as follows:
wherein p is i Representing the position of the ith self-driven particle, μ represents the damping coefficient of translational motion,is the orientation of the ith self-driven particle, θ i Represents the orientation angle of the ith self-driven particle, E i (t) translational noise, ω, of the ith self-driven particle i (t) is the rotation noise of the ith self-driven particle, and both the translation noise and the rotation noise are Gaussian white noise and satisfy the fluctuation dissipation theorem, U (p) represents the interaction energy of the ith self-driven particle, F a Is the active force acting on each self-driven particle ρ t The translational diffusion coefficient ρ of the ith self-driven particle r Is the rotational diffusion coefficient of the ith self-driven particle.
The working principle and the beneficial effects of the technical scheme are as follows:
according to the method, the device and the system, the analysis initialization relaxation result is obtained from the overlapping condition of the self-driven particles and the energy overflow condition of the self-driven particles, simulation failure attribution is carried out when the overlapping condition is the overlapping of the self-driven particles or the energy overflow condition is the energy overflow, correction parameters are determined according to attribution results attributed to the simulation failure to be corrected, if the overlapping condition is the non-overlapping energy overflow condition of the self-driven particles, the random model is obtained, and the acquisition rationality of the random model is improved.
In one embodiment, determining the correction parameters and making corresponding corrections based on the attribution results of the simulation failure attribution comprises:
determining model parameters to be corrected according to attribution results; wherein, the model parameters are: attributing to determining model parameters to be corrected;
based on a preset parameter scanning rule, performing parameter scanning on model parameters to acquire a plurality of parameter combinations determined by the parameter scanning; the preset parameter scanning rule is as follows: which parameter is scanned first, and which parameter is scanned again; the parameter combinations are as follows: a combination of model parameters of different parameter types;
according to the parameter combination, carrying out correction feasibility verification, obtaining a verification value, and correlating with the corresponding parameter combination; the correction feasibility verification is as follows: verifying whether corresponding parameters in the parameter combination can be adjusted to corresponding values; the higher the verification value is, the more feasible the parameter correction scheme corresponding to the corresponding parameter combination is;
performing correction simulation according to the parameter combination to obtain a simulation effect evaluation value, and correlating with the corresponding parameter combination; wherein, the correction simulation is: setting a simulation model and adjusting model parameters to corresponding values of parameter combinations; the higher the simulation effect evaluation value is, the better the simulation effect of the corresponding simulation model is;
Summing the verification value and the simulation effect evaluation value associated with the calculation parameter combination to obtain a correction preferred value;
determining a parameter combination corresponding to the maximum correction optimal value and taking the parameter combination as a correction parameter;
and carrying out corresponding correction according to the correction parameters. When corresponding correction is carried out according to the correction parameters, the numerical value corresponding to the correction parameters is adjusted and corrected by the model parameters.
The working principle and the beneficial effects of the technical scheme are as follows:
generally, when simulation fails, parameters need to be revised again, but the blind parameter correction efficiency is very low, so that the parameter scanning rule is introduced, the parameter scanning is performed on the model parameters which are required to be corrected due to determination, a plurality of parameter combinations are obtained, feasibility verification and correction simulation are respectively performed on the parameter combinations, correction optimal values of the parameter combinations are determined, correction is performed according to the parameter combinations corresponding to the maximum correction optimal values, the correction process is more accurate, and the correction efficiency is higher.
In one embodiment, step 5: adjusting a second target parameter of the random model and simulating to obtain a simulation result, wherein the method comprises the following steps:
obtaining a model parameter adjustment scheme library; the model parameter adjustment scheme library comprises the following steps: a database formed by a model parameter adjustment scheme for manually further adjusting model parameters of an initial configuration to obtain a more accurate and reliable simulation result;
Analyzing a model parameter adjustment scheme library to obtain a first simulation characteristic corresponding to a first model parameter adjustment scheme; wherein the first simulation feature is: the process characteristics of the simulation process corresponding to the first model parameter adjustment scheme, for example: what hardware supports and what software supports;
acquiring a second simulation characteristic of the random model; wherein the second simulation feature is: process characteristics when the stochastic model performs simulation, such as: what hardware supports and what software supports;
performing feature matching on the first simulation feature and the second simulation feature to obtain a first model parameter adjustment scheme conforming to the feature matching, and taking the first model parameter adjustment scheme as a second model parameter adjustment scheme;
determining a second target parameter according to a second model parameter adjustment scheme; wherein the second target parameters are: model adjustment parameters of the random model determined according to the parameter adjustment logic of the second model parameter adjustment scheme;
and carrying out parameter adjustment on the random model according to the second target parameter, obtaining track coordinate file information output by the random model after parameter adjustment, and taking the track coordinate file information as a simulation result. The track coordinate file information comprises track coordinate files of self-driven particles and track coordinate files of dendrimers.
The working principle and the beneficial effects of the technical scheme are as follows:
according to the method, a model parameter adjustment scheme library is introduced, a first simulation feature corresponding to the first model parameter adjustment scheme is determined, meanwhile, a second simulation feature of the random model is obtained, feature matching is conducted on the first simulation feature and the second simulation feature, the feature matching accords with the second model parameter adjustment scheme, a second target parameter of the random model is determined according to parameter adjustment logic of the second model parameter adjustment scheme, parameter adjustment of the random model is conducted according to the second target parameter, track coordinate file information output by the random model after parameter adjustment is used as a simulation result, and accuracy of the simulation result is improved.
In one embodiment, obtaining a library of model parameter tuning schemes includes:
acquiring a plurality of first artificial model parameter adjustment records; wherein, the first artificial model parameter adjustment record is: manually further adjusting model parameters of the initial configuration to obtain a history record of more accurate and reliable simulation results;
analyzing the first artificial model parameter adjustment record, and determining a first simulation environment parameter and a target time step; the first simulation environment parameters are as follows: the first artificial model parameter adjustment records the corresponding recorded simulation environment parameters; the target time step is: the discretization interval of time in simulation, the simulation precision is affected when the target time step is too large, and the calculation time and the resource consumption are increased when the target time step is too small;
Setting a rationality analysis model based on a preset time step, and determining a time step setting reasonable value of a first artificial model parameter adjustment record according to a first simulation environment parameter and a target time step; the preset time step is set as a rationality analysis model, which is as follows: the preset substitution expert determines an intelligent model of reasonable degree of setting of the target time step according to the simulation environment parameters and the time step;
if the time step setting reasonable value is larger than or equal to a preset first threshold value, taking the corresponding first artificial model parameter adjustment record as a second artificial model parameter adjustment record; the preset first threshold value is preset manually;
analyzing the second artificial model parameter adjustment record, and determining a second simulation environment parameter and interaction potential energy setting; wherein the interaction potential energy is set as: in the second artificial model parameter adjustment record, manually set interaction potential energy parameters;
based on a preset interaction potential rationality analysis model, determining interaction potential setting reasonable values of a second artificial model parameter adjustment record according to the second simulation environment parameters and the interaction potential setting; the interaction potential energy rationality analysis model is: the preset intelligent model is used for replacing an expert to determine the reasonable degree of interaction potential energy setting according to the simulation environment parameters and the interaction potential energy setting;
If the interaction potential energy setting reasonable value is larger than or equal to a preset second threshold value, taking the corresponding second artificial model parameter adjustment record as a third artificial model parameter adjustment record; wherein the preset second threshold value is preset manually;
analyzing a third manual model parameter adjustment record to obtain a third simulation characteristic of the model to be adjusted and a third model parameter adjustment scheme; wherein, the model to be regulated is: corresponding preparations in the third artificial model parameter adjustment record are further adjusted to obtain an initial configuration of a more accurate and reliable simulation result; the third simulation feature is: process characteristics to be adjusted by the adjustment model, such as: what hardware supports and what software supports; the third model parameter adjustment scheme is as follows: a model parameter adjustment scheme of the model to be adjusted, which is recorded correspondingly in the third manual model parameter adjustment record;
and (3) associating and pairing the third simulation feature and a third model parameter adjustment scheme corresponding to the third simulation feature, and storing the third model parameter adjustment scheme and the third model parameter adjustment scheme into a preset blank database to finish the acquisition of the model parameter adjustment scheme library. The preset blank database is as follows: and (5) an empty database.
The working principle and the beneficial effects of the technical scheme are as follows:
The method comprises the steps of establishing a model parameter adjustment scheme library, analyzing acquired first artificial model parameter adjustment records, determining first simulation environment parameters and target time steps, setting a rationality analysis model according to the introduced time steps, determining time step reasonable setting values of the first artificial model parameter adjustment records, improving accuracy of determining the time step reasonable setting values, acquiring second artificial model parameter adjustment records with the time step reasonable setting values being larger than or equal to a preset first threshold value, introducing interaction potential energy rationality analysis models, determining interaction potential energy reasonable setting values of the second artificial model parameter adjustment records according to second simulation environment parameters and interaction potential energy obtained by analyzing the second artificial model parameter adjustment records, acquiring third artificial model parameter adjustment records with the interaction potential energy reasonable setting values being larger than or equal to a second threshold value, intelligently determining interaction potential energy reasonable setting values, finally analyzing the third artificial model parameter adjustment records, storing the acquired third simulation characteristics and the third model parameter adjustment scheme into an empty database to obtain a model parameter adjustment scheme library, and enabling the construction process to be more reasonable.
The embodiment of the invention provides a research simulation method of a tree-shaped molecular structure in a self-driven particle bath, which further comprises the following steps:
step 6: according to the simulation result, carrying out corresponding research; when corresponding research is carried out, theoretical results can be subjected to physical analysis and compared with the existing experimental data and simulation results so as to improve a model and deepen physical knowledge;
wherein, step 6: according to the simulation result, carrying out corresponding research, including:
obtaining a comparison simulation result; wherein, the comparison simulation result is: theoretical results;
based on a preset data analysis tool, acquiring a data analysis result according to the simulation result and a comparison simulation result; the preset data analysis tool is, for example: python and C language;
and uploading the data analysis result to a preset research node for corresponding research. Wherein, the preset research node is: and carrying out communication nodes of researchers with the dendrimer structure in the self-driven particle bath.
The working principle and the beneficial effects of the technical scheme are as follows:
according to the method, the control simulation result and the data analysis tool are introduced to conduct data analysis, the data analysis result is obtained, and the data analysis result is uploaded in real time for research, so that the analysis is more intelligent and more accurate.
In one embodiment, based on a preset data analysis tool, obtaining a data analysis result according to a simulation result and a comparison simulation result includes:
acquiring a first result item of a simulation result and a second result item of a comparison simulation result; wherein, the first result item is: results content items in the simulation results, such as: the radius of gyration, shape factor and mean square displacement of the dendrimer, and the density of the self-driven particles; the second result item is: comparing the result content items in the simulation result, such as: the radius of gyration, shape factor and mean square displacement of the dendrimer, and the density of the self-driven particles;
determining a first result type of the first result item and a second result type of the second result item; wherein the first result type is: the category of the first result item; the second result type is: a category of the second outcome type;
acquiring first-type description semantics of a first result type, and simultaneously acquiring second-type description semantics of a second result type; wherein, the first type of description semantics are: the semantics of the first result type are described, and the determination can be realized based on the semantic analysis technology; the second type of descriptive semantics are: semantics of the second result type;
Acquiring semantic matching values of the first type description semantics and the second type description semantics, and taking the semantic matching values as comparison necessary values; the semantic matching value characterizes the semantic matching degree of the first type description semantics and the second type description semantics, and the semantic matching belongs to the category of the prior art and is not repeated;
if the comparison necessary value is larger than or equal to a preset third threshold value, carrying out data analysis on the corresponding first result item and second result item through a data analysis tool to obtain an analysis sub-result; wherein the preset third threshold value is preset manually;
obtaining result semantics of the analysis sub-result; the result semantics are, for example: as the motion capability of the self-driven particles is enhanced, the spontaneous phase separation speed of the dendrimers is increased;
generating an associated literature index according to the result semantics; wherein, the associated literature index is: index corresponding to documents that are semantically related to the result, such as: index of literature of intermolecular interactions of active substances with polymers therein;
acquiring a target abstract of an associated document corresponding to the index of the associated document; the target abstract is quickly positioned and obtained by searching the catalog of the related literature;
analyzing the target abstract to obtain conclusion semantics; the conclusion semantics are, for example: … is improved, … is reduced, … is increased, … is reduced and the like;
Determining conclusion content after conclusion semantics correspond to target content of the target abstract, and annotating the conclusion content based on a preset annotation rule; the target content is summary content with conclusion semantics corresponding to the target abstract; the conclusion content is: summary content following the target content; the preset labeling rules are preset manually, for example: labeling with yellow;
the target abstract marked with the theoretical content is displayed in a related mode in a preset range around the corresponding related literature index; the surrounding preset range is as follows: within 10 cm; the association is shown as: connecting the target abstract marked with the theoretical content with the corresponding associated document index by using a dotted line to mark the association relation of the target abstract and the associated document index;
and integrating all the analysis sub-results, the associated literature indexes corresponding to the analysis sub-results and the target abstracts corresponding to the associated literature indexes and marked with conclusion contents to obtain data analysis results.
The working principle and the beneficial effects of the technical scheme are as follows:
generally, after analyzing the first result item and the second result item to obtain an analysis sub-result, researchers often analyze through the analysis sub-result retrieval document, and each time the manual retrieval document is required, the manual retrieval document is not humanized or intelligent enough, so that the application obtains the first result type of the first result item and the second result type of the second result item, determines the second type description semantic of the second result type according to the first type description semantic of the first result type and the second type description semantic of the second result type, and uses the second type description semantic of the second result type as a comparison necessary value. Determining conclusion semantics of target summaries of associated documents corresponding to the associated document indexes, labeling the target summaries of the associated documents corresponding to the associated document indexes based on the introduced labeling rules, obtaining labeling conclusion contents, and displaying the target summaries of the labeling conclusion contents in an associated mode within a preset range around the corresponding associated document indexes, so that the method is more humanized.
The embodiment of the invention provides a research simulation system of a tree-shaped molecular structure in a self-driven particle bath, which is shown in fig. 3 and comprises the following components:
the target model building subsystem 1 is used for building a first target model of a polymer chain with a dendrimer structure and a second target model of self-driven particles;
a target parameter obtaining subsystem 2, configured to set a first target parameter of the simulation environment based on LAMMPS;
the initialization relaxation subsystem 3 is used for performing initialization relaxation according to the first target parameter, the first target model and the second target model to obtain an initialization relaxation result;
a random model determining subsystem 4 for determining a random model according to the difference of the initial relaxation results;
and the simulation result acquisition subsystem 5 is used for adjusting the second target parameters of the random model and simulating to acquire a simulation result.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. The research simulation method of the tree-shaped molecular structure in the self-driven particle bath is characterized by comprising the following steps of:
Step 1: establishing a first target model of a polymer chain with a tree-shaped molecular structure and a second target model of self-driven particles;
step 2: setting a first target parameter of the simulation environment based on LAMMPS;
step 3: initializing relaxation is carried out according to the first target parameter, the first target model and the second target model, and an initializing relaxation result is obtained;
step 4: determining a random model according to different initialized relaxation results;
step 5: adjusting a second target parameter of the random model and simulating to obtain a simulation result;
step 4: determining a random model based on the difference in the initial relaxation results, comprising:
analyzing an initialization relaxation result, and acquiring an overlapping condition of the self-driven particles and an energy overflow condition of the self-driven particles;
if the overlapping condition is the self-driven particle overlapping or the energy overflow condition is the energy overflow, performing simulation failure attribution;
determining correction parameters and carrying out corresponding correction according to attribution results of simulation failure attribution;
obtaining a random model with corrected parameters subjected to corresponding correction;
if the overlapping condition is that the self-driven particles are not overlapped and the energy overflow condition is that the energy is not overflowed, judging whether a random model can be obtained;
if the random model can be obtained, outputting a corresponding random model;
If the random model cannot be obtained, continuing relaxation until a corresponding random model is obtained.
2. The method for simulating the study of the dendrimer structure in a self-driven particle bath according to claim 1, wherein the following steps are carried out: establishing a first target model of a polymer chain of a dendrimer structure and a second target model of a self-driven particle, wherein the first target model comprises:
acquiring a first construction requirement of a polymer chain, and simultaneously acquiring a second construction requirement of a self-driven particle;
based on the LAMMPS, constructing a first target model according to a first construction requirement;
based on LAMMPS, a second target model is constructed according to a second construction requirement.
3. The method for simulating the study of the dendrimer structure in a self-driven particle bath according to claim 1, wherein the following step 3: according to the first target parameter, the first target model and the second target model, carrying out initial relaxation to obtain an initial relaxation result, wherein the method comprises the following steps:
initializing relaxation is carried out according to the first target parameter, the first target model and the second target model, and the overlapping condition of the self-driven particles and the energy overflow condition of the self-driven particles are obtained;
the overlap condition and the energy overflow condition are used together as an initialization relaxation result.
4. The method for simulating the study of a dendrimer structure in a self-driven particle bath according to claim 1, wherein determining the correction parameters and performing the corresponding correction according to the attribution result attributed to the simulation failure comprises:
determining model parameters to be corrected according to attribution results;
based on a preset parameter scanning rule, performing parameter scanning on model parameters to acquire a plurality of parameter combinations determined by the parameter scanning;
according to the parameter combination, carrying out correction feasibility verification, obtaining a verification value, and correlating with the corresponding parameter combination;
performing correction simulation according to the parameter combination to obtain a simulation effect evaluation value, and correlating with the corresponding parameter combination;
summing the verification value and the simulation effect evaluation value associated with the calculation parameter combination to obtain a correction preferred value;
determining a parameter combination corresponding to the maximum correction optimal value and taking the parameter combination as a correction parameter;
and carrying out corresponding correction according to the correction parameters.
5. The method for simulating the study of dendrimer structures in a self-driven particle bath according to claim 1, wherein the step 5: adjusting a second target parameter of the random model and simulating to obtain a simulation result, wherein the method comprises the following steps:
obtaining a model parameter adjustment scheme library;
Analyzing a model parameter adjustment scheme library to obtain a first simulation characteristic corresponding to a first model parameter adjustment scheme;
acquiring a second simulation characteristic of the random model;
performing feature matching on the first simulation feature and the second simulation feature to obtain a first model parameter adjustment scheme conforming to the feature matching, and taking the first model parameter adjustment scheme as a second model parameter adjustment scheme;
determining a second target parameter according to a second model parameter adjustment scheme;
and carrying out parameter adjustment on the random model according to the second target parameter, obtaining track coordinate file information output by the random model after parameter adjustment, and taking the track coordinate file information as a simulation result.
6. The method for simulating the study of a dendrimer structure in a self-driven particle bath according to claim 5, wherein obtaining a model parameter adjustment scheme library comprises:
acquiring a plurality of first artificial model parameter adjustment records;
analyzing the first artificial model parameter adjustment record, and determining a first simulation environment parameter and a target time step;
setting a rationality analysis model based on a preset time step, and determining a time step setting reasonable value of a first artificial model parameter adjustment record according to a first simulation environment parameter and a target time step;
If the time step setting reasonable value is larger than or equal to a preset first threshold value, taking the corresponding first artificial model parameter adjustment record as a second artificial model parameter adjustment record;
analyzing the second artificial model parameter adjustment record, and determining a second simulation environment parameter and interaction potential energy setting;
based on a preset interaction potential rationality analysis model, determining interaction potential setting reasonable values of a second artificial model parameter adjustment record according to the second simulation environment parameters and the interaction potential setting;
if the interaction potential energy setting reasonable value is larger than or equal to a preset second threshold value, taking the corresponding second artificial model parameter adjustment record as a third artificial model parameter adjustment record;
analyzing a third manual model parameter adjustment record to obtain a third simulation characteristic of the model to be adjusted and a third model parameter adjustment scheme;
and (3) associating and pairing the third simulation feature and a third model parameter adjustment scheme corresponding to the third simulation feature, and storing the third model parameter adjustment scheme and the third model parameter adjustment scheme into a preset blank database to finish the acquisition of the model parameter adjustment scheme library.
7. The method for simulating the study of dendrimer structures in a self-driven particle bath according to claim 1, further comprising:
Step 6: according to the simulation result, carrying out corresponding research;
wherein, step 6: according to the simulation result, carrying out corresponding research, including:
obtaining a comparison simulation result;
based on a preset data analysis tool, acquiring a data analysis result according to the simulation result and a comparison simulation result;
and uploading the data analysis result to a preset research node for corresponding research.
8. The method for simulating the study of a dendrimer structure in a self-driven particle bath according to claim 7, wherein the step of obtaining the data analysis result from the simulation result and the comparison simulation result based on the preset data analysis tool comprises:
acquiring a first result item of a simulation result and a second result item of a comparison simulation result;
determining a first result type of the first result item and a second result type of the second result item;
acquiring first-type description semantics of a first result type, and simultaneously acquiring second-type description semantics of a second result type;
acquiring semantic matching values of the first type description semantics and the second type description semantics, and taking the semantic matching values as comparison necessary values;
if the comparison necessary value is larger than or equal to a preset third threshold value, carrying out data analysis on the corresponding first result item and second result item through a data analysis tool to obtain an analysis sub-result;
Obtaining result semantics of the analysis sub-result;
generating an associated literature index according to the result semantics;
acquiring a target abstract of an associated document corresponding to the index of the associated document;
analyzing the target abstract to obtain conclusion semantics;
determining conclusion content after conclusion semantics correspond to target content of the target abstract, and annotating the conclusion content based on a preset annotation rule;
the target abstract marked with the theoretical content is displayed in a related mode in a preset range around the corresponding related literature index;
and integrating all the analysis sub-results, the associated literature indexes corresponding to the analysis sub-results and the target abstracts corresponding to the associated literature indexes and marked with conclusion contents to obtain data analysis results.
9. The research simulation system of the tree-shaped molecular structure in the self-driven particle bath is characterized by comprising the following components:
the target model building subsystem is used for building a first target model of a polymer chain with a dendrimer structure and a second target model of self-driven particles;
the target parameter acquisition subsystem is used for setting a first target parameter of the simulation environment based on the LAMMPS;
the initialization relaxation subsystem is used for performing initialization relaxation according to the first target parameter, the first target model and the second target model to obtain an initialization relaxation result;
The random model determining subsystem is used for determining a random model according to the different initial relaxation results;
the simulation result acquisition subsystem is used for adjusting a second target parameter of the random model and simulating to acquire a simulation result;
the stochastic model determination subsystem performs the following operations:
analyzing an initialization relaxation result, and acquiring an overlapping condition of the self-driven particles and an energy overflow condition of the self-driven particles;
if the overlapping condition is the self-driven particle overlapping or the energy overflow condition is the energy overflow, performing simulation failure attribution;
determining correction parameters and carrying out corresponding correction according to attribution results of simulation failure attribution;
obtaining a random model with corrected parameters subjected to corresponding correction;
if the overlapping condition is that the self-driven particles are not overlapped and the energy overflow condition is that the energy is not overflowed, judging whether a random model can be obtained;
if the random model can be obtained, outputting a corresponding random model;
if the random model cannot be obtained, continuing relaxation until a corresponding random model is obtained.
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