A high-throughput computational method, apparatus, equipment, and storage medium for grafted polymers based on molecular dynamics simulations.

Abstract

This application discloses a high-throughput computational method, apparatus, device, and storage medium for grafted polymers based on molecular dynamics simulation, belonging to the field of computational chemistry technology, and capable of solving the problem of the inability to quickly perform simulation calculations of grafted polymers. The method includes: receiving first parameters of at least one grafted polymer to be simulated; generating a corresponding initial model based on the first parameters, wherein the first parameters include at least the main chain length, side chain length, and grafting density; performing molecular dynamics simulation on the initial model according to pre-configured molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer; determining second parameters of the grafted polymer based on the evolution trajectory, wherein the second parameters include at least the mean square radius of gyration and the mean square end-to-end distance of the main chain; and generating corresponding analysis results based on the first and second parameters, wherein the analysis results include at least the parameter combination corresponding to the grafted polymer and a scatter plot corresponding to the parameter combination.

Description

Technical Field

[0001] This application belongs to the field of computational chemistry technology, specifically relating to a high-throughput computational method, apparatus, equipment, and storage medium for grafted polymers based on molecular dynamics simulation. Background Technology

[0002] Graft polymers are important polymer materials with broad application prospects in areas such as interface modification, self-assembly materials, drug carriers, and nanocomposites. Understanding the structure-property relationship between the structural parameters and macroscopic properties of graft polymers at the molecular scale is crucial for the rational design of novel functional polymer materials. In current techniques, researchers typically use manual or semi-automatic methods to simulate the microstructure and dynamic behavior of graft polymers, thereby obtaining detailed information at the molecular chain scale that is difficult to observe directly experimentally, such as parameters like chain segment motion, interfacial interactions, and molecular chain configuration.

[0003] In existing technologies, the microstructure and dynamic behavior of grafted polymers are simulated manually or semi-automatically. This approach has a long working cycle and low computational efficiency, and cannot quickly construct a structure-property relationship map covering a broad parameter space, which greatly limits the speed of grafted polymer material development. Furthermore, due to the fragmented computational workflow, simulation conditions and data analysis methods are difficult to unify and reproduce across different studies, affecting the reliability and comparability of the results. Summary of the Invention

[0004] The purpose of this application is to provide a high-throughput calculation method, apparatus, device, and storage medium for grafted polymers based on molecular dynamics simulation, which can solve the problem of the inability to quickly perform simulation calculations of grafted polymers.

[0005] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a high-throughput calculation method for grafted polymers based on molecular dynamics simulations, the method comprising: Receive at least one first parameter of the grafted polymer to be simulated, and generate a corresponding initial model based on the first parameter, wherein the first parameter includes at least the main chain length, the side chain length, and the grafting density. Based on the pre-configured molecular dynamics parameters, the initial model is subjected to molecular dynamics simulation to obtain the evolution trajectory of the grafted polymer; Based on the evolution trajectory, a second parameter of the grafted polymer is determined, the second parameter including at least the mean square radius of gyration and the mean square end-to-end distance of the main chain; Based on the first parameter and the second parameter, corresponding analysis results are generated. The analysis results include at least the parameter combination corresponding to the grafted polymer and the scatter plot corresponding to the parameter combination.

[0006] Optionally, generating the corresponding initial model based on the first parameter includes: The arrangement of the main chain and side chains of the grafted polymer is determined according to a preset sequence; The chemical bonds and connection positions of the grafted polymer are determined according to a preset connection rule; Based on the arrangement, chemical bonds, and connection positions, the main chain and side chains set in the first parameter are combined to obtain the initial model.

[0007] Optionally, the step of performing molecular dynamics simulations on the initial model based on pre-configured molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer includes: Create a corresponding file for the initial model; Add the corresponding calculation files and the files corresponding to the molecular dynamics parameters to the directory where the folder is located to form the corresponding calculation task; Submit the computation task to the task queue; If the cluster resources meet the computing resource requirements of the computing task, the computing task is executed to obtain the evolution trajectory of the grafted polymer.

[0008] Optionally, determining the second parameter of the grafted polymer based on the evolution trajectory includes: The evolution trajectory is sampled at preset sampling intervals; Based on the sampled graft polymer molecular structure, the mean square radius of gyration and the mean square end-to-end distance of the main chain of the graft polymer molecule are determined. Determine the standard deviation and average value of the mean square radius of gyration and the mean square end distance of the main chain.

[0009] Optionally, the method further includes: The evolution trajectory is analyzed to determine the radial distribution function and chain segment sequence parameters between the main chain and side chains of the grafted polymer.

[0010] Optionally, generating the corresponding analysis result based on the first parameter and the second parameter includes: Based on the first parameter and the second parameter, a corresponding data table is generated, and each row of the data table represents a parameter combination; Based on the first parameter and the second parameter, a corresponding scatter plot is generated.

[0011] Optionally, the method further includes: The first parameter and the second parameter are used as training data to train the machine learning model, and a trained machine learning model is obtained. The parameters of the grafted polymer are predicted using the machine learning model to obtain the corresponding prediction results. Based on the prediction results, a corresponding prediction scatter plot is generated.

[0012] Secondly, embodiments of this application provide a high-throughput grafted polymer computing device based on molecular dynamics simulations, the device comprising: The model building module is used to receive at least one first parameter of the grafted polymer to be simulated and generate a corresponding initial model based on the first parameter. The first parameter includes at least the main chain length, side chain length and grafting density. The simulation analysis module is used to perform molecular dynamics simulation on the initial model according to the pre-configured molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer; The data analysis module is used to determine a second parameter of the grafted polymer based on the evolution trajectory, wherein the second parameter includes at least the mean square radius of gyration and the mean square end-to-end distance of the main chain; The result output module is used to generate corresponding analysis results based on the first parameter and the second parameter. The analysis results include at least the parameter combination corresponding to the grafted polymer and the scatter plot corresponding to the parameter combination.

[0013] Optionally, the model building module includes: The sequence determination submodule is used to determine the arrangement of the main chain and side chains of the grafted polymer according to a preset sequence. The connection rule determination submodule is used to determine the chemical bonds and connection positions of the grafted polymer according to preset connection rules; The initial model construction submodule is used to combine the main chain and side chains set in the first parameter according to the arrangement, chemical bonds and connection positions to obtain the initial model.

[0014] Optionally, the simulation analysis module includes: The file creation submodule is used to create corresponding files for the initial model; The computation task construction submodule is used to add the corresponding computation files and the files corresponding to the molecular dynamics parameters to the directory where the folder is located, thereby forming the corresponding computation task. The task submission submodule is used to submit the computing task to the task queue; The evolution trajectory acquisition submodule is used to execute the computing task and obtain the evolution trajectory of the grafted polymer when the cluster resource status meets the computing resources requested by the computing task.

[0015] Optionally, the data analysis module includes: The sampling submodule is used to sample the evolution trajectory at preset sampling intervals. The parameter calculation submodule is used to determine the mean square radius of gyration and the mean square end-to-end distance of the main chain of the grafted polymer molecule based on the sampled grafted polymer molecular structure. The mean calculation submodule is used to determine the standard deviation and average value of the mean square radius of gyration and the mean square end distance of the main chain.

[0016] Optionally, the device further includes: The trajectory analysis submodule is used to analyze the evolution trajectory and determine the radial distribution function and chain segment sequence parameters between the main chain and side chains of the grafted polymer.

[0017] Optionally, the result output module includes: The data table generation submodule is used to generate a corresponding data table based on the first parameter and the second parameter, wherein each row of the data table represents a parameter combination; The first scatter plot generation submodule is used to generate a corresponding scatter plot based on the first parameter and the second parameter.

[0018] Optionally, the device further includes: The model training submodule is used to train the machine learning model using the first parameter and the second parameter as training data to obtain a trained machine learning model. The result prediction submodule is used to predict the parameters of the grafted polymer using the machine learning model and obtain the corresponding prediction results. The second scatter plot generation submodule is used to generate a corresponding prediction scatter plot based on the prediction results.

[0019] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0020] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0021] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0022] The high-throughput grafted polymer calculation method based on molecular dynamics simulation provided in this application receives at least one first parameter of a grafted polymer to be simulated, generates a corresponding initial model based on the first parameter, the first parameter including at least main chain length, side chain length, and grafting density; performs molecular dynamics simulation on the initial model according to pre-configured molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer; determines a second parameter of the grafted polymer based on the evolution trajectory, the second parameter including at least the mean square radius of gyration and the mean square end-to-end distance of the main chain; and generates corresponding analysis results based on the first parameter and the second parameter, the analysis results including at least the parameter combination corresponding to the grafted polymer and the scatter plot corresponding to the parameter combination.

[0023] This method involves inputting parameters of the grafted polymer, simulating the grafted polymer to generate an initial model, performing molecular dynamics simulations to obtain its evolution trajectory, analyzing the trajectory to derive corresponding parameters, and generating analysis results. This automates the parameter setting and result waiting process, improving research efficiency. It allows for simultaneous simulation calculations of multiple parameters, further enhancing efficiency. High-precision simulation and analysis of grafted polymers are achieved. Attached Figure Description

[0024] Figure 1 This is a flowchart of a high-throughput grafted polymer calculation method based on molecular dynamics simulation proposed in an embodiment of this application; Figure 2 This is a data processing flowchart proposed in one embodiment of this application; Figure 3 This is a two-dimensional scatter plot proposed in one embodiment of this application; Figure 4 This is a schematic diagram of a high-throughput grafted polymer computing device based on molecular dynamics simulation proposed in an embodiment of this application; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0025] 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.

[0026] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0027] In this embodiment, the high-throughput graft polymer computation method based on molecular dynamics simulation is implemented using a high-throughput graft polymer computation system based on brain-intensive dynamics simulation. The graft polymer analysis is performed in a high-performance computer cluster, with multiple computing nodes set up, using high-performance processor cores and large-capacity memory, and managed through a unified job management system, enabling simultaneous simulation analysis of multiple graft polymers with different structures.

[0028] The system includes a parameterized model building unit, implemented in C++, which stores key structural parameters of the grafted polymer, including the number of main-chain monomers (N). bb ), number of side chain monomers (N) sc ) and grafting density (N) g These parameters serve as adjustable input variables. Based on these parameters, the program automatically generates an initial 3D model of the grafted polymer with a precisely controllable topology, along with the corresponding LAMMPS data file, the input file for molecular dynamics simulations. Then, the task calculation file is written according to the requirements.

[0029] The automated task management and scheduling unit receives computational tasks from the model building unit, intelligently allocates computing resources, executes them in batches and in parallel, submits the tasks to the supercomputing queue, monitors task status in real time, and triggers subsequent data analysis processes upon task completion. This unit supports a parameter scanning mode, which can scan parameter files, automatically generate computational tasks, and submit them.

[0030] An automated data sampling and extraction unit, after molecular dynamics simulations are completed, efficiently and purposefully analyzes the resulting large-scale trajectory files using a pre-configured program. It can automatically identify and extract a series of key microstructural parameters.

[0031] The intelligent data computing and processing unit receives data extracted from upstream modules and uses data analysis libraries such as pandas and numpy to perform data cleaning, aggregation, and statistical testing. It uses the massive data generated by high-throughput computing as a training set to build machine learning models to predict the polymer performance of new structural parameters. It also uses libraries such as matplotlib and seaborn to automatically generate publication-quality charts to intuitively display the "structure-performance" relationship.

[0032] The following section will describe in detail the execution process of each unit of the system in conjunction with the system's execution process.

[0033] refer to Figure 1 , Figure 1 This is a flowchart of a high-throughput grafted polymer calculation method based on molecular dynamics simulation proposed in an embodiment of this application, as shown below. Figure 1 As shown, the method specifically includes the following steps: S11: Receive at least one first parameter of the grafted polymer to be simulated, and generate a corresponding initial model based on the first parameter, wherein the first parameter includes at least the main chain length, the side chain length, and the grafting density.

[0034] In this embodiment, the first parameter includes at least the main chain length, side chain length, and grafting density of the grafted polymer.

[0035] In this embodiment, the system's parameterized model building unit receives first parameters from multiple grafted polymers to be simulated. Based on the main chain length, side chain length, and grafting density specified in the first parameters, and according to a preset sequence and connection rules, it generates an initial model of the corresponding grafted polymer in three-dimensional space. The generated initial model is then placed into a cubic simulation box to generate a corresponding model file. Simultaneously, a corresponding calculation file is configured for this initial model to provide computational requirements for subsequent molecular dynamics simulations.

[0036] For example, the parameters that can be set in the first parameter for the grafted polymer include: main chain length (N) bb : 31 monomers, side chain length (N) sc : 4, 7, 10, 12, 15 monomers, grafting density (N) g ): 2, this parameter combination forms a total of 1×5×1=5 systems to be simulated. The generated molecular chain model is placed in a cubic simulation box with dimensions of 100 Å×100 Å×100 Å. The model file format must be recognizable by the simulation software, for example, the file format is .data, which can be recognized by LAMMPS (molecular dynamics simulation software).

[0037] S12: Perform molecular dynamics simulation on the initial model according to the pre-configured molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer.

[0038] In this embodiment, molecular dynamics parameters are parameters that need to be pre-set during molecular dynamics simulation. The initial model obtained based on the first parameter is an initial model, which needs to be simulated through molecular dynamics simulation to simulate the structure of the grafted polymer in a real environment. Molecular dynamics parameters include at least system configuration parameters (applicable units, boundary conditions, force field parameters, etc.), simulation condition parameters (temperature control, pressure control, time step), and multi-threaded parallel computing parameters. Evolutionary trajectory is the path of motion of atoms or molecules over time during the simulation. It records the position and velocity of each atom at each time point and is key data for understanding the dynamic behavior of the system.

[0039] In this embodiment, after the initial model is built, the system's automated task management and scheduling unit places the initial model file, calculation file, and molecular dynamics parameter file in the same folder, generates a calculation task, and executes the task to realize the molecular dynamics simulation of the grafted polymer, thereby obtaining the evolution trajectory of the grafted polymer.

[0040] In this embodiment, during large-scale computations, i.e., when there are multiple grafted polymer structures to be simulated, a separate folder is created for each initial model file. The corresponding computational files and molecular dynamics parameter files are copied into this folder. A job submission script is generated for the entire task, which also declares the computational resources requested by the task. Multiple computational tasks are then submitted to the supercomputing queue at once using corresponding commands. These tasks are automatically queued and executed based on cluster resource availability. During execution, the molecular dynamics simulation software reads the model file (data), molecular dynamics parameter file, and computation command file from the computational tasks. Based on the parameters in the files, molecular dynamics simulations are performed on the initial model to obtain the evolution trajectory.

[0041] For example, there are 5 sets of first parameters. Create 5 folders, put the 5 initial model files, the corresponding calculation files and molecular dynamic parameter files into the folders, and submit these calculation tasks to the calculation queue through the supercomputing submission script, so as to simulate the evolutionary trajectories corresponding to these 5 initial models.

[0042] S13: Determine the second parameter of the grafted polymer based on the evolution trajectory, wherein the second parameter includes at least the mean square radius of gyration and the mean square end-to-end distance of the main chain.

[0043] In this embodiment, the second parameter is the mean square radius of gyration and the mean square end-to-end distance of the main chain. The mean square radius of gyration is a key parameter describing the spatial extension of a polymer chain in solution; simply put, it reflects the "size" or "compactness" of the molecular chain. The mean square end-to-end distance of the main chain is a core parameter describing the size of the polymer chain, defined as the statistical average of the squares of the straight-line distances between the two ends of the chain, used to quantify the flexibility and spatial distribution range of the polymer chain.

[0044] In this embodiment, after the initial model has undergone molecular dynamics simulation and the evolution trajectory has been obtained, the system's automated data sampling and extraction unit analyzes the evolution trajectory and calculates the mean square radius of gyration of the grafted polymer molecules according to a preset calculation formula. ), main chain mean square end distance During the calculation of parameters, due to the large amount of data in the evolutionary trajectory, data was collected and calculated at preset acquisition time intervals, finally obtaining the average value and standard deviation. Simultaneously, the evolutionary trajectory was analyzed to calculate the radial distribution function between the main chain and side chains, as well as the chain segment order parameters. In multi-model parallel analysis, all results obtained after analysis were output to a unified, structured data file.

[0045] S14: Generate corresponding analysis results based on the first parameter and the second parameter. The analysis results include at least the parameter combination corresponding to the grafted polymer and the scatter plot corresponding to the parameter combination. In this embodiment, after calculating the evolution trajectory and obtaining the second parameter, the system's intelligent data calculation and processing unit reads the data file containing the first and second parameters through the data processing and analysis library. It then constructs a data table containing the parameters corresponding to different grafted polymers, with each row representing a parameter combination system, including main chain length, side chain length, grafting density, etc. , And so on. Based on the obtained parameters, a corresponding 3D scatter plot is drawn to show how the three structural parameters (main chain length, side chain length, and grafting density) jointly affect the structure. Record these parameter combinations and scatter plots in a report to generate the corresponding report, which will serve as the analysis result.

[0046] For example, the system can be used to automatically complete the construction, simulation, analysis, and report generation of five polymer systems with different structures in a short period of time.

[0047] In this embodiment, the results show that as the number of grafted side chains increases, the mean square radius of gyration and the mean square end distance of the system both increase.

[0048] refer to Figure 2 , Figure 2This is a data processing flowchart proposed in an embodiment of this application, as follows: Figure 2 As shown, firstly, a parameterized model is constructed based on the input parameters, i.e., the first parameter, to obtain an initial model. Then, corresponding computational tasks are generated, and multiple initial models are automatically managed and scheduled to obtain multiple corresponding evolutionary trajectories. Then, multiple evolutionary trajectories are automatically sampled and extracted to obtain the corresponding second parameters. Finally, intelligent data calculation and processing are performed based on the obtained parameters to generate analysis results and output the corresponding analysis report.

[0049] In this embodiment, multiple initial models of grafted polymers are created and analyzed simultaneously to obtain corresponding parameters and generate analysis reports. The entire process requires no manual intervention, which improves the analysis efficiency of grafted polymers and accelerates the material development cycle.

[0050] In another embodiment of this application, generating the corresponding initial model based on the first parameter includes: S21: Determine the arrangement of the main chain and side chains of the grafted polymer according to a preset sequence.

[0051] In this embodiment, the arrangement of the main chain and side chains of the grafted compound is first determined according to a preset sequence.

[0052] S22: Determine the chemical bonds and connection positions of the grafted polymer according to the preset connection rules.

[0053] In this embodiment, the chemical bonds of the grafted compound and the connection positions of the main chain and side chains are determined according to a preset connection rule.

[0054] S23: Based on the arrangement, chemical bonds, and connection positions, combine the main chain and side chains set in the first parameter to obtain the initial model.

[0055] In this embodiment, after determining the arrangement, chemical bonds, and connection positions of the main chain and side chains, the main chain and side chains set in the first parameter are combined to obtain the corresponding initial model.

[0056] In this embodiment, when multiple sets of parameters are set, for each set of parameters, a pre-written control program can be executed to generate a corresponding graft chain in three-dimensional space according to the specified sequence and connection rules, thereby obtaining the corresponding initial model and realizing the automatic construction of the model based on the parameters.

[0057] In another embodiment of this application, the step of performing molecular dynamics simulation on the initial model according to pre-configured molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer includes: S21: Create a corresponding file for the initial model.

[0058] In this embodiment, after obtaining the initial model, a corresponding file is created for the initial model to prepare it for calculation in the automated management and task scheduling system.

[0059] S22: Add the corresponding calculation files and the files corresponding to the molecular dynamics parameters to the directory where the folder is located to form the corresponding calculation task.

[0060] In this embodiment, the corresponding calculation file and the file corresponding to the molecular dynamics parameters are added to the directory where the file is located, thereby forming the corresponding calculation task.

[0061] S23: Submit the computation task to the task queue.

[0062] In this embodiment, after the computing task is constructed, it is submitted to the task queue through the job submission control program. The control program has already requested the computing resources required by the computing task.

[0063] S24: If the cluster resource status meets the computing resources requested by the computing task, execute the computing task to obtain the evolution trajectory of the grafted polymer.

[0064] In this embodiment, if the cluster resources meet the computing resource requirements of the computing task, the computing task is executed, and the initial model is simulated and analyzed based on the molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer.

[0065] In this embodiment, when analyzing the initial model, the execution status of the task is monitored in real time, and the subsequent data analysis process is automatically triggered after the task is completed.

[0066] In this embodiment, to meet the needs of large-scale computing, task scheduling is carried out through a supercomputing cluster job scheduling system, and automated control programs are deeply integrated to realize parallel analysis of multiple models and improve the efficiency of materials research and development.

[0067] In another embodiment of this application, determining the second parameter of the grafted polymer based on the evolution trajectory includes: S41: The evolution trajectory is sampled at every preset sampling interval.

[0068] In this embodiment, the evolution trajectory is sampled at preset sampling intervals, and the sampled data is the structure of the grafted polymer at the current moment.

[0069] For example, the sampling time interval is set to 100 frames, meaning that sampling and calculation are performed every 100 frames of data.

[0070] S42: Based on the sampled graft polymer molecular structure, determine the mean square radius of gyration and the mean square end-to-end distance of the main chain of the graft polymer molecule.

[0071] In this embodiment, at each sampling, the radius of gyration and mean square end-to-end distance of the grafted polymer molecules are determined based on the sampled grafted polymer molecular structure.

[0072] S43: Determine the standard deviation and average value of the mean square radius of gyration and the mean square end distance of the main chain.

[0073] In this embodiment, the standard deviations of the mean square radius of gyration and the mean square end-to-end distance of the main chain reflect the overall stability of the grafted polymer. The average values ​​of the mean square radius of gyration and the mean square end-to-end distance of the main chain reflect the overall size of the grafted polymer. In the analysis of grafted polymers, these two parameters can quickly determine the homogeneity of the molecular chains and their environmental sensitivity.

[0074] In this embodiment, the automated data sampling and extraction unit starts the corresponding sampling program to automatically enter each working directory to read the evolution trajectory file, automatically identify and extract a series of key microstructure parameters, calculate the corresponding second parameter. The calculation formula has been pre-configured in the executable program, and the calculation can be automatically performed by calling the formula, thus realizing the automated analysis of the evolution trajectory of the grafted polymer.

[0075] In another embodiment of this application, the method further includes: S51: Analyze the evolution trajectory to determine the radial distribution function and chain segment sequence parameters between the main chain and side chains of the grafted polymer.

[0076] In this embodiment, the radial distribution function indicates the spatial correlation between the main chain and side chains of the grafted polymer. The chain segment order parameter is used to determine the degree of orderliness in a specific direction.

[0077] In this embodiment, the system also analyzes the evolution trajectory to determine the radial distribution function and chain segment sequence parameters between the main chain and side chains of the grafted polymer. The system's program is pre-configured with formulas for calculating the radial distribution function and chain segment sequence parameters of the grafted polymer; by calling these formulas, the radial distribution function and chain segment sequence parameters of the grafted polymer can be automatically calculated.

[0078] In this embodiment, the evolution trajectory is analyzed using a preset formula to determine the radial distribution function and chain segment sequence parameters, thereby enabling accurate analysis of the grafted compound.

[0079] In another embodiment of this application, generating the corresponding analysis result based on the first parameter and the second parameter includes: S61: Generate a corresponding data table based on the first parameter and the second parameter, wherein each row of the data table represents a parameter combination.

[0080] In this embodiment, after the second parameter is obtained through analysis, the intelligent data calculation and processing unit analyzes the file containing all parameters of the grafted polymer. The data processing and analysis library analyzes the file and extracts the corresponding data table. Each row represents a parameter combination system, and the columns include parameters such as main chain length, side chain length, grafting density, mean square radius of gyration, and mean square end distance of the main chain.

[0081] S62: Generate a corresponding scatter plot based on the first parameter and the second parameter.

[0082] In this embodiment, the system uses statistical tools to generate corresponding scatter plots based on the first parameter and the second parameter. For each parameter combination, the first parameter is used as the horizontal axis, and the two values ​​in the second parameter are used as the vertical axes to obtain two two-dimensional scatter plots.

[0083] In this embodiment, the data is analyzed to generate corresponding data tables and scatter plots, which facilitates analysis by researchers and improves the efficiency of materials development.

[0084] In another embodiment of this application, the method further includes: S71: Use the first parameter and the second parameter as training data to train the machine learning model and obtain a trained machine learning model.

[0085] In this embodiment, the first parameter and the second parameter are used as training data to train the machine learning model. During the training process, the corresponding second parameter is predicted using the unlabeled first parameter, and the loss value of the loss function is determined using the original second parameter. The loss value is then used for backpropagation to adjust the parameters of the machine learning model until the loss value reaches the optimal value. The iterative training ends, and the trained machine learning model is obtained.

[0086] For example, the model can be an RNN or an LSTM model.

[0087] S72: The parameters of the grafted polymer are predicted using the machine learning model to obtain the corresponding prediction results.

[0088] In this embodiment, the parameters of the subsequently input grafted polymer are predicted using a trained machine learning model to obtain the corresponding prediction results, i.e., the first parameter is input and the corresponding second parameter is predicted.

[0089] S73: Generate a corresponding prediction scatter plot based on the prediction results.

[0090] In this embodiment, after predicting the second parameter corresponding to different first parameters, a corresponding prediction scatter plot is generated based on the predicted value to show the predicted mean square radius of gyration and mean square end distance of the main chain under different parameter combinations.

[0091] In this embodiment, the mean square radius of gyration and mean square end-to-end distance of the main chain of the grafted polymer are predicted by a machine learning model, which is beneficial for analyzing the structure of the grafted polymer and improves the analysis efficiency.

[0092] In another embodiment of this application, the structure of coarse-grained bottle-brush PDMS was analyzed for high-throughput screening. Five initial models were established with a main chain length of 31, side chain lengths of 4, 7, 10, 12, and 15 monomers, and a grafting density of 2. The corresponding tasks were submitted to the supercomputer, and the corresponding data were extracted after the simulation trajectory was completed. The data obtained after processing the data is shown in Table 1.

[0093] The resulting scatter plot is Figure 3 , Figure 3 This is a two-dimensional scatter plot proposed in one embodiment of this application.

[0094] In the embodiments described above, only the main chain length, side chain length, and grafting density of the grafted polymer need to be set to obtain the corresponding analytical results, greatly improving research efficiency. Through automated task scheduling and parallel computing, simulations and analyses of a large number of grafted polymer systems with different parameter combinations can be completed in a short time, significantly improving computational efficiency and shortening the material development cycle. Experiments show that simulations and preliminary analyses of over 100 grafted polymer systems with different parameter combinations can be completed within 24 hours, with computational efficiency more than 10 times higher than traditional methods. The entire process is controlled by a unified computational task, ensuring that all simulation settings, force fields, and analysis methods remain consistent, guaranteeing high comparability and reliability of calculation results under different parameter conditions, facilitating the construction of a systematic and complete material database. By automatically extracting multi-dimensional structural parameters and performing correlation analysis, the evolutionary laws of grafted polymer structures and their impact mechanisms on performance can be deeply revealed at the molecular level, providing a solid theoretical basis for material design. Compared with experimental data and benchmark theoretical calculations, the prediction error of this method for key physical quantities such as free energy and radius of gyration of a specified grafted polymer system is less than 5%, and the conformation sampling coverage exceeds 95%, demonstrating excellent calculation accuracy and reliability.

[0095] It should be noted that the high-throughput grafting polymer computation method based on molecular dynamics simulation provided in this application can be executed by a high-throughput grafting polymer computation device based on molecular dynamics simulation, or by a control module within that device for executing the high-throughput grafting polymer computation method based on molecular dynamics simulation. This application uses the execution of the high-throughput grafting polymer computation method based on molecular dynamics simulation by a high-throughput grafting polymer computation device as an example to illustrate the high-throughput grafting polymer computation method based on molecular dynamics simulation provided in this application.

[0096] refer to Figure 4 , Figure 4 This is a schematic diagram of a high-throughput grafted polymer computing device 400 based on molecular dynamics simulation according to an embodiment of this application, as shown below. Figure 4 As shown, the device includes: The model building module 401 is used to receive at least one first parameter of the grafted polymer to be simulated and generate a corresponding initial model based on the first parameter. The first parameter includes at least the main chain length, side chain length and grafting density. Simulation analysis module 402 is used to perform molecular dynamics simulation on the initial model according to pre-configured molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer; Data analysis module 403 is used to determine a second parameter of the grafted polymer based on the evolution trajectory, wherein the second parameter includes at least the mean square radius of gyration and the mean square end-to-end distance of the main chain; The result output module 404 is used to generate corresponding analysis results based on the first parameter and the second parameter. The analysis results include at least the parameter combination corresponding to the grafted polymer and the scatter plot corresponding to the parameter combination.

[0097] Optionally, the model building module includes: The sequence determination submodule is used to determine the arrangement of the main chain and side chains of the grafted polymer according to a preset sequence. The connection rule determination submodule is used to determine the chemical bonds and connection positions of the grafted polymer according to preset connection rules; The initial model construction submodule is used to combine the main chain and side chains set in the first parameter according to the arrangement, chemical bonds and connection positions to obtain the initial model.

[0098] Optionally, the simulation analysis module includes: The file creation submodule is used to create corresponding files for the initial model; The computation task construction submodule is used to add the corresponding computation files and the files corresponding to the molecular dynamics parameters to the directory where the folder is located, thereby forming the corresponding computation task. The task submission submodule is used to submit the computing task to the task queue; The evolution trajectory acquisition submodule is used to execute the computing task and obtain the evolution trajectory of the grafted polymer when the cluster resource status meets the computing resources requested by the computing task.

[0099] Optionally, the data analysis module includes: The sampling submodule is used to sample the evolution trajectory at preset sampling intervals. The parameter calculation submodule is used to determine the mean square radius of gyration and the mean square end-to-end distance of the main chain of the grafted polymer molecule based on the sampled grafted polymer molecular structure. The mean calculation submodule is used to determine the standard deviation and average value of the mean square radius of gyration and the mean square end distance of the main chain.

[0100] Optionally, the device further includes: The trajectory analysis submodule is used to analyze the evolution trajectory and determine the radial distribution function and chain segment sequence parameters between the main chain and side chains of the grafted polymer.

[0101] Optionally, the result output module includes: The data table generation submodule is used to generate a corresponding data table based on the first parameter and the second parameter, wherein each row of the data table represents a parameter combination; The first scatter plot generation submodule is used to generate a corresponding scatter plot based on the first parameter and the second parameter.

[0102] Optionally, the device further includes: The model training submodule is used to train the machine learning model using the first parameter and the second parameter as training data to obtain a trained machine learning model. The result prediction submodule is used to predict the parameters of the grafted polymer using the machine learning model and obtain the corresponding prediction results. The second scatter plot generation submodule is used to generate a corresponding prediction scatter plot based on the prediction results.

[0103] The high-throughput grafted polymer computing device based on molecular dynamics simulation in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network-attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0104] The high-throughput grafted polymer computing device based on molecular dynamics simulation in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0105] The high-throughput graft polymer computing device based on molecular dynamics simulation provided in this application embodiment can achieve… Figures 1 to 3 The various processes implemented by the high-throughput grafted polymer computing device based on molecular dynamics simulation in the method embodiments will not be described again here to avoid repetition.

[0106] Optionally, this application embodiment also provides an electronic device, including a processor 110, a memory 109, and a program or instructions stored in the memory 109 and executable on the processor 110. When the program or instructions are executed by the processor 110, they implement the various processes of the above-described high-throughput grafted polymer calculation method embodiment based on molecular dynamics simulation and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0107] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0108] Figure 5 This is a schematic diagram of the hardware structure of an electronic device proposed in an embodiment of this application.

[0109] The electronic device 100 includes, but is not limited to, components such as: radio frequency unit 101, network module 102, audio output unit 103, input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, and processor 110.

[0110] Those skilled in the art will understand that the electronic device 100 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 110 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0111] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described high-throughput grafted polymer calculation method based on molecular dynamics simulation and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0112] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0113] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described high-throughput grafted polymer calculation method embodiment based on molecular dynamics simulation, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0114] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0115] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0116] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0117] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A high-throughput grafted polymer calculation method based on molecular dynamics simulation, characterized in that, The method comprises: receiving a first parameter of a graft polymer to be simulated, generating a corresponding initial model according to the first parameter, the first parameter comprising at least a main chain length, a side chain length and a graft density; performing molecular dynamics simulation on the initial model according to pre-configured molecular dynamics parameters to obtain an evolution trajectory of the graft polymer; determining a second parameter of the graft polymer according to the evolution trajectory, the second parameter comprising at least a mean square radius of gyration and a mean square end-to-end distance of the main chain; generating a corresponding analysis result according to the first parameter and the second parameter, the analysis result comprising at least a parameter combination corresponding to the graft polymer and a scatter plot corresponding to the parameter combination.

2. The high-throughput grafted polymer calculation method based on molecular dynamics simulation according to claim 1, characterized in that, The generating of the initial model according to the first parameter comprises: determining an arrangement mode of the main chain and the side chain of the graft polymer according to a preset sequence; determining chemical bonds and connection positions of the graft polymer according to a preset connection rule; combining the main chain and the side chain set in the first parameter according to the arrangement mode, the chemical bonds and the connection positions to obtain the initial model.

3. The high-throughput grafted polymer calculation method based on molecular dynamics simulation according to claim 1, characterized in that, The performing of the molecular dynamics simulation on the initial model according to the pre-configured molecular dynamics parameters to obtain the evolution trajectory of the graft polymer comprises: creating a corresponding file for the initial model; adding a corresponding calculation file and a file corresponding to the molecular dynamics parameters in a directory where the folder is located to form a corresponding calculation task; submitting the calculation task to a task queue; in a case where a cluster resource status meets a calculation resource request of the calculation task, executing the calculation task to obtain the evolution trajectory of the graft polymer.

4. The high-throughput grafted polymer calculation method based on molecular dynamics simulation according to claim 1, wherein, The determining of the second parameter of the graft polymer according to the evolution trajectory comprises: sampling the evolution trajectory at every other preset sampling interval time; determining a mean square radius of gyration and a mean square end-to-end distance of the main chain of the graft polymer according to a graft polymer molecular structure obtained by sampling; determining a standard deviation and an average value of the mean square radius of gyration and the mean square end-to-end distance of the main chain.

5. The high-throughput grafted polymer calculation method based on molecular dynamics simulation according to claim 4, characterized in that, The method further comprises: analyzing the evolution trajectory to determine a radial distribution function and a segment sequence parameter between the main chain and the side chain of the graft polymer.

6. The high-throughput grafted polymer calculation method based on molecular dynamics simulation according to claim 1, wherein, The generating of the analysis result according to the first parameter and the second parameter comprises: generating a corresponding data table according to the first parameter and the second parameter, each row of the data table representing a parameter combination; generating a corresponding scatter plot according to the first parameter and the second parameter.

7. The high-throughput grafted polymer calculation method based on molecular dynamics simulation according to claim 6, characterized in that, The method further comprises: training a machine learning model by taking the first parameter and the second parameter as training data to obtain a trained machine learning model; predicting parameters of the graft polymer by using the machine learning model to obtain a corresponding prediction result; generating a corresponding prediction scatter plot according to the prediction result.

8. A high-throughput grafted polymer computing device based on molecular dynamics simulation, characterized in that, The device comprises: The model building module is used to receive at least one first parameter of the grafted polymer to be simulated and generate a corresponding initial model based on the first parameter. The first parameter includes at least the main chain length, side chain length and grafting density. The simulation analysis module is used to perform molecular dynamics simulation on the initial model according to the pre-configured molecular dynamics parameters to obtain the evolution trajectory of the grafted polymer; The data analysis module is used to determine a second parameter of the grafted polymer based on the evolution trajectory, wherein the second parameter includes at least the mean square radius of gyration and the mean square end-to-end distance of the main chain; The result output module is used to generate corresponding analysis results based on the first parameter and the second parameter. The analysis results include at least the parameter combination corresponding to the grafted polymer and the scatter plot corresponding to the parameter combination.

9. An electronic device, comprising: It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of any of the methods described in claims 1-7.

10. A readable storage medium, characterized by, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of any of the methods described in claims 1-7.

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