Reaction rate prediction method and device based on machine learning and related equipment

Abstract

The invention provides a reaction rate prediction method and device based on machine learning and related equipment. The method comprises the following steps: obtaining reaction basic conditions of a polyfunctional group molecular model; carrying out polymerization reaction simulation on the polyfunctional group molecular model based on reaction basic conditions to obtain first simulated molecular weight distribution data; training a preset neural network based on the first simulated molecular weight distribution data to obtain a polymerization reaction rate prediction model; obtaining molecular weight distribution data to be predicted; and performing reaction rate prediction on the to-be-predicted molecular weight distribution data based on the polymerization reaction rate prediction model to obtain reaction rate prediction data. According to the application, the first simulated molecular weight distribution data is obtained by performing reaction rate adjustable polymerization reaction simulation on the polyfunctional group molecular model, and the preset neural network is trained, so that the polymerization reaction rate prediction model can provide the predicted value of the reaction rate coefficient ratio of different types of reaction functional groups in the polymerization reaction process.

Description

Technical Field

[0001] The present invention relates to the field of polymer material polymerization technology, and in particular to a reaction rate prediction method, device and related equipment based on machine learning. Background Art

[0002] The molecular weight distribution of polymers is a fundamental property of polymers, which has a significant impact on the dynamic modulus, fracture toughness, glass transition temperature, viscosity and other properties of the material. Therefore, accurate prediction of molecular weight distribution before actual synthesis has always been an important research direction in polymer science.

[0003] However, existing simulation methods have certain limitations in predicting molecular weight distribution. For example, the Flory-Stockmayer theory tends to ignore the formation of ring structures during the reaction process, and the reaction rates of all monomers in the traditional functional group molecular model are constant during the reaction process, that is, the reactivity of the functional group is a constant value during the entire reaction process. It is not suitable for complex reaction situations and is only applicable to systems with a low degree of polymerization or catalyst-free systems. It cannot be applied to more polymer polymerization simulation scenarios. Summary of the invention

[0004] The present application proposes a reaction rate prediction method, device and related equipment based on machine learning, which is applicable to more polymer polymerization simulation scenarios and is conducive to the polymerization reaction rate prediction model to provide predicted values ​​of the reaction rate coefficient ratios of different types of reaction functional groups during the polymerization reaction.

[0005] In a first aspect, a reaction rate prediction method based on machine learning is provided, comprising: Obtain the reaction basis conditions of multi-functional molecular models; Performing polymerization reaction simulation on the multi-functional molecular model based on the reaction basic conditions to obtain first simulated molecular weight distribution data; Training a preset neural network based on the first simulated molecular weight distribution data to obtain a polymerization reaction rate prediction model; Obtaining molecular weight distribution data to be predicted; The reaction rate is predicted based on the polymerization reaction rate prediction model for the molecular weight distribution data to be predicted, so as to obtain reaction rate prediction data.

[0006] In a second aspect, a reaction rate prediction device based on machine learning is provided, comprising: The first acquisition module is used to obtain the basic reaction conditions of the multi-functional group molecular model; A first simulation module, used for performing a polymerization reaction simulation on the multi-functional molecular model based on the reaction basic conditions to obtain first simulated molecular weight distribution data; A training module, used for training a preset neural network based on the first simulated molecular weight distribution data to obtain a polymerization reaction rate prediction model; A second acquisition module is used to acquire molecular weight distribution data to be predicted; The first prediction module is used to perform reaction rate prediction on the molecular weight distribution data to be predicted based on the polymerization reaction rate prediction model to obtain reaction rate prediction data.

[0007] Optionally, in some embodiments of the present application, the device further includes: A third acquisition module is used to obtain actual molecular weight distribution data of the multi-functional group molecular model; A second prediction module is used to perform reaction rate prediction on the actual molecular weight distribution data based on the polymerization reaction rate prediction model to obtain actual reaction rate data; A second simulation module is used to update the reaction basic conditions based on the actual reaction rate data, so as to perform a polymerization reaction simulation on the multi-functional molecular model based on the updated reaction basic conditions to obtain second simulated molecular weight distribution data; A verification module is used to verify the polymerization reaction rate prediction model according to the actual molecular weight distribution data and the second simulated molecular weight distribution data.

[0008] Optionally, in some embodiments of the present application, the basic reaction conditions include scanning rate ratio parameters and reaction association parameters, the scanning rate ratio parameters include the reaction rate of each functional group in the multi-functional molecular model under different conditions, and the reaction association parameters include the concentration of each functional group in the multi-functional molecular model under different conditions.

[0009] Optionally, in some embodiments of the present application, different functional groups have different concentrations.

[0010] Optionally, in some embodiments of the present application, the first simulation module includes: A calculation submodule, used to calculate the reaction rate of each functional group in the multi-functional group molecular model under different conditions using a preset reaction rate calculation formula; The simulation submodule is used to use a Monte Carlo algorithm to simulate the polymerization reaction of the reaction rate of each functional group in the multi-functional group molecular model under different conditions based on the reaction basic conditions to obtain the first simulated molecular weight distribution data.

[0011] Optionally, in some embodiments of the present application, the simulation submodule includes: A determination unit, used for determining a target reaction based on the reaction rate of each functional group in the multi-functional group molecular model under different conditions; A simulation unit, used for performing polymerization reaction simulation on any two unreacted functional groups in the multi-functional group molecular model based on the target reaction; The obtaining unit is used for returning to calculate the reaction rate of any reaction in the multi-functional molecular model and continuing to execute if the reaction degree of the polymerization reaction simulation does not reach the prediction threshold, and obtaining the first simulated molecular weight distribution data.

[0012] Optionally, in some embodiments of the present application, the multi-functional group molecular model includes functional group A and functional group B, and the reaction rate calculation formula is expressed as: Where R represents the reaction rate, represents the reaction rate coefficient of the i-th case of functional group A, represents the concentration of the i-th case of functional group A, is the reaction rate coefficient of the jth case of functional group B, is the concentration of the jth case of the B functional group, N represents the number of different cases of the A functional group, and M represents the number of different cases of the B functional group.

[0013] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned reaction rate prediction method based on machine learning when executing the computer program.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned reaction rate prediction method based on machine learning are implemented.

[0015] The present application provides a method, device, computer equipment and storage medium for predicting reaction rate based on machine learning, by obtaining the basic reaction conditions of a multi-functional molecular model; based on the basic reaction conditions, the multi-functional molecular model is subjected to polymerization reaction simulation to obtain first simulated molecular weight distribution data; based on the first simulated molecular weight distribution data, a preset neural network is trained to obtain a polymerization reaction rate prediction model; the molecular weight distribution data to be predicted is obtained; based on the polymerization reaction rate prediction model, the molecular weight distribution data to be predicted is subjected to reaction rate prediction to obtain reaction rate prediction data. In the method for predicting reaction rate based on machine learning provided in the present application, by simulating a polymerization reaction with adjustable reaction rate on the multi-functional molecular model under the constraint of the basic reaction conditions, the molecular weight distribution under different reaction conditions, i.e., the first simulated molecular weight distribution data, can be obtained, and then, the preset neural network is trained using the first simulated molecular weight distribution data, which is conducive to the neural network learning the law of molecular weight change of the multi-functional molecular model during the polymerization reaction from the molecular weight distribution under different reaction conditions, so as to be applicable to more polymer polymerization simulation scenarios, and is conducive to the polymerization reaction rate prediction model providing a predicted value of the reaction rate coefficient ratio of different types of reaction functional groups during the polymerization reaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 An application environment diagram of the reaction rate prediction method based on machine learning provided in an embodiment of the present application; Figure 2 A flowchart of a reaction rate prediction method based on machine learning provided in an embodiment of the present application; Figure 3 A schematic diagram of a multi-functional molecular model provided in an embodiment of the present application; Figure 4 A schematic diagram of a polymerization reaction simulation process provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of a neural network provided in an embodiment of the present application; Figure 6 A schematic diagram of a process flow of a reaction rate prediction method based on machine learning provided in yet another embodiment of the present application; Figure 7A bar chart comparing the structure prediction of the polymerization reaction rate prediction model provided by an embodiment of the present application and the traditional model; Figure 8 A bar chart comparing the structure prediction of the polymerization reaction rate prediction model provided by another embodiment of the present application and the traditional model; Fig. 9 A scatter plot comparing the molecular weight distribution of the polymerization reaction rate prediction model provided in one embodiment of the present application and the traditional model; Fig.10 A scatter plot comparing molecular weight distribution of a polymerization reaction rate prediction model provided in another embodiment of the present application and a traditional model; Fig.11 A structural block diagram of a reaction rate prediction device based on machine learning provided in an embodiment of the present application; Fig.12 A structural block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0020] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0021] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0022] The reaction rate prediction method based on machine learning provided by the embodiment of the present invention can be applied in Figure 1 In the application environment. Among them, the computer device 110 communicates with the server 120 through the network 130. The computer device 110 can obtain the reaction basic conditions of the multi-functional molecular model; simulate the polymerization reaction of the multi-functional molecular model based on the reaction basic conditions to obtain the first simulated molecular weight distribution data; train the preset neural network based on the first simulated molecular weight distribution data to obtain the polymerization reaction rate prediction model; obtain the molecular weight distribution data to be predicted; predict the reaction rate of the molecular weight distribution data to be predicted based on the polymerization reaction rate prediction model to obtain the reaction rate prediction data, and display it through the computer device 110. In the present invention, by simulating the polymerization reaction of the multi-functional molecular model under the constraint of the reaction basic conditions, the molecular weight distribution under different reaction conditions, that is, the first simulated molecular weight distribution data, can be obtained. Then, the preset neural network is trained using the first simulated molecular weight distribution data, which is conducive to the neural network learning the law of molecular weight change of the multi-functional molecular model during the polymerization reaction from the molecular weight distribution under different reaction conditions, so as to be applicable to more polymer polymerization simulation scenarios, and is conducive to the polymerization reaction rate prediction model to provide a predicted value of the reaction rate coefficient ratio of different types of reaction functional groups during the polymerization reaction. The computer device 110 may be, but is not limited to, various smart phones 110 - 1 , tablet computers 110 - 2 and notebook computers 110 - 3 . The present invention is described in detail below through specific embodiments.

[0023] See also Figure 2 As shown, Figure 2 A flow chart of a reaction rate prediction method based on machine learning provided in an embodiment of the present invention is provided. The method can be applied to both a terminal and a server. This embodiment is illustrated by applying it to a server. The reaction rate prediction method based on machine learning includes the following steps: S101: Obtain the basic reaction conditions of the multi-functional molecular model.

[0024] Among them, the multifunctional group molecular model refers to a particle containing at least two functional groups, which records the information of the functional groups that can react. Functional groups can undergo polymerization reactions to link with other polymer monomers. A polymer monomer can include one or more functional groups.

[0025] The multi-functional group molecule model includes the molecule name, relative molecular mass, and a hash table, which stores the names of different functional groups on a single molecule and the corresponding numbers of the functional group names, such as "-OH": 3.

[0026] Exemplarily, it is assumed that the multi-functional group molecular model includes molecule A, molecule B and molecule C, as shown in Table 1, the types and quantities of functional groups contained in each molecule, and a set of reaction rates corresponding to each functional group.

[0027] Table 1 In Table 1,<a1, 1, 2> It means that the reaction rate of the A molecule with 2 remaining a1 functional groups is 2, and the reaction rate of the A molecule with 1 remaining a1 functional group is 1;<b1, 1, 2.2, 3.3> It means that the reaction rate of the b1 functional group of the B molecule with 3 b1 functional groups remaining is 3.3, the reaction rate of the b1 functional group of the B molecule with 2 b1 functional groups remaining is 2.2, and the reaction rate of the b1 functional group of the B molecule with 1 b1 functional group remaining is 1;<b2, 2, 3, 2> It means that the reaction rate of the b2 functional group of the B molecule with 3 remaining b2 functional groups is 2, the reaction rate of the b2 functional group of the B molecule with 2 remaining b2 functional groups is 3, and the reaction rate of the b2 functional group of the B molecule with 1 remaining b2 functional group is 2;<c1, 2, 3.4, 5.1, 10> It means that the reaction rate of C molecules with 4 remaining c1 functional groups is 10, the reaction rate of C molecules with 3 remaining c1 functional groups is 5.1, the reaction rate of C molecules with 2 remaining c1 functional groups is 3.4, and the reaction rate of C molecules with 1 remaining c1 functional group is 2.

[0028] Assuming that the multifunctional molecular model includes reactant 1 and reactant 2, the functional group reaction rules of the multifunctional molecular model are shown in Table 2: Table 2 Table 2 shows that in the multi-functional molecular model, molecule A containing an unreacted a1 functional group can react once with molecule B containing an unreacted b1 functional group; molecule B containing an unreacted b2 functional group can react once with molecule C containing an unreacted c1 functional group.

[0029] In one embodiment, the basic reaction conditions include a scanning rate ratio parameter and a reaction association parameter, wherein the scanning rate ratio parameter includes the reaction rate of each functional group in the multi-functional molecular model under different conditions, and the reaction association parameter includes the concentration of each functional group in the multi-functional molecular model under different conditions.

[0030] Among them, different situations can be different parameters under the basic reaction conditions, such as different reaction degrees and reaction rate coefficient ratios between functional groups.

[0031] The polymerization reaction of the proportional coefficient of the reaction rate of the same functional group in different states can be simulated, so as to obtain different distributions of each molecular weight, and finally the relationship between the reaction rate coefficient ratio of the functional group in different states and the molecular weight distribution can be obtained. Therefore, the proportional coefficient of the reaction rate of the functional group in different states (i.e. different situations) (i.e. the reaction rate coefficient ratio) can be predicted through the molecular weight distribution of a single molecule.

[0032] In one embodiment, different functional groups have different concentrations.

[0033] S102: performing polymerization reaction simulation on the multi-functional molecular model based on the reaction basic conditions to obtain first simulated molecular weight distribution data.

[0034] The reaction basic conditions may include relevant data of various molecular species, functional group reaction rules, reaction rate calculation formulas, and set total number of reactions or reaction degrees, etc. The first simulated molecular weight distribution data may be data obtained by simulating the polymerization reaction of each functional group in the multifunctional molecular model under the reaction basic conditions. It may include the statistical distribution of the number of molecules of different functional groups in the multifunctional molecular model.

[0035] The reaction basic conditions may include multiple pre-set reaction degrees and multiple reaction ratios. The total number of reactions refers to the total number of times all reaction rules in the multi-functional group molecular model occur.

[0036] Polymerization reaction simulation usually includes simulating the steps of chain initiation, chain growth, chain termination, and possible reactions such as chain transfer, branching and cross-linking in the polymerization process.

[0037] In one embodiment, a mathematical model of the polymerization reaction is established by a machine learning algorithm, and the molecular weight distribution of different reaction rate coefficient ratios of each functional group under different conditions is calculated in combination with the basic reaction conditions, such as the reaction rate constant, the reaction rate coefficient ratio, the degree of reaction, etc., to obtain the first molecular weight distribution data.

[0038] In one embodiment, the molecular weight distribution of different reaction ratios of each functional group under different conditions can be predicted by simulating the movement behavior of molecules under reaction conditions, thereby obtaining the first molecular weight distribution data.

[0039] Monte Carlo simulations of polymerization reactions with multiple groups of different reaction rate coefficient ratios are performed for the input multifunctional molecular model under different conditions. During the Monte Carlo simulation, the reaction rate is dynamically adjusted according to a preset reaction rate calculation formula, so that the reaction rate can be calculated more accurately and the accuracy of the reaction rate calculation can be increased, thereby obtaining the first molecular weight distribution data. That is, in one embodiment, the polymerization reaction simulation of the multifunctional molecular model based on the reaction basic conditions is performed to obtain the first simulated molecular weight distribution data, including: Calculate the reaction rate of each functional group in the multi-functional group molecular model under different conditions using a preset reaction rate calculation formula; A Monte Carlo algorithm is used to simulate the polymerization reaction of the reaction rate coefficient ratios of the functional groups in the multi-functional molecular model under different conditions based on the reaction basic conditions to obtain the first simulated molecular weight distribution data.

[0040] For example, assuming that the multi-functional group molecular model includes functional group A and functional group B, the reaction rate calculation formula can be expressed as: Where R represents the reaction rate, represents the reaction rate coefficient of the i-th case of functional group A, represents the concentration of the i-th case of functional group A, is the reaction rate coefficient of the jth case of functional group B, is the concentration of the jth case of the B functional group, N represents the number of different cases of the A functional group, and M represents the number of different cases of the B functional group.

[0041] In the traditional Monte Carlo algorithm, when the functional group changes between the i-th and j-th situations during the simulation reaction, and In this embodiment, when the i-th situation and the j-th situation change, the changed and New predictions are made to enable it to automatically adjust the reactivity of functional groups. In subsequent reaction rate predictions, the polymerization reaction rate prediction model can be used to predict the reaction rate changes of functional groups, making it applicable to more polymer polymerization simulation scenarios.

[0042] The reaction rate calculation formula for different functional groups is the same, but the concentration is different.

[0043] By improving the Monte Carlo algorithm, that is, adding a reaction rate calculation formula to the Monte Carlo algorithm, and then using this Monte Carlo algorithm, the reaction rates of each functional group in the multi-functional group molecular model under different conditions are calculated using the reaction rate calculation formula, and based on the reaction basic conditions, polymerization reaction simulations are performed on the reaction rates of each functional group in the multi-functional group molecular model under different conditions to obtain the first simulated molecular weight distribution data.

[0044] In this embodiment, based on the reaction rate calculation formula, the changing reaction rate is predicted so that the reaction activity of the functional group can be automatically adjusted. Subsequently, machine learning is used to predict the change of the reaction rate of the functional group from experimental data, so as to be applicable to more polymer polymerization simulation scenarios.

[0045] The Gillespie algorithm is a stochastic simulation algorithm based on the Monte Carlo method that is often applied in the field of polymer polymerization simulation. At present, the following deficiencies exist in the application of this algorithm in the field of polymer polymerization simulation: This algorithm and program are both based on the equal reactivity theory such as Flory, that is, during the reaction process, the reaction activity of the functional group is a constant value throughout the reaction process, and it is only applicable to systems with a small degree of polymerization or catalyst-free systems, which limits the application scope of this algorithm. Therefore, in this embodiment, Monte Carlo simulations of polymerization reactions with different reaction rate coefficient ratios can be performed on the incoming functional groups under different conditions, so as to obtain the final molecular weight distribution of the system to obtain the first simulated molecular weight distribution data. That is, in one embodiment, the Monte Carlo algorithm is used to perform polymerization reaction simulations on the reaction rates of each functional group in the multi-functional group molecular model under different conditions based on the reaction basic conditions to obtain the first simulated molecular weight distribution data, including: Determine the target reaction based on the reaction rates of each functional group in the multi-functional group molecular model under different conditions; Perform polymerization reaction simulations on any two unreacted functional groups in the multi-functional group molecular model based on the target reaction; If the reaction degree of the polymerization reaction simulation does not reach the prediction threshold, return to calculate the reaction rate of any reaction in the multi-functional group molecular model and continue to execute until the reaction degree of the polymerization reaction simulation reaches the prediction threshold to obtain the first simulated molecular weight distribution data.

[0046] For example, assume that a multi-functional group molecular reaction requires setting five reaction rate coefficients k1, k2, k3, k4, and k5. For a molecular system containing this reaction type, it is necessary to simulate the system evolution at ten evenly spaced reaction degrees (10%, 20%,..., 100%), and for each reaction degree, all parameter combinations need to be traversed for simulation to generate multiple sets of molecular distribution amounts. The simulation parameter settings are as follows: Reaction degree stage: set 10 reaction endpoints (10%~100%, step length 10%) Reaction rate coefficient benchmark: k5 is set to 1 as a reference coefficient, and the ratio of k1-k4 to k5 takes 50 gradient values ​​(0.1~10, step length 0.2) Under the above setting conditions, execute the following nested loop process: Using a five-layer nested loop structure: Outer loop: the reaction degree is cycled from 10% to 100% (10% step, 10 times); Second layer: k1 cycles from 0.1 to 5 (step size 0.2, 25 times); The third layer: k2 cycles from 0.1 to 5 (step size 0.2, 25 times); Fourth layer: k3 cycles from 0.1 to 5 (step size 0.2, 25 times); Innermost layer: k4 cycles from 0.1 to 5 (step size 0.2, 25 times); After each layer of loop completes all iterations, the upper layer variable update is triggered; each parameter combination corresponds to a simulation scenario; each reaction degree stage executes a complete four-layer loop independently. Finally, 10 (reaction degree) × 25^4 (parameter combination) groups of molecular weight distribution data are obtained, forming the first molecular weight distribution data set.

[0047] Molecular weight distribution can be represented in the form of a multidimensional array.

[0048] Exemplarily, the molecular weight distribution can be shown as: [(0.1,0.2,0.3,...,1.0,1.1...,4.8,4.9,5.0),(0.01,0.02,0.04,......,0.20,0.25,....,0.07,0.04,0.01)], where the first () is the molecular mass and the second () is the probability density of the corresponding molecular mass.

[0049] The reaction ratio refers to the ratio of the number of molecules of different reactive monomers, and the reaction degree refers to the progress of the reaction, that is, how many functional groups in the multi-functional molecular model react. The reaction between different monomers is the reaction of functional groups. The initial reaction ratio of different monomers will polymerize to generate polymers with different molecular weight distributions. For example, Figure 3 As shown in the figure, assuming that the multifunctional molecular model includes 1000 A functional groups and 1000 B functional groups, if the reaction degree is set to 90%, it means that 900 A and 900 B undergo polymerization. The molecular weight distribution of the generated polymer is different with different reaction degrees.

[0050] In one embodiment, the basic reaction conditions can be obtained in response to the user's information input operation, and the basic reaction conditions also include the type and quantity of molecules, the type and quantity of functional groups, the reaction rules of functional groups, etc., and the corresponding number of molecule classes and reaction rule classes can be initialized, and the corresponding relationship between functional groups and reaction rates can be recorded using global variables. The reaction rate corresponding to each functional group is shown in Table 1.

[0051] After obtaining the basic conditions of the reaction, such as Figure 4 As shown, the reaction rate of each possible reactive functional group in the multi-functional group molecular model is calculated, one of the reactions, namely the target reaction, is randomly selected from the reaction rates of each possible reactive functional group, two monomers with unreacted functional groups are selected to perform a polymerization reaction on the selected target reaction, the two monomers are linked, and the multi-functional group molecular model is updated to determine whether the updated multi-functional group molecular model reaches the expected reaction degree (ie, the prediction threshold). If not, the reaction rate of each possible reaction is continued to be calculated based on the reaction rate calculation formula, and the execution is continued until the reaction degree of the multi-functional group molecular model reaches the expected reaction degree, and the molecular weight distribution of different reaction proportions corresponding to the functional groups under the reaction degree is output, so that the first molecular weight distribution data can be obtained.

[0052] Each molecule records the connection status with other molecules. Based on the information recorded by each molecule, the total molecular weight generated, the reaction status of each type of molecule (such as the remaining 1, 2... the proportion of unreacted functional groups in the total number of participating molecules, that is, the K value), the number average molecular weight Mn, the weight average molecular weight Mw and other data can be counted as the first molecular weight distribution data.

[0053] It should be noted that the basic reaction conditions also include environmental factors. In the Monte Carlo simulation algorithm, the values ​​of the reaction rates of monomer functional groups under different states are replaced by multiple variables so that the values ​​can be input and passed. These variables usually represent different reaction conditions or environmental factors, such as temperature, pressure, reactant concentration, etc., which may affect the reaction rate. For example, the reaction rate can be specified explicitly or using the Arrhenius expression. The values ​​under different states refer to the specific values ​​of the reaction rates under these different conditions. For example, the reaction rate of a functional group at high temperature and low temperature will be different, and this difference can be simulated by setting different variable values. In this way, the simulation software can handle various reaction conditions more flexibly, thereby more accurately predicting the behavior and characteristics of polymerization reactions.

[0054] S103: Training a preset neural network based on the first simulated molecular weight distribution data to obtain a polymerization reaction rate prediction model.

[0055] After the polymerization reaction simulation is completed, the first simulated molecular weight distribution data is used as the characteristic value, and the reaction rate coefficient ratio between the functional groups at each reaction degree is used as the target value to train the preset neural network to obtain the polymerization reaction rate prediction model.

[0056] For example, in a polymerization reaction of only A2+B3 (molecules A and B react with each other, A has 2 reactive functional groups and B has 3), 5 k values ​​are finally counted, namely kA0, kA1, kB0, kB1, and kB2. kA0 represents the proportion of molecules A with 0 unreacted functional groups to the total number of molecules that have reacted, and the rest are similar. Thus, the first molecular weight distribution data obtained by simulating the polymerization reaction of molecules A and B is used as the characteristic value, and the ratio of the reaction rate coefficients between the functional groups of molecules A and B at each reaction degree is used as the target value to train the preset neural network, and obtain a polymerization reaction rate prediction model.

[0057] The target value is the reaction rate coefficient corresponding to the functional group of molecule A, the reaction rate coefficient corresponding to the functional group of molecule B, the ratio of the reaction rate coefficients of molecules A and B, and the final reaction degree reached by the reaction.

[0058] Optionally, the preset neural network may be a feedforward neural network, such as Figure 5 As shown, the feedforward neural network includes an input layer, a hidden layer and an output layer. In this application, the input layer is used to receive the feature values ​​of the external input, convert the feature values ​​into a numerical form that the neural network can process, and pass it to the hidden layer, which is the key part of feature extraction and transformation. The hidden layer can have one or more layers, and each layer contains a certain number of neurons. The output layer is used to generate the final output result.

[0059] After the polymerization reaction rate prediction model training is completed, the method further includes: Obtaining actual molecular weight distribution data of the multi-functional molecular model; Performing reaction rate prediction on the actual molecular weight distribution data based on the polymerization reaction rate prediction model to obtain actual reaction rate data; updating the reaction basic conditions based on the actual reaction rate data, so as to perform a polymerization reaction simulation on the multi-functional molecular model based on the updated reaction basic conditions to obtain second simulated molecular weight distribution data; The polymerization reaction rate prediction model is verified based on the actual molecular weight distribution data and the second simulated molecular weight distribution data.

[0060] Among them, the actual molecular weight distribution data includes each molecule recording the connection status with other molecules. According to the information recorded by each molecule, it is possible to count the total molecular weight generated, the reaction status of each type of molecule (such as the remaining 1, 2... the proportion of unreacted functional groups in the total number of participating molecules, that is, the K value (reaction rate coefficient ratio)), the number average molecular weight Mn, the weight average molecular weight Mw and other data. The number average molecular weight Mn and the weight average molecular weight Mw are two important parameters for describing the molecular weight distribution of high molecular weight polymers. The number average molecular weight Mn is often used to describe the average molecular weight of polymers, and the weight average molecular weight Mw is often used to describe the physical properties of polymers, such as viscosity and mechanical properties, because these properties are usually more affected by large molecules.

[0061] During the polymerization simulation, the molecular weight of each molecule in the multifunctional molecular model can be recorded to calculate the number average molecular weight and weight average molecular weight.

[0062] The number average molecular weight can be calculated according to the following formula: in, The molecular weight is The number of molecules.

[0063] The weight average molecular weight can be calculated according to the following formula: in, The molecular weight is The number of molecules.

[0064] The actual molecular weight distribution data of the multi-functional molecular model can be obtained by experimental means (such as exclusion chromatography); the actual molecular weight distribution data is used as a characteristic value and input into a polymerization reaction rate prediction model for reaction rate prediction to obtain the reaction ratio, reaction degree and reaction rate corresponding to all functional groups corresponding to the actual molecular weight distribution data; the reaction ratio, reaction degree and reaction rate corresponding to all functional groups, i.e., the actual reaction rate data, are updated according to each reaction ratio, reaction degree and reaction rate corresponding to all functional groups in the reaction basic conditions, so as to simulate the polymerization reaction of the multi-functional molecular model based on the updated reaction basic conditions to obtain second simulated molecular weight distribution data; a similarity algorithm can be used to calculate the similarity between the second simulated molecular weight distribution data and the actual molecular weight distribution data. If the calculated similarity meets the similarity threshold, it proves that the polymerization reaction rate prediction model predicts accurately. If the calculated similarity does not meet the similarity threshold, the model parameters can be adjusted, the first molecular weight distribution data can be added, and the generalization ability of the polymerization reaction rate prediction model can be improved. More types of polymerization reaction data can also be introduced to improve the robustness of the polymerization reaction rate prediction model. After that, after adjusting the parameters and adding data, the polymerization reaction rate prediction model is retrained. Through gradual adjustment and verification, the prediction accuracy of the polymerization reaction rate prediction model can be improved.

[0065] After obtaining the polymerization reaction rate prediction model, the method further comprises: S104: Obtaining molecular weight distribution data to be predicted.

[0066] The molecular weight distribution data to be predicted refers to the molecular weight distribution data required for reaction rate prediction.

[0067] S105: performing reaction rate prediction on the molecular weight distribution data to be predicted based on the polymerization reaction rate prediction model to obtain reaction rate prediction data.

[0068] Characteristic values ​​can be extracted from the molecular weight distribution data to be predicted to obtain characteristic values, and the reaction rate can be predicted for the characteristic values ​​using a polymerization reaction rate prediction model to obtain the reaction ratio and reaction degree of each functional group corresponding to the molecular weight distribution data to be predicted, as well as the reaction rate corresponding to all functional groups, as reaction rate prediction data.

[0069] Exemplarily, it is assumed that the molecular weight distribution data to be predicted includes 5 k values, namely kA0, kA1, kB0, kB1, and kB2, as well as Mw and Mn, wherein kA0 represents the percentage of A molecules with 0 remaining unreacted functional groups in the total number of reacted molecules, kA1 represents the percentage of A molecules with 1 remaining unreacted functional group in the total number of reacted molecules, kB0 represents the percentage of B molecules with 0 remaining unreacted functional groups in the total number of reacted molecules, kB1 represents the percentage of B molecules with 1 remaining unreacted functional group in the total number of reacted molecules, and kB2 represents the percentage of B molecules with 2 remaining unreacted functional groups in the total number of reacted molecules. Therefore, the five k values ​​and Mw, Mn can be extracted from the molecular weight distribution data to be predicted as characteristic values, and the reaction rate is predicted for the characteristic values ​​through a polymerization reaction rate prediction model, so as to obtain the reaction ratio and reaction degree of each functional group corresponding to the molecular weight distribution data to be predicted and the reaction rate corresponding to all functional groups as reaction rate prediction data; through the predicted reaction rate prediction data, a polymer can be synthesized according to the reaction rate prediction data, so that the molecular weight distribution of the synthesized polymer meets expectations, and a polymer with a specific molecular weight and molecular weight distribution can be efficiently synthesized, which not only improves the synthesis efficiency but also reduces the experimental cost and time.

[0070] The polymerization reaction rate prediction model is used to predict the reaction rate ratio coefficient. In industrial production, the reaction rate coefficient can be used to optimize reaction conditions, such as temperature, pressure, concentration and catalyst selection; in chemical engineering, the reaction rate coefficient is an important basis for designing reactors, which can help engineers predict the reaction behavior in the reactor, select the appropriate reactor type (such as continuous reactor or batch reactor), and optimize the operating conditions of the reactor; in drug synthesis, the reaction rate coefficient can be used to predict the reaction progress and product generation, thereby optimizing the synthesis process.

[0071] In one embodiment, if Figure 6 As shown, a reaction rate prediction method based on machine learning is provided, and the specific process steps are as follows: Step 1: Receive input information and initialize the system.

[0072] The specific operations of receiving input information and initializing the system include: accepting various types of input information (i.e., basic reaction conditions) for initializing the system. The input information includes: relevant data of various molecular species, functional group reaction rules, reaction rate calculation formulas, and the set total number of reactions or reaction degrees.

[0073] Step 2: Rewrite the Monte Carlo simulation program and add an adjustable reaction rate function.

[0074] The traditional Monte Carlo simulation algorithm for polymerization reactions is rewritten based on the reaction rate calculation formula.

[0075] Step 3: Use the algorithm in step 2 to simulate a large number of different initial material reaction ratios and reaction degrees for the same polymerization reaction to obtain enough data. Each set of reactant ratios and reaction degrees will correspond to the molecular weight distribution of a polymer system after the reaction and the proportion of different types of monomers. The polymer system can be a type of material system composed of high molecular weight compounds (also called polymers).

[0076] Step 4: Train the preset neural network to obtain the final polymerization reaction rate prediction model.

[0077] The large amount of data in step three is used to train a machine learning model (such as a neural network), with molecular weight distribution and reaction degree as input features and reaction rate coefficient ratio as output. A feedforward neural network model is used for training to obtain a model that predicts the reaction rate coefficient ratio through molecular weight distribution and reaction degree.

[0078] Compared with the traditional Monte Carlo simulation of polymer polymerization, in which the reaction rate is constant during the reaction, the polymerization process of the present application can more accurately calculate the reaction rate according to the input reaction rate formula. Figure 3 The functional group A and the functional group B shown are used as a multi-functional molecular model for the reaction experiment, and the reaction rates of the two are used as a factual comparison. The traditional model is predicted based on the Gillespie algorithm, a random simulation algorithm based on the Monte Carlo algorithm; and the present application enables it to automatically adjust the reactivity of the functional group and predict the change in the reaction rate of the functional group from the experimental data through a machine learning algorithm. It is more suitable for more polymer polymerization simulation scenarios and has distinct flexibility and mobility. Refer to Table 3 and Figures 7 and 8 It can be seen that Figure 7 , 8 The comparison shown clearly shows the predicted comparison of one of the basic properties of polymers - structure. The comparison is the ratio of the number of monomers with different numbers of functional groups in each monomer reaction. Figure 3 The multifunctional molecular model shown has two monomers, A has two functional groups that can react, and B has three functional groups that can react. KA0 represents the number of A monomers that react with both functional groups (0 functional groups that can react have 0 remaining), KA1 represents the number of A monomers that react with one functional group, KB0 represents the number of B monomers that react with 3 functional groups, and so on. After comparing the numbers, the proportional data in Table 3 are obtained. According to the comparison of the final experimental data, it can be clearly found that compared with the traditional model, it is obvious that the polymerization reaction prediction model of the present application is more accurate, closer to the actual data, and the reliability of the prediction results is higher.

[0079] Table 3 Another basic property of polymers is molecular weight distribution prediction. Fig. 9 , Fig.10 As shown, compared with the traditional model, the data predicted by the polymerization reaction rate prediction model obtained by training the neural network with the first molecular weight distribution data obtained by simulation in this application can be used to predict the molecular weight distribution compared with the traditional Flory-Stockmayer polymer reaction theory, which improves its limitations, such as ignoring the formation of ring structures and active assumptions such as functional groups.

[0080] The above is the process of the reaction rate prediction method based on machine learning in this application.

[0081] As mentioned above, the present application provides a method, device, computer equipment and storage medium for predicting reaction rate based on machine learning, by obtaining the basic reaction conditions of a multi-functional molecular model; based on the basic reaction conditions, the multi-functional molecular model is subjected to polymerization reaction simulation to obtain first simulated molecular weight distribution data; based on the first simulated molecular weight distribution data, a preset neural network is trained to obtain a polymerization reaction rate prediction model; molecular weight distribution data to be predicted is obtained; based on the polymerization reaction rate prediction model, the molecular weight distribution data to be predicted is subjected to reaction rate prediction to obtain reaction rate prediction data. In the method for predicting reaction rate based on machine learning provided in the present application, by simulating a polymerization reaction with adjustable reaction rate on a multi-functional molecular model under the constraint of the basic reaction conditions, the molecular weight distribution under different reaction conditions, i.e., the first simulated molecular weight distribution data, can be obtained, and then, the preset neural network is trained using the first simulated molecular weight distribution data, which is conducive to the neural network learning the law of molecular weight change of the multi-functional molecular model during the polymerization reaction from the molecular weight distribution under different reaction conditions, so as to be applicable to more polymer polymerization simulation scenarios, and is conducive to the polymerization reaction rate prediction model providing a predicted value of the reaction rate coefficient ratio of different types of reaction functional groups during the polymerization reaction.

[0082] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0083] In one embodiment, a reaction rate prediction device based on machine learning is provided, and the reaction rate prediction device based on machine learning corresponds one-to-one to the reaction rate prediction method based on machine learning in the above embodiment. Fig.11 As shown, the reaction rate prediction device based on machine learning includes: The first acquisition module 201 is used to obtain the reaction basic conditions of the multi-functional group molecular model; A first simulation module 202 is used to perform a polymerization reaction simulation on the multi-functional group molecular model based on the reaction basic conditions to obtain first simulated molecular weight distribution data; A training module 203 is used to train a preset neural network based on the first simulated molecular weight distribution data to obtain a polymerization reaction rate prediction model; The second acquisition module 204 is used to acquire molecular weight distribution data to be predicted; The first prediction module 205 is used to perform reaction rate prediction on the molecular weight distribution data to be predicted based on the polymerization reaction rate prediction model to obtain reaction rate prediction data.

[0084] In the reaction rate prediction method scheme based on machine learning provided in the present application, by obtaining the reaction basic conditions of the multi-functional molecular model; based on the reaction basic conditions, the multi-functional molecular model is subjected to polymerization reaction simulation to obtain the first simulated molecular weight distribution data; based on the first simulated molecular weight distribution data, the preset neural network is trained to obtain the polymerization reaction rate prediction model; the molecular weight distribution data to be predicted is obtained; based on the polymerization reaction rate prediction model, the molecular weight distribution data to be predicted is subjected to reaction rate prediction to obtain the reaction rate prediction data. In the reaction rate prediction method scheme based on machine learning provided in the present application, by simulating the polymerization reaction with adjustable reaction rate on the multi-functional molecular model under the constraint of the reaction basic conditions, the molecular weight distribution under different reaction conditions, i.e., the first simulated molecular weight distribution data, can be obtained, and then, the preset neural network is trained using the first simulated molecular weight distribution data, which is conducive to the neural network learning the law of molecular weight change of the multi-functional molecular model during the polymerization reaction from the molecular weight distribution under different reaction conditions, so as to be applicable to more polymer polymerization simulation scenarios, and is conducive to the polymerization reaction rate prediction model providing the predicted value of the reaction rate coefficient ratio of different types of reaction functional groups during the polymerization reaction.

[0085] Optionally, in some embodiments of the present application, the device further includes: A third acquisition module is used to obtain actual molecular weight distribution data of the multi-functional group molecular model; A second prediction module is used to perform reaction rate prediction on the actual molecular weight distribution data based on the polymerization reaction rate prediction model to obtain actual reaction rate data; A second simulation module is used to update the reaction basic conditions based on the actual reaction rate data, so as to perform a polymerization reaction simulation on the multi-functional molecular model based on the updated reaction basic conditions to obtain second simulated molecular weight distribution data; A verification module is used to verify the polymerization reaction rate prediction model according to the actual molecular weight distribution data and the second simulated molecular weight distribution data.

[0086] Optionally, in some embodiments of the present application, the basic reaction conditions include scanning rate ratio parameters and reaction association parameters, the scanning rate ratio parameters include the reaction rate of each functional group in the multi-functional molecular model under different conditions, and the reaction association parameters include the concentration of each functional group in the multi-functional molecular model under different conditions.

[0087] Optionally, in some embodiments of the present application, different functional groups have different concentrations.

[0088] Optionally, in some embodiments of the present application, the first simulation module includes: A calculation submodule, used to calculate the reaction rate of each functional group in the multi-functional group molecular model under different conditions using a preset reaction rate calculation formula; The simulation submodule is used to use a Monte Carlo algorithm to simulate the polymerization reaction of the reaction rate of each functional group in the multi-functional group molecular model under different conditions based on the reaction basic conditions to obtain the first simulated molecular weight distribution data.

[0089] Optionally, in some embodiments of the present application, the simulation submodule includes: A determination unit, used for determining a target reaction based on the reaction rate of each functional group in the multi-functional group molecular model under different conditions; A simulation unit, used for performing polymerization reaction simulation on any two unreacted functional groups in the multi-functional group molecular model based on the target reaction; The obtaining unit is used for returning to calculate the reaction rate of any reaction in the multi-functional molecular model and continuing to execute if the reaction degree of the polymerization reaction simulation does not reach the prediction threshold, and obtaining the first simulated molecular weight distribution data.

[0090] Optionally, in some embodiments of the present application, the multi-functional group molecular model includes functional group A and functional group B, and the reaction rate calculation formula is expressed as: Where R represents the reaction rate, represents the reaction rate coefficient of the i-th case of functional group A, represents the concentration of the i-th case of functional group A, is the reaction rate coefficient of the jth case of functional group B, is the concentration of the jth case of the B functional group, N represents the number of different cases of the A functional group, and M represents the number of different cases of the B functional group.

[0091] In one embodiment, a computer device is provided, the internal structure diagram of which can be as follows: Fig.12 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, the functions or steps of a reaction rate prediction method based on machine learning are implemented.

[0092] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented: Obtain the basic reaction conditions of the multi-functional molecular model; simulate the polymerization reaction of the multi-functional molecular model based on the basic reaction conditions to obtain the first simulated molecular weight distribution data; train the preset neural network based on the first simulated molecular weight distribution data to obtain the polymerization reaction rate prediction model; obtain the molecular weight distribution data to be predicted; predict the reaction rate of the molecular weight distribution data to be predicted based on the polymerization reaction rate prediction model to obtain the reaction rate prediction data.

[0093] In an embodiment, by performing a polymerization reaction simulation with adjustable reaction rate on a multi-functional molecular model under the constraints of basic reaction conditions, the molecular weight distribution under different reaction conditions, i.e., the first simulated molecular weight distribution data, can be obtained. Then, the preset neural network is trained using the first simulated molecular weight distribution data, which is beneficial for the neural network to learn the law of molecular weight change of the multi-functional molecular model during the polymerization reaction from the molecular weight distribution under different reaction conditions, thereby being applicable to more polymer polymerization simulation scenarios, and being beneficial for the polymerization reaction rate prediction model to provide a predicted value of the reaction rate coefficient ratio of different types of reaction functional groups during the polymerization reaction.

[0094] In one embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented: Obtain the basic reaction conditions of the multi-functional molecular model; simulate the polymerization reaction of the multi-functional molecular model based on the basic reaction conditions to obtain the first simulated molecular weight distribution data; train the preset neural network based on the first simulated molecular weight distribution data to obtain the polymerization reaction rate prediction model; obtain the molecular weight distribution data to be predicted; predict the reaction rate of the molecular weight distribution data to be predicted based on the polymerization reaction rate prediction model to obtain the reaction rate prediction data.

[0095] In an embodiment, by performing a polymerization reaction simulation with adjustable reaction rate on a multi-functional molecular model under the constraints of basic reaction conditions, the molecular weight distribution under different reaction conditions, i.e., the first simulated molecular weight distribution data, can be obtained. Then, the preset neural network is trained using the first simulated molecular weight distribution data, which is beneficial for the neural network to learn the law of molecular weight change of the multi-functional molecular model during the polymerization reaction from the molecular weight distribution under different reaction conditions, thereby being applicable to more polymer polymerization simulation scenarios, and being beneficial for the polymerization reaction rate prediction model to provide a predicted value of the reaction rate coefficient ratio of different types of reaction functional groups during the polymerization reaction.

[0096] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0097] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0098] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0099] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A reaction rate prediction method based on machine learning, characterized in that: The method comprises: Obtain the reaction basis conditions of multi-functional molecular models; Performing polymerization reaction simulation on the multi-functional molecular model based on the reaction basic conditions to obtain first simulated molecular weight distribution data; Training a preset neural network based on the first simulated molecular weight distribution data to obtain a polymerization reaction rate prediction model; Obtaining molecular weight distribution data to be predicted; The reaction rate is predicted based on the polymerization reaction rate prediction model for the molecular weight distribution data to be predicted, so as to obtain reaction rate prediction data.

2. The reaction rate prediction method based on machine learning according to claim 1, characterized in that: The method further comprises: Obtaining actual molecular weight distribution data of the multi-functional molecular model; Performing reaction rate prediction on the actual molecular weight distribution data based on the polymerization reaction rate prediction model to obtain actual reaction rate data; updating the reaction basic conditions based on the actual reaction rate data, so as to perform a polymerization reaction simulation on the multi-functional molecular model based on the updated reaction basic conditions to obtain second simulated molecular weight distribution data; The polymerization reaction rate prediction model is verified based on the actual molecular weight distribution data and the second simulated molecular weight distribution data.

3. The reaction rate prediction method based on machine learning according to claim 1, characterized in that: The basic reaction conditions include a scan rate ratio parameter and a reaction association parameter. The scan rate ratio parameter includes the reaction rate of each functional group in the multi-functional molecular model under different conditions. The reaction association parameter includes the concentration of each functional group in the multi-functional molecular model under different conditions.

4. The reaction rate prediction method based on machine learning according to claim 3 is characterized in that: Different functional groups have different concentrations.

5. The reaction rate prediction method based on machine learning according to claim 1, characterized in that: The step of performing a polymerization reaction simulation on the multifunctional molecular model based on the reaction basic conditions to obtain first simulated molecular weight distribution data comprises: Calculate the reaction rate of each functional group in the multi-functional group molecular model under different conditions using a preset reaction rate calculation formula; A Monte Carlo algorithm is used to simulate the polymerization reaction of the reaction rate of each functional group in the multi-functional molecular model under different conditions based on the reaction basic conditions to obtain the first simulated molecular weight distribution data.

6. The reaction rate prediction method based on machine learning according to claim 5, characterized in that: The method adopts the Monte Carlo algorithm to simulate the polymerization reaction of the reaction rate of each functional group in the multi-functional group molecular model under different conditions based on the reaction basic conditions to obtain the first simulated molecular weight distribution data, including: Determining a target reaction based on the reaction rates of each functional group in the multi-functional group molecular model under different conditions; Performing polymerization reaction simulation on any two unreacted functional groups in the multi-functional molecular model based on the target reaction; If the reaction degree of the polymerization reaction simulation does not reach the prediction threshold, the process returns to calculating the reaction rate of any reaction in the multi-functional molecular model and continues until the reaction degree of the polymerization reaction simulation reaches the prediction threshold to obtain the first simulated molecular weight distribution data.

7. The reaction rate prediction method based on machine learning according to claim 5, characterized in that: The multi-functional group molecular model includes functional group A and functional group B, and the reaction rate calculation formula is expressed as: Where R represents the reaction rate, represents the reaction rate coefficient of the i-th case of functional group A, represents the concentration of the i-th case of functional group A, is the reaction rate coefficient of the jth case of functional group B, is the concentration of the jth case of the B functional group, N represents the number of different cases of the A functional group, and M represents the number of different cases of the B functional group.

8. A reaction rate prediction device based on machine learning, characterized in that: include: The first acquisition module is used to obtain the basic reaction conditions of the multi-functional group molecular model; A first simulation module, used for performing a polymerization reaction simulation on the multi-functional molecular model based on the reaction basic conditions to obtain first simulated molecular weight distribution data; A training module, used for training a preset neural network based on the first simulated molecular weight distribution data to obtain a polymerization reaction rate prediction model; A second acquisition module is used to acquire molecular weight distribution data to be predicted; The first prediction module is used to perform reaction rate prediction on the molecular weight distribution data to be predicted based on the polymerization reaction rate prediction model to obtain reaction rate prediction data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the reaction rate prediction method based on machine learning as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the reaction rate prediction method based on machine learning as described in any one of claims 1 to 7 are implemented.

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