Prediction method of precursor base-induced reaction activity based on physicochemical properties and molecular simulation

Abstract

The present invention discloses a method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation, comprising: obtaining the physicochemical properties of multiple different base excitation precursor materials; obtaining the molecular dynamics parameters of each precursor material under the corresponding physicochemical properties; obtaining the strength activity index of each precursor material through a traditional unconfined compressive strength test; sorting the physicochemical properties and molecular dynamics parameters according to the significant degree of influence on the strength activity index of the precursor material, eliminating non-significant parameters, and retaining significant influence parameters; based on the correspondence between the significant influence parameters and the strength activity index of each precursor material, a prediction model of the precursor base excitation reaction activity index with significant influence parameters as variables is established through multiple linear regression combined with an artificial neural network algorithm. The present invention ensures accuracy while realizing a rapid prediction of the activity of the base excitation reaction of the precursor material.

Description

Technical Field

[0001] The invention belongs to the technical field of base-excited materials and relates to a method for predicting the activity of a precursor base-excited reaction based on physicochemical properties and molecular simulation. Background Art

[0002] There are many kinds of precursor materials used to prepare alkali-activated materials. The differences in the physical and chemical properties of each precursor material lead to large differences in the activity of the alkali-activated reaction, which in turn leads to large differences in the strength of the prepared alkali-activated materials. Therefore, the estimation and evaluation of the activity of the precursor alkali-activated reaction is an important part of the material quality control, which significantly affects the preparation of high-strength alkali-activated materials. In the prior art, the precursor alkali-activated reaction activity is generally evaluated by mixing the precursor material with cement in a certain proportion, preparing the specimen, and measuring the unconfined compressive strength after curing for 28 days. The unconfined compressive strength is used as a quantitative parameter of the precursor alkali-activated reaction activity. This technology requires a curing time of up to 28 days, and has extremely poor timeliness for material quality control. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation, which ensures accuracy while realizing rapid prediction of the activity of precursor materials.

[0004] The technical solution adopted by the present invention is a method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation, comprising the following steps:

[0005] S1, obtaining the physicochemical properties of a plurality of different alkali-activated precursor materials; the physicochemical properties of the precursor materials include a ratio parameter and a shape parameter of the material particle size, a specific surface area, a total mass fraction of active silicon and aluminum, an active silicon and aluminum molar ratio, and a mass fraction of active calcium;

[0006] S2, obtaining the molecular dynamics parameters of each precursor material in S1 under the corresponding physical and chemical properties; the molecular dynamics parameters include the weighted average bond length, Si-O-Al bond angle distribution ratio and elastic modulus of the alkali-activated material gel;

[0007] S3, obtaining the strength activity index of each precursor material in S1 through a traditional unconfined compressive strength test;

[0008] S4, sort the physicochemical properties and molecular dynamics parameters according to the significance of their influence on the strength activity index of the precursor materials, eliminate the non-significant parameters, and retain the significantly influential parameters; based on the correspondence between the significantly influential parameters and the strength activity index of each precursor material, a prediction model for the activity index of the precursor base excitation reaction with the significantly influential parameters as variables is established through multiple linear regression combined with an artificial neural network algorithm.

[0009] Furthermore, in S1, the total mass fraction of active silicon and aluminum, the molar ratio of active silicon and aluminum, and the mass fraction of active calcium in the precursor material are obtained by leaching test to obtain a filtrate, and then an inductively coupled plasma emission spectroscopy test is performed.

[0010] Furthermore, in S1, the method for obtaining the ratio parameter and shape parameter of the particle size of the precursor material is as follows:

[0011] Perform laser particle size test on the precursor material, and fit the material particle size distribution curve through Weibull distribution function (see the following formula) to determine the ratio parameter and shape parameter of the precursor material particle size;

[0012] ,

[0013] Where: y is the cumulative passing percentage; x is the particle size; k 1 is the scale parameter of the Weibull distribution function; k 2 is the shape parameter of the Weibull distribution function.

[0014] Furthermore, in S1, the specific surface area of ​​the precursor material is determined by a specific surface area test.

[0015] Furthermore, S2 is specifically as follows: according to the obtained total mass fraction of active silicon and aluminum and the molar ratio of active silicon and aluminum, a model of alkali-activated material gel with different active silicon-aluminum molar ratios and different active calcium mass fractions is established in a molecular dynamics simulation software, and the weighted average bond length, Si-O-Al bond angle distribution ratio and elastic modulus of the alkali-activated material gel corresponding to the active silicon-aluminum molar ratio and active calcium mass fraction in S1 are calculated.

[0016] Furthermore, the alkali-activated material gel model with different active silicon-aluminum molar ratios and different active calcium mass fractions is established as follows: Na2Si2O5 molecules are used as the starting point for modeling, the Na2Si2O5 molecular model is imported through molecular dynamics simulation software, the original Na ions in the model are deleted, and it is expanded to an amorphous preliminary Si2O5 macromolecular model through the AC module, and part of the Si in the model is modified to Al according to the active silicon-aluminum ratio determined by S1, Ca ions are adsorbed into the model according to the determined active calcium mass fraction, the remaining Na ions are adsorbed, and then H2O molecules are adsorbed into the system cavity through the adsorption module in the molecular dynamics simulation software.

[0017] Furthermore, S3 is specifically as follows: preparing standard specimens with each precursor material in S1 and silicate cement of a fixed strength grade in a fixed proportion and a fixed water-cement ratio, curing at a standard curing temperature and humidity, testing to obtain the standard strength of the specimens, and calculating the strength activity index of the precursor material based on the standard strength according to the following formula:

[0018] ,

[0019] Where: It is the strength activity index; the standard strength unit is MPa.

[0020] Furthermore, in S4, the degree of influence of the physicochemical properties and molecular kinetic parameters of the precursor material on the activity index of the precursor material is determined by the multivariate analysis of variance method. The degree of influence is represented by the test level. A test level greater than 0.05 indicates that the parameter has no significant effect and is a non-significant parameter; a test level less than or equal to 0.05 indicates a significant influence parameter.

[0021] Furthermore, in S4, the shape parameter and bond angle distribution ratio of the precursor material particle size are eliminated, and the estimation model of the precursor base excitation reaction activity index is shown in the following formula:

[0022] ,

[0023] In the formula, F x is a precursor base-induced reactivity index based on physicochemical properties and molecular simulations. A scale parameter representing the particle size of the precursor material, represents the total mass fraction of active silicon and aluminum in the precursor material, represents the active silicon-aluminum molar ratio of the precursor material, represents the weighted average bond length of the precursor material, represents the elastic modulus of the precursor material; represents the specific surface area of ​​the precursor material; represents the active calcium mass fraction of the precursor material; a 1 is a constant term, a 2- a 9 is the coefficient of each parameter.

[0024] Furthermore, the precursor material is metakaolin, slag, fly ash or construction solid waste powder.

[0025] The beneficial effects of the present invention are:

[0026] The present invention determines the physicochemical properties of the precursor material through experiments, and uses molecular dynamics simulation to calculate the molecular dynamics parameters of the precursor material under the physicochemical properties, and establishes an empirical model of the physicochemical properties of the precursor material, the molecular dynamics parameters and the alkali-excitation reaction activity index of the precursor material. The parameters considered are more comprehensive, and the molecular dynamics simulation method is combined to reveal the characteristic law of the alkali-excitation reaction activity of the material from the molecular level (nanoscopic scale), which is more accurate and reliable.

[0027] The method of the present invention can complete the evaluation of the reaction activity of the alkali-excited precursor within 4 hours, which significantly improves the timeliness of the traditional evaluation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] 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.

[0029] Figure 1 It is the Weibull distribution function fitting particle size distribution curve in the embodiment of the present invention.

[0030] Figure 2 This is the precursor material leaching test process in the embodiment of the present invention.

[0031] Figure 3a It is the calculation result of the molecular dynamics simulation bond length (Al-O) in the embodiment of the present invention.

[0032] Figure 3b It is the calculation result of the molecular dynamics simulation bond length (Si-O) in the embodiment of the present invention.

[0033] Figure 4a It is the Na2Si2O5 molecule which is the starting point of the molecular dynamics simulation modeling in the embodiment of the present invention.

[0034] Figure 4b It is the model of molecular dynamics simulation of Al doping in the embodiment of the present invention.

[0035] Figure 4c It is the final gel model of the molecular dynamics simulation in the embodiment of the present invention.

[0036] Figure 5 This is the molecular dynamics simulation modeling calculation process in the embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with 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 only 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 making creative work are within the scope of protection of the present invention.

[0038] The basic concept of the embodiment of the present invention is as follows:

[0039] The active silicon-aluminum components in the precursor material can be dissolved in the alkaline activator to form free silicon-aluminum-calcium, and then the free silicon-aluminum-calcium components undergo a condensation reaction in an alkaline environment to form a three-dimensional aluminosilicate network structure, thereby forming the strength of the alkali-activated material. The physicochemical properties of the precursor material, such as the total mass fraction of active silicon and aluminum, the molar ratio of active silicon and aluminum, the mass fraction of active calcium, the precursor particle size distribution, and the specific surface area, have a significant effect on the alkali-activated reaction activity of the precursor material. The embodiment of the present invention conducts a microscopic analysis of the physicochemical properties of the precursor material, combined with a large number of test results and molecular dynamics simulation results, to establish a rapid prediction model for the precursor alkali-activated reaction activity based on the physicochemical properties of the material.

[0040] Example 1

[0041] A method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation comprises the following steps:

[0042] S1, obtaining the physical and chemical properties of a plurality of base-activated reaction precursor materials of different properties; the physical and chemical properties of the precursor materials include the ratio parameter and shape parameter of the material particle size, the specific surface area, the total mass fraction of active silicon and aluminum, the molar ratio of active silicon and aluminum, and the mass fraction of active calcium;

[0043] S11, laser particle size test is performed on the precursor material, and the particle size distribution curve of the material is fitted by the Weibull distribution function (see formula (1)), such as Figure 1 As shown, the proportion parameter and shape parameter of the material particle size are determined;

[0044] (1)

[0045] Where: y is the cumulative passing percentage; x is the particle size; k 1 is the scale parameter of the Weibull distribution function; k 2 is the shape parameter of the Weibull distribution function.

[0046] S12, determining the specific surface area of ​​the precursor material using a specific surface area test;

[0047] S13, such as Figure 2 As shown, 10 g of the precursor material was immersed in 100 ml of 1 mol / L NaOH solution and ultrasonically stirred for 20 min. The filtrate was filtered to perform inductively coupled plasma emission spectrometry test to obtain the mass fraction of active silicon, the mass fraction of active aluminum, and the mass fraction of active calcium in the precursor material. The total mass fraction of alkali-soluble silicon and aluminum (total mass fraction of active silicon and aluminum), the alkali-soluble silicon-aluminum ratio (active silicon-aluminum ratio), and the alkali-soluble calcium mass fraction (active calcium mass fraction) were calculated; the active silicon-aluminum ratio is the molar ratio of active silicon and aluminum.

[0048] S2, obtaining the molecular dynamics parameters of each precursor material in S1 under the corresponding physical and chemical properties; the molecular dynamics parameters include the weighted average bond length, Si-O-Al bond angle distribution ratio and elastic modulus of the alkali-activated material gel;

[0049] According to the data of total active silicon-aluminum mass fraction, active silicon-aluminum ratio and active calcium mass fraction obtained from the leaching test, the alkali-activated material gel model with different silicon-aluminum ratios and different active calcium mass fractions was established in the molecular dynamics simulation software, and its weighted average bond length, Si-O-Al bond angle distribution ratio and elastic modulus parameters were calculated. The weighted average bond length = Al-O average bond length × Al-O proportion + Si-O average bond length × Si-O proportion. The Al-O bond length and Si-O bond length are as follows: Figure 3a-Figure 3b shown.

[0050] Because the molecular structure of the alkali-activated gel product is close to that of the Na2Si2O5 molecule, the Na2Si2O5 molecule is first used as the starting point for modeling. The Na2Si2O5 molecular model is imported through the molecular dynamics simulation software (Materials Studio), the original Na ions in the model are deleted, and the model is expanded to an amorphous preliminary Si2O5 macromolecular model through the AC module (Amorphous Cell) in the software. According to the active silicon-aluminum ratio determined by S1, part of the Si in the model is modified to Al, and Ca ions are adsorbed into the model according to the determined active calcium mass fraction. In order to ensure the charge balance of the system, the remaining Na ions are adsorbed, and then the H2O molecules are adsorbed into the system cavity through the adsorption module in the molecular dynamics simulation software. The final model is obtained after structural optimization and kinetic equilibrium of the model through the module in the software (Forcite-Geometryoptimization). The model-related model diagram is shown in the figure below. Figure 4a-4c The weighted average bond length, Si-O-Al bond angle distribution ratio and elastic modulus corresponding to the gel model with different Si-Al ratios and different calcium mass fractions were obtained through the calculation module in the molecular dynamics simulation software, as shown in Figure 5 shown.

[0051] S3, obtaining the intensity activity index of each precursor material in S1;

[0052] Standard specimens were prepared by mixing each precursor material with silicate cement of strength grade 42.5 in a ratio of 7:3 and a water-cement ratio of 0.5. The specimens were cured for 28 days at standard curing temperature and humidity, and the standard strength of the specimens was obtained by testing. The strength activity index of the precursor material was then calculated according to formula (2).

[0053] (2)

[0054] Where: It is the strength activity index; the standard strength unit is MPa.

[0055] S4. The multivariate analysis of variance method was used to determine the significant influence of the total mass fraction of active silicon and aluminum in the precursor material, the active silicon-aluminum ratio (molar ratio), the weighted average bond length of the gel, the distribution ratio of the Si-O-Al bond angles in the gel, the elastic modulus of the gel, the precursor particle size ratio parameter and shape parameter, the precursor specific surface area and the estimated mass fraction of active calcium in the precursor on the precursor activity index. The physicochemical properties and molecular dynamics parameters were ranked according to the significance of their influence on the strength activity index of the precursor material, as shown in Table 1.

[0056] Table 1 Results of multivariate analysis of variance

[0057]

[0058] Note: The serial numbers in Table 1 represent different precursor materials (including kaolin, slag, fly ash and construction solid waste powder); the test level (P) ≤ 0.05 means that the parameter has a significant effect, and the test level greater than 0.05 means that the parameter has no significant effect and is a non-significant parameter; the results of multivariate analysis of variance show that except for shape parameters and Si-O-Al bond angle distribution ratio, the other parameters have a significant effect on the precursor activity index.

[0059] The non-significant parameters (shape parameters and Si-O-Al bond angle distribution ratio) were eliminated, and the significant influencing parameters were retained, as shown in Table 2. Based on the correspondence between the significant influencing parameters and the intensity activity index of the 18 precursor materials in Table 2, a prediction model for the precursor base excitation reaction activity index with the significant influencing parameters as variables was established by multiple linear regression combined with an artificial neural network algorithm (the specific operation method is known in the art), as shown in formula (3).

[0060] (3)

[0061] In the formula, F x The activity index of precursor base-induced reactivity based on physicochemical properties and molecular simulations A scale parameter representing the particle size of the precursor material, represents the total mass fraction of active silicon and aluminum in the precursor material, represents the active silicon-aluminum molar ratio of the precursor material, represents the average bond length of the precursor material, represents the elastic modulus of the precursor material; represents the specific surface area of ​​the precursor material; represents the mass fraction of active calcium in the precursor material; the correlation coefficient of formula (3) is 99%.

[0062] After many experiments, the general form of the estimation model is shown in formula (4):

[0063] (4)

[0064] In the formula, a 1 is a constant term, a 2- a 9 is the coefficient of each parameter item. A positive coefficient indicates that the parameter is positively correlated with the activity index, and a negative coefficient indicates that the parameter is negatively correlated with the activity index.

[0065] Table 2 Significant parameters of multiple linear regression

[0066]

[0067] Note: The serial numbers represent precursor materials of different properties (including metakaolin, slag, fly ash and construction solid waste powder).

[0068] Example 2

[0069] Precursor material No. 1 was taken for laser particle size analysis test. The proportional parameter k1 was calculated by fitting the Weibull distribution function to obtain a value of 13.23. The specific surface area k8 of precursor material No. 1 was measured to obtain a value of 480 m 2 / kg; the leaching test showed that the total mass fraction of active silicon and aluminum k3 was 45%, the active silicon-aluminum ratio k4 was 1, and the active calcium mass fraction k9 was 13%; the above three tests can be carried out simultaneously. According to the data of the total mass fraction of active silicon and aluminum, the active silicon-aluminum ratio and the active calcium mass fraction, a gel model was established in the molecular dynamics simulation software, and the average bond length k5 was measured to be 1.63 and the elastic modulus k7 was 25.9 GPa. The activity index of precursor material No. 1 was calculated by the activity index estimation model of formula (3) F x =0.96.

[0070] Traditional method:

[0071] Precursor material No. 1 (same as Example 2) was used to prepare a standard specimen with a ratio of 7:3 and a water-cement ratio of 0.5 with a strength grade of 42.5 silicate cement. The specimen was cured for 28 days at the standard curing temperature and humidity. The test showed that the standard strength of the specimen was 40.8 MPa. The strength activity index of the precursor material was then calculated according to formula (2), and the strength activity index of the precursor material No. 1 was 0.96, and the total time was 28 days.

[0072] Compared with the traditional method, the embodiment of the present invention saves 28 days of maintenance time, the total time is less than 4 hours, and the prediction result is consistent with the actual measurement.

[0073] Example 3

[0074] The first-grade fly ash from Ninghai, Zhejiang Province was used as the precursor material to be tested. The laser particle size analysis test was carried out. The proportional parameter k1 was calculated by fitting the Weibull distribution function to obtain a value of 26.40. The specific surface area k8 of the precursor material was measured to obtain a value of 510 m 2 / kg, the leaching test showed that the total mass fraction of active silicon and aluminum k3 was 47%, the active silicon-aluminum ratio k4 was 1.4, and the active calcium mass fraction k9 was 13%. Based on the data of the total mass fraction of active silicon and aluminum, the active silicon-aluminum ratio and the active calcium mass fraction, a gel model was established in the molecular dynamics simulation software, and the average bond length k5 was measured to be 1.64 and the elastic modulus k7 was 26.2 GPa. The activity index of the first-class fly ash sample in Ninghai, Zhejiang Province can be calculated by the activity index estimation model of formula (3). F x =0.97.

[0075] Using the traditional strength activity index evaluation method, the standard strength was 41.8MPa after 28 days of standard curing. F c =0.98.

[0076] Using the existing technology (a method for analyzing the reactivity of fly ash / slag for geopolymers, patent application number 202410147804.8), ω 非晶相CaO =8.2%,ω 非晶相MgO =1.1%,ω 非晶相SiO2 =33.4%,ω 非晶相Al2O3 =20.8%, the activity index is calculated to be =(ω 非晶相CaO +ω 非晶相MgO +ω 非晶相Al2O3 ) / ω 非晶相SiO2 =0.90.

[0077] In comparison, the activity index prediction result of the method of the embodiment of the present invention is closer to the activity index obtained by the traditional strength evaluation method and has higher accuracy.

[0078] Example 4

[0079] The construction solid waste powder in Changsha, Hunan Province was taken as the precursor material to be tested. The laser particle size analysis test was carried out. The proportional parameter k1 was calculated by fitting the Weibull distribution function to obtain 47.82. The specific surface area k8 of the precursor material was measured to obtain 450 m 2 / kg, the leaching test showed that the total mass fraction of active silicon and aluminum k3 was 23%, the active silicon-aluminum ratio k4 was 1.8, and the active calcium mass fraction was 5%. Based on the total mass fraction of active silicon and aluminum, the active silicon-aluminum ratio and the active calcium mass fraction data, a gel model was established in the molecular dynamics simulation software, and the average bond length k5 was measured to be 1.64 and the elastic modulus k7 was 18.7GPa. The activity index of the construction solid waste micropowder sample in Changsha, Hunan Province can be calculated by the activity index estimation model of formula (3). F x =0.76.

[0080] Using the traditional strength activity index evaluation method, the standard strength was 31.9MPa after 28 days of standard curing. F c =0.75.

[0081] Using the existing technology 1 (a method for analyzing the reactivity of fly ash / slag for geopolymer), ω 非晶相CaO =12.4%,ω 非晶相MgO =0.8%,ω 非晶相SiO2 =35.4%,ω 非晶相Al2O3 =7.6%, the activity index is calculated to be =(ω 非晶相CaO +ω 非晶相MgO +ω 非晶相Al2O3 ) / ω 非晶相SiO2 =0.59.

[0082] In comparison, the activity index prediction result of the method of the embodiment of the present invention is closer to the activity index obtained by the traditional strength evaluation method and has higher accuracy.

[0083] Comparative Example 1

[0084] Eliminating the influence of the specific surface area of ​​k8, the results are shown in Table 3.

[0085] Table 3 Calculation of predicted activity index (excluding k8)

[0086]

[0087] The error of the predicted activity index is more than 10%. Each parameter in the embodiment of the present invention has different degrees of contribution in the reaction process of the precursor material. If only some parameters are considered, it means that the influence of the parameter will be eliminated, which will affect the accuracy of the prediction result.

[0088] The embodiment of the present invention combines a large number of microscopic characterization experiments, macroscopic mechanical properties tests and numerical simulation structures at the molecular level, and couples the physical and chemical properties of the precursor materials obtained from the microscopic characterization experiments and the properties of the materials at the molecular level and the macroscopic mechanical properties obtained from the molecular dynamics simulation into a prediction model to predict the activity of the precursor materials, thereby improving accuracy and shortening time.

[0089] The embodiments of the present invention encompass the molecular level, the microscopic level and the macroscopic level. Molecular simulation belongs to the molecular level, the specific surface area test belongs to the microscopic level, and the standard strength test belongs to the macroscopic mechanical property level. The specific surface area and the ratio parameters are related to the contact degree between the precursor material and the alkali activator, that is, the sufficient degree of reaction of the precursor material. The higher the contact degree, the faster and more sufficient the reaction. The total mass fraction of active silicon and aluminum is related to the amount of substances that can react in the precursor material; the active silicon-aluminum ratio and the active calcium mass fraction are related to the composition structure of the product formed after the material reaction. Different product structures will affect its macroscopic mechanical strength and thus affect the calculated activity index. Different active silicon-aluminum ratios and active calcium mass fractions combined with molecular dynamics simulation can be used to obtain different product structures and analyze and calculate the mechanical properties of the product structure. Therefore, the method of the embodiment of the present invention is suitable for the activity prediction of all alkali-activated reaction precursors (containing active silicon, aluminum and calcium components), including but not limited to materials such as metakaolin, slag, fly ash and construction solid waste powder.

[0090] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation, characterized in that: The following steps are involved: S1, obtaining the physical and chemical properties of a plurality of different alkali-activated precursor materials; the physical and chemical properties of the precursor materials include the ratio parameter and shape parameter of the material particle size, the specific surface area, the total mass fraction of active silicon and aluminum, the molar ratio of active silicon and aluminum, and the mass fraction of active calcium; S2, obtaining the molecular dynamics parameters of each precursor material in S1 under the corresponding physical and chemical properties; the molecular dynamics parameters include the weighted average bond length, Si-O-Al bond angle distribution ratio and elastic modulus of the alkali-activated material gel; S3, obtaining the strength activity index of each precursor material in S1 through a traditional unconfined compressive strength test; S4, sorting the physical and chemical properties and molecular dynamics parameters according to the significance of their influence on the strength activity index of the precursor materials, eliminating the non-significant parameters, and retaining the significant influence parameters; based on the correspondence between the significant influence parameters and the strength activity index of each precursor material, a prediction model of the precursor base excitation reaction activity index with the significant influence parameters as variables is established through multiple linear regression combined with artificial neural network algorithm; In S4, the shape parameters and bond angle distribution ratio of the precursor material particle size are eliminated, and the estimation model of the precursor base excitation reaction activity index is shown in the following formula: <h2 style=";text-align:left;direction:ltr">F<h2 style=";text-align:left;direction:ltr"> x <h2 style=";text-align:left;direction:ltr"> = a1+a2k1+a3k3+a4k4+a5k5+a7k7+a8k8+a9k9 In the formula, F x is the precursor base excitation reaction activity index based on physicochemical properties and molecular simulation, k1 represents the proportional parameter of the precursor material particle size, k3 represents the total mass fraction of active silicon and aluminum in the precursor material, k4 represents the molar ratio of active silicon and aluminum in the precursor material, k5 represents the weighted average bond length of the precursor material, k7 represents the elastic modulus of the precursor material; k8 represents the specific surface area of ​​the precursor material; k9 represents the active calcium mass fraction of the precursor material; a1 is a constant term, and a2-a9 are the coefficients of each parameter term.

2. The method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation according to claim 1, characterized in that: In S1, the total mass fraction of active silicon and aluminum, the molar ratio of active silicon and aluminum, and the mass fraction of active calcium in the precursor material are obtained by leaching test to obtain a filtrate, and then an inductively coupled plasma emission spectroscopy test is performed.

3. The method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation according to claim 1, characterized in that: In S1, the method for obtaining the ratio parameter and shape parameter of the precursor material particle size is as follows: Perform laser particle size test on the precursor material, and fit the material particle size distribution curve through Weibull distribution function (see the following formula) to determine the ratio parameter and shape parameter of the precursor material particle size; Where: y is the cumulative passing percentage; x is the particle size; k1 is the scale parameter of the Weibull distribution function; k2 is the shape parameter of the Weibull distribution function.

4. The method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation according to claim 1, characterized in that: In S1, the specific surface area of ​​the precursor material is determined by a specific surface area test.

5. The method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation according to claim 1, characterized in that: Specifically, S2 comprises: establishing alkali-activated material gel models with different active silicon-aluminum molar ratios and different active calcium mass fractions in molecular dynamics simulation software according to the obtained total mass fraction of active silicon-aluminum and the active silicon-aluminum molar ratio, and calculating the weighted average bond length, Si-O-Al bond angle distribution ratio and elastic modulus of the alkali-activated material gel corresponding to the active silicon-aluminum molar ratio and active calcium mass fraction in S1.

6. The method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation according to claim 5, characterized in that: The alkali-activated material gel model with different active silicon-aluminum molar ratios and different active calcium mass fractions is established as follows: Na2Si2O5 molecules are used as the starting point for modeling, the Na2Si2O5 molecular model is imported through molecular dynamics simulation software, the original Na ions in the model are deleted, and it is expanded to an amorphous preliminary Si2O5 macromolecular model through the AC module, and part of the Si in the model is modified to Al according to the active silicon-aluminum ratio determined by S1, Ca ions are adsorbed into the model according to the determined active calcium mass fraction, the remaining Na ions are adsorbed, and then H2O molecules are adsorbed into the system cavity through the adsorption module in the molecular dynamics simulation software.

7. The method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation according to claim 1, characterized in that: The S3 is specifically as follows: prepare standard specimens by mixing the precursor materials in S1 with silicate cement of fixed strength grade in a fixed proportion and a fixed water-cement ratio, cure at standard curing temperature and humidity, test to obtain standard strength of the specimens, and calculate the strength activity index of the precursor material based on the standard strength according to the following formula: Where: F c It is the strength activity index; the standard strength unit is MPa.

8. The method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation according to claim 1, characterized in that: In S4, the multivariate analysis of variance method is used to determine the degree of influence of the physicochemical properties and molecular kinetic parameters of the precursor material on the activity index of the precursor material. The degree of influence is represented by the test level. A test level greater than 0.05 indicates that the parameter has no significant effect and is a non-significant parameter; a test level less than or equal to 0.05 indicates a significant influence parameter.

9. The method for predicting the activity of precursor base excitation reaction based on physicochemical properties and molecular simulation according to claim 1, characterized in that: The precursor material is metakaolin, slag, fly ash or construction solid waste powder.

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