A modeling method for synergistic effect of multiple hydration products based on molecular dynamics simulation

Abstract

The application relates to the technical field of material modeling, and discloses a modeling method for the synergistic action of multiple hydration products based on molecular dynamics simulation, which comprises the following steps: based on the atomic coordinates of the hydration products, determining a regulation range and a proportion range, and constructing an initial solid mixture model by using charge balance and water molecule adding mode; configuring simulation environment parameters and force field values for the initial solid mixture model, and executing mechanical structure optimization and dynamics simulation of the initial solid mixture model based on the configuration results, and outputting a stable solid mixture model based on the simulation results; extracting target parameter information of the stable solid mixture model, comparing the target parameter information with experimental standard data, verifying the deviation state of the stable solid mixture model according to the comparison results, optimizing the stable solid mixture model based on the deviation state, and outputting a stable solid mixture model for describing the synergistic action of the hydration products until the deviation state meets the requirements. The application provides direct theoretical support for mechanical property optimization through the analysis of the bonding state and atomic diffusion behavior at the nanoscale level.

Description

Technical Field

[0001] This invention relates to the field of materials modeling technology, and more specifically, to a modeling method for the synergistic effects of multiple hydration products based on molecular dynamics simulations. Background Technology

[0002] Existing research has focused on molecular dynamics modeling of CASH gels. The constructed single-phase system models have provided key theoretical basis for analyzing the nanoscale structure, atomic coordination characteristics and basic properties of hydrated gels. However, most existing modeling schemes are based on simplified ideal single-phase structures and do not fully consider the real hydration environment in which multiple hydration products such as CASH and NASH coexist during the hydration process of cementitious materials. This results in differences between the models and actual hydration products in terms of chemical composition and interfacial interaction characteristics, making it difficult to achieve a quantitative correlation between the nanoscale structure and macroscopic mechanical properties of materials.

[0003] More importantly, there is currently no unified technical standard for modeling methods of complex systems with multiple hydration products, making it impossible to clarify the regulatory mechanism of key component parameters on the nanoscale structure of hydration products, and making it difficult to achieve targeted optimization of material properties through simulation calculations. At the same time, the construction steps of existing nanoscale models of hydration products are cumbersome and have poor repeatability, lacking a simple and efficient standardized modeling process, which greatly limits the in-depth development of molecular dynamics simulation technology in the research and engineering application of hydration mechanisms of cementitious materials.

[0004] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a modeling method for the synergistic effects of multiple hydration products based on molecular dynamics simulations. This method has the advantages of strong versatility and adaptability to the component characteristics of hydration products after hydration of different raw materials, thereby solving the problem of not being able to clearly define the regulatory mechanism of key component parameters on the nanostructure of hydration products.

[0006] (II) Technical Solution To achieve the aforementioned advantages of high versatility and adaptability to the component characteristics of hydration products after hydration of different raw materials, the specific technical solution adopted in this invention is as follows: A modeling method for the synergistic effects of multiple hydration products based on molecular dynamics simulations includes: Based on the molecular structure of the hydration products, atomic coordinates were determined. Using the atomic coordinates, the control range and proportion range were determined. An initial solid-mix model was constructed by using element substitution, charge balance and water molecule addition. Configure simulation environment parameters and force field values ​​for the initial solid-concrete model, and perform mechanical structure optimization and dynamic simulation of the initial solid-concrete model based on the configuration results. Output a stable solid-concrete model based on the simulation results. Extract the target parameter information of the stable solid-mix model and compare it with the experimental standard data. Verify the deviation state of the stable solid-mix model based on the comparison results. Optimize the stable solid-mix model based on the deviation state until the deviation state meets the requirements. Output a stable solid-mix model to describe the synergistic effect of hydration products.

[0007] Preferably, based on the molecular structure of the hydration products, atomic coordinates are determined, and the control range and proportion range are determined using the atomic coordinates. An initial solid-mix model is then constructed using element substitution, charge balance, and water molecule addition methods, including: Obtain molecular structures with similar skeletal characteristics to hydration products, determine the constituent elements of the molecular structures, view the space groups corresponding to the molecular structures, find the lattice constants and angles of the molecular structures based on the space groups, and output the atomic coordinates corresponding to each constituent element. Atomic coordinates are introduced into the crystal lattice, and the molar ratio of each constituent element is determined according to the characterization requirements of X-ray fluorescence spectroscopy and X-ray diffraction. Based on the molar ratio, the control range and proportion range are defined. The molecular structure was adjusted by regulating the range and proportion of elements, charge balance, and water molecule addition, and the adjusted molecular structure was used as the initial solid-mix model.

[0008] Preferably, simulation environment parameters and force field values ​​are configured for the initial solid-concrete model, and mechanical structure optimization and dynamic simulation of the initial solid-concrete model are performed based on the configuration results. The stable solid-concrete model output based on the simulation results includes: Based on the synergistic effect mechanism of hydration products, the atomic potential field of condensed matter optimized molecular simulation is selected as the force field, and the force field interaction potential is clarified by using the atomic potential field of condensed matter optimized molecular simulation, so as to assign values ​​to the force field of the initial solid-mix model to describe the bonding and non-bonding interactions between atoms. Set the temperature and pressure of the simulated environment to complete the core parameter setting of the thermodynamic simulation environment, configure the calculation rules for inter-particle interactions in the initial solid-mix model, and determine the calculation methods for van der Waals forces and electrostatic forces; Based on force field assignment, core parameter setting of thermodynamic simulation environment and calculation rule configuration, the parameter settings of the simulation calculation system are completed; Based on the simulation calculation system, a three-level geometric optimization method is used to eliminate the residual stress inside the initial solid-mix model. Then, isothermal and isobaric technology is used to perform molecular dynamics equilibrium simulation on the optimized initial solid-mix model. Based on the simulation results, a stable solid-mix model is output.

[0009] Preferably, a three-level geometric optimization method is used to eliminate residual stress inside the initial solid-mix model according to the simulation calculation system, and molecular dynamics equilibrium simulation is performed on the optimized initial solid-mix model using isothermal and isobaric technology. Based on the simulation results, a stable solid-mix model is output, including: In the Forcite simulation computing technology application interface, select the geometry optimization function, and after the function selection is completed, import the initial solid-mix model, and set the optimization parameters according to the simulation computing system. Based on the imported results and optimization parameters, a three-level geometric optimization method is run to eliminate the energy peaks and atomic overlaps in the initial solid-mix model, and the configuration and atomic charge distribution of the initial solid-mix model are corrected after the elimination is completed. The initial solid-mix model after configuration and atomic charge distribution correction is subjected to atomic charge distribution and local bond angle distortion processing to complete the geometric optimization operation and obtain the geometrically optimized initial solid-mix model; Verify the rationality of the atomic arrangement and bond configuration of the geometrically optimized initial solid-mix model, and evaluate the elimination of residual stress inside the geometrically optimized initial solid-mix model based on the verification results. When the elimination is completely eliminated, output the initial stable solid-mix model with the lowest energy. Based on the Forcite simulation technology, an initial stable solid-mixture model is imported to perform molecular dynamics equilibrium simulation, and a stable solid-mixture model is output according to the energy stability and temperature convergence state of the simulation results.

[0010] Preferably, the process of importing an initial stable solid-mixture model based on Forcite simulation technology to perform molecular dynamics equilibrium simulation, and outputting a stable solid-mixture model based on the energy stability and temperature convergence state of the simulation results, includes: In the Forcite simulation technology application interface, switch the geometry optimization function to the molecular dynamics simulation function and import the initial stable solid-mix model; Based on the imported results and simulation parameter settings, a molecular dynamics equilibrium simulation is initiated. Temperature and pressure during the simulation process are adjusted using temperature regulation and pressure control techniques. The energy and temperature change states of the initial stable solid-mix model are determined based on the simulation results. The energy stability and temperature convergence of the initial stable solid-mix model are judged based on the energy and temperature change state. According to the judgment result, the initial stable solid-mix model when the energy stability and temperature convergence tend to be stable and without fluctuation is output as the stable solid-mix model.

[0011] Preferably, the molecular dynamics equilibrium simulation is initiated based on the imported results and simulation parameter settings. Temperature and pressure are adjusted during the simulation using temperature and pressure control techniques. The energy and temperature change states of the initial stable solid-mixture model are determined based on the simulation results, including: Verify the atomic coordinates, bonding topology, and charge balance state within the initial stable solid-mix model. Inherit the simulation calculation system based on the verification results, and randomly generate the initial motion velocities of all atoms within the initial stable solid-mix model according to the target initial temperature to complete the initial stable solid-mix model initialization process. Based on the initialization results, the atomic positions of the initial stable solid-mix model are updated using the velocity Verlet formula. The nanoscale structural parameters, including the characteristic bond lengths, characteristic bond angles, and interatomic radial distances of the hydration products, are calculated as the spatial coordinate basis for the calculation of interatomic interactions and net external forces. The instantaneous temperature of the initial stable solid-mix model is determined by calculating the atomic motion velocity based on spatial coordinates and temperature control technology. The atomic velocity is then calibrated to control the temperature of the initial stable solid-mix model. Based on pressure control technology, the pressure of the initial stable solid-mix model is stabilized to the target state by scaling the periodic cell volume in real time, so as to keep the total number of atoms in the initial stable solid-mix model unchanged. Combined with temperature control, the temperature and pressure of the initial stable solid-mix model are coordinated and regulated. Based on the thermodynamic constraints of the real curing environment of the hydration products under collaborative control, repeated simulations are performed according to a set number of times. During the iteration process, the temperature and pressure of the initial stable solid-mix model are recorded at fixed intervals to determine the energy and temperature change state of the initial stable solid-mix model.

[0012] Preferably, the target parameter information of the stable solid-mixture model is extracted and compared with experimental standard data. The deviation state of the stable solid-mixture model is verified based on the comparison results. The stable solid-mixture model is optimized based on the deviation state until the deviation state meets the requirements. The output stable solid-mixture model used to describe the synergistic effect of hydration products includes: Based on the stable solid-mix model, nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters of hydration products were extracted and characterized. Standard samples of cementitious materials with the same composition as the hydration products were prepared. The nanoscale structural parameters of the standard samples of cementitious materials were determined by X-ray diffraction and Fourier transform infrared spectroscopy. The atomic coordination and diffusion behavior of the standard samples of cementitious materials were characterized by rotating nuclear magnetic resonance instrument, and the atomic dynamics parameters were output. The mechanical property parameters of the cementitious material standard specimens were determined by in-situ mechanical testing. The nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters corresponding to the stable solid-mix model were quantitatively compared with the nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters of the cementitious material standard specimens. The relative deviation of the stable solid-mix model is evaluated based on the quantitative comparison results. The matching compliance of the stable solid-mix model is assessed, and the construction parameters of the stable solid-mix model are adjusted based on the matching compliance. The stable solid-mix model is iteratively corrected until the matching compliance meets the requirements, and a stable solid-mix model for describing the synergistic effect of hydration products is output.

[0013] (III) Beneficial Effects Compared with existing technologies, this invention provides a modeling method for the synergistic effects of multiple hydration products based on molecular dynamics simulations, which has the following beneficial effects: (1) This invention proposes a standardized modeling process for the coexistence of multiple hydration products, accurately restores the real gel phase composition of polymers, solves the problem of the disconnect between single phase modeling and actual system, clarifies the regulation mechanism of key parameters on the network structure of coexisting phases, and provides direct theoretical support for the optimization of mechanical properties through the analysis of bonding state and atomic diffusion behavior at the nanoscale level.

[0014] (2) The present invention verifies the modeling process with experimental data. The mechanical performance index of the model is highly consistent with the experimental value, which significantly shortens the material development cycle, reduces the experimental cost, and has strong versatility. It can be adapted to the composition characteristics of hydration products after the hydration of different raw materials, and provides a standardized nano-scale modeling tool.

[0015] (3) The present invention can solve the technical problems in the prior art of lacking a unified standard for modeling systems with synergistic effects of hydration products, large deviation between the model and the actual geological polymer, and inability to accurately reflect the influence of component ratio on micro-nano mechanical properties. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention; Figure 2 This is a flowchart of a modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention. Figure 3 This is an elemental diagram of a solid-mixture model in a modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention. Figure 4This is a structural diagram of a solid-mixture model in a modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention. Figure 5 This is a temperature diagram during the geometry optimization process in the modeling method of synergistic effects of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention. Figure 6 This is an energy diagram during the geometry optimization process in a modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention. Figure 7 This is a result diagram of the radial distribution function in the modeling method of synergistic effects of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention; Figure 8 This is a graph showing the mean square displacement results in the modeling method of synergistic effects of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention. Figure 9 This is a diagram showing the bond angle calculation results in the modeling method of synergistic effects of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention. Detailed Implementation

[0018] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0019] According to an embodiment of the present invention, a modeling method for the synergistic effects of multiple hydration products based on molecular dynamics simulation is provided.

[0020] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figures 1 to 9 As shown, the modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to an embodiment of the present invention includes: Step S1 determines the atomic coordinates based on the molecular structure of the hydration products. Using the atomic coordinates, the control range and proportion range are determined. An initial solid-mix model is constructed by using element substitution, charge balance and water molecule addition.

[0021] In one embodiment, based on the molecular structure of the hydration products, atomic coordinates are determined. Using these atomic coordinates, the control range and proportion range are determined. An initial solid-mix model is then constructed using element substitution, charge balance, and water molecule addition methods. Obtain molecular structures with similar skeletal characteristics to hydration products, determine the constituent elements of the molecular structures, view the space groups corresponding to the molecular structures, find the lattice constants and angles of the molecular structures based on the space groups, and output the atomic coordinates corresponding to each constituent element. Atomic coordinates are introduced into the crystal lattice, and the molar ratio of each constituent element is determined according to the characterization requirements of X-ray fluorescence spectroscopy and X-ray diffraction. Based on the molar ratio, the control range and proportion range are defined. The molecular structure was adjusted by regulating the range and proportion of elements, charge balance, and water molecule addition, and the adjusted molecular structure was used as the initial solid-mix model.

[0022] Specifically, through literature review, the molar ratio of key elements in the hydration products CASH and NASH was determined. In addition, molecular templates matching the hydration products CASH and NASH were selected based on the literature review. Atomic coordinates were retrieved using FindIt software and imported into Materials Studio. An initial solid-mix model was constructed by element substitution, charge balance, and water molecule addition.

[0023] The Na₂Si₂O₅ molecular structure model from the FindIt software database was imported into the molecular dynamics simulation software. All Si atoms were selected, and the atomic composition was changed to 50% Si atoms and 50% Al atoms. Then, Ca atoms around Si were randomly added and redistributed, and finally, a certain amount of OH groups were added. - [AlO4] is obtained by replacing Si with Al atoms. 5- Structure, [SiO4] 4- The added OH- ions carry a negative charge, while Na and Ca, when present in ionic form, carry a positive charge. Therefore, Na... + Ca 2+ It can help balance [AlO4] 5- Structure and [SiO4] 4- The structure plays a role in maintaining the overall charge balance of the structure. The initial molecular model is optimized and adjusted to eliminate unreasonable configuration defects in artificial modeling and ensure that the constructed geopolymer model has a high degree of consistency with the actual results.

[0024] It should be noted that this example uses the Na2Si2O5 molecular structure as an example. Based on literature review, existing molecular models with CASH and NASH were found. In the FindIt software, the elements Na, Si, and O were selected, and Exclusive AND was selected in Type. The Search was then clicked to find the Na2Si2O5 molecule. The corresponding Space Group and Space Group Number (SG number) were found in the Space Group. The corresponding lattice constant and angle will be displayed in Unit Cell. The corresponding atomic coordinates will be given for each Na, Si, and O.

[0025] In Materials Studio, create a new file, click Build, select Crystals, and perform a Crystal Build. Based on the information obtained from FindIt, enter the space group number in the Enter group field. Click Lattice Parameters and enter the Lengths (Å) corresponding to the lattice constants a, b, and c. Click Build again, select a portion of Si atoms, replace them with Al atoms, and adjust the ratio to 50% Si and 50% Al. Then, randomly add and redistribute Ca atoms around the Si atoms. Finally, add a certain amount of OH groups. - .

[0026] Import the coordinates of each atom in the lattice, click Build, select Add Atoms, select Na in the Element, input the a, b, and c values ​​of an atom from the FindIt software, and click Add. Repeat the above steps to build the remaining Na atoms. After building the Na atoms, the input method for Si and O atoms is the same as for Na. Finally, the Na2Si2O5 molecular model is built.

[0027] Step S2: Configure simulation environment parameters and force field values ​​for the initial solid-mixed model, and perform mechanical structure optimization and dynamic simulation of the initial solid-mixed model based on the configuration results. Output a stable solid-mixed model based on the simulation results.

[0028] Its main purpose is to match the synergistic mechanism of hydration products CASH and NASH. Through the simulation system parameterization method, force field values ​​are assigned to the target system, thermodynamic simulation environment parameters are set and inter-particle interaction calculation rules are configured. Furthermore, three-level geometric optimization is used to eliminate internal stress in the model, and the stability of the CASH and NASH model structures is achieved through NPT ensemble dynamic equilibrium.

[0029] In one embodiment, simulation environment parameters and force field values ​​are configured for an initial solid-mixed model, and mechanical structure optimization and dynamic simulation of the initial solid-mixed model are performed based on the configuration results. A stable solid-mixed model is output based on the simulation results, including: Based on the synergistic effect mechanism of hydration products, the atomic potential field of condensed matter optimized molecular simulation is selected as the force field, and the force field interaction potential is clarified by using the atomic potential field of condensed matter optimized molecular simulation, so as to assign values ​​to the force field of the initial solid-mix model to describe the bonding and non-bonding interactions between atoms. Set the temperature and pressure of the simulated environment to complete the core parameter setting of the thermodynamic simulation environment, configure the calculation rules for inter-particle interactions in the initial solid-mix model, and determine the calculation methods for van der Waals forces and electrostatic forces; Based on force field assignment, core parameter setting of thermodynamic simulation environment and calculation rule configuration, the parameter settings of the simulation calculation system are completed; Based on the simulation calculation system, a three-level geometric optimization method is used to eliminate the residual stress inside the initial solid-mix model. Then, isothermal and isobaric technology is used to perform molecular dynamics equilibrium simulation on the optimized initial solid-mix model. Based on the simulation results, a stable solid-mix model is output.

[0030] In one embodiment, the force field potential includes bond energy diagonal terms, bond energy cross terms, and non-bond energy. The formula for calculating the potential of the force field is as follows: ; In the formula, Representing the total potential energy, it is a function of the coordinates of all atoms and is used to measure the energy level of a conformation. This represents the diagonal term of the valence bond (the main term of bonding), which describes the fundamental potential energy contribution related to the covalent bond and corresponds to terms 1-4 in the formula (bond stretching, bond angle bending, dihedral angle twisting, and out-of-plane bending). This represents the valence bond cross-coupling term, describing the coupling effect between coordinates within different valence bonds. It corresponds to terms 5-9 in the formula and is used to improve the accuracy of the force field's description of potential energy. This represents the non-bonded interaction term, describing the non-covalent interactions between atoms that are not directly bonded (non-adjacent atoms within a molecule, and atoms between different molecules), corresponding to term 10-11 in the formula. The constant representing the stretching force of a bond characterizes its rigidity; the larger the value, the more difficult the bond is to stretch / compress. It is a core parameter for force field fitting. and This represents two adjacent covalent bonds. Indicates an index. The width parameter, related to the bond vibration frequency, determines the steepness of the potential energy curve. Indicates the instantaneous bond length of a covalent bond. The equilibrium bond length of a covalent bond, i.e., the bond length at which the potential energy is lowest, is an empirical fitting parameter for the force field. The equilibrium bond angle, representing the bond angle at which the potential energy is lowest, is a force field fitting parameter. Indicates the bond angle. The bending force constant of the bond angle represents the rigidity of the bond angle; the larger the value, the more difficult the bond angle is to bend. and These represent the deviations between the instantaneous angles and the equilibrium angles of the two bond angles, respectively. The torsional constant representing the dihedral angle. This represents the sign factor, taking a value of ±1, used to adjust the phase of the potential energy curve and determine the location of the potential energy minimum. Indicates multiplicity (number of periods), describing the number of potential energy minima during a 360° rotation of the dihedral angle (e.g., n=3 for single bonds and n=2 for double bonds). This represents the instantaneous torsional angle value of the dihedral angle. This represents all out-of-plane curvature coordinates (angles from the surface) in the traversal system. The force constant representing out-of-plane bending characterizes the rigidity of a planar structure; the larger the value, the more difficult it is for atoms to deviate from the equilibrium plane. In equation 10, the potential well depth is represented, characterizing the strength of the van der Waals attraction between two atoms; it is a force field parameter related to the atom type. In equation 11, the dielectric constant is represented, which is the vacuum dielectric constant under vacuum conditions. In the implicit solvent model, is the effective dielectric constant, used to describe the shielding effect of the medium on electrostatic interactions. This represents the collision diameter, i.e., the interatomic spacing when the potential energy is 0, corresponding to the van der Waals radius of the atom, and is a force field parameter. and Represents atoms and atoms The partial charge (atomic charge) describes the charged state of an atom and is a parameter for fitting the force field. Represents atoms and atoms The instantaneous time interval.

[0031] In one embodiment, a three-level geometric optimization method is used to eliminate residual stress inside the initial solid-mix model based on the simulation calculation system, and molecular dynamics equilibrium simulation is performed on the optimized initial solid-mix model using isothermal and isobaric techniques. Based on the simulation results, a stable solid-mix model is output, including: In the Forcite simulation computing technology application interface, select the geometry optimization function, and after the function selection is completed, import the initial solid-mix model, and set the optimization parameters according to the simulation computing system. Based on the imported results and optimization parameters, a three-level geometric optimization method is run to eliminate the energy peaks and atomic overlaps in the initial solid-mix model, and the configuration and atomic charge distribution of the initial solid-mix model are corrected after the elimination is completed. The initial solid-mix model after configuration and atomic charge distribution correction is subjected to atomic charge distribution and local bond angle distortion processing to complete the geometric optimization operation and obtain the geometrically optimized initial solid-mix model; Verify the rationality of the atomic arrangement and bond configuration of the geometrically optimized initial solid-mix model, and evaluate the elimination of residual stress inside the geometrically optimized initial solid-mix model based on the verification results. When the elimination is completely eliminated, output the initial stable solid-mix model with the lowest energy. Based on the Forcite simulation technology, an initial stable solid-mixture model is imported to perform molecular dynamics equilibrium simulation, and a stable solid-mixture model is output according to the energy stability and temperature convergence state of the simulation results.

[0032] In one embodiment, an initial stable solid-mixture model is imported based on Forcite simulation technology to perform molecular dynamics equilibrium simulation, and the stable solid-mixture model is output based on the energy stability and temperature convergence state of the simulation results, including: In the Forcite simulation technology application interface, switch the geometry optimization function to the molecular dynamics simulation function and import the initial stable solid-mix model; Based on the imported results and simulation parameter settings, a molecular dynamics equilibrium simulation is initiated. Temperature and pressure during the simulation process are adjusted using temperature regulation and pressure control techniques. The energy and temperature change states of the initial stable solid-mix model are determined based on the simulation results. The energy stability and temperature convergence of the initial stable solid-mix model are judged based on the energy and temperature change state. According to the judgment result, the initial stable solid-mix model when the energy stability and temperature convergence tend to be stable and without fluctuation is output as the stable solid-mix model.

[0033] In one embodiment, a molecular dynamics equilibrium simulation is initiated based on the imported results and simulation parameter settings. Temperature and pressure are adjusted during the simulation using temperature regulation and pressure control techniques. The energy and temperature change states of the initial stable solid-mixture model are determined based on the simulation results, including: Verify the atomic coordinates, bonding topology, and charge balance state within the initial stable solid-mix model. Inherit the simulation calculation system based on the verification results, and randomly generate the initial motion velocities of all atoms within the initial stable solid-mix model according to the target initial temperature to complete the initial stable solid-mix model initialization process. Based on the initialization results, the atomic positions of the initial stable solid-mix model are updated using the velocity Verlet formula. The nanoscale structural parameters, including the characteristic bond lengths, characteristic bond angles, and interatomic radial distances of the hydration products, are calculated as the spatial coordinate basis for the calculation of interatomic interactions and net external forces. The instantaneous temperature of the initial stable solid-mix model is determined by calculating the atomic motion velocity based on spatial coordinates and temperature control technology. The atomic velocity is then calibrated to control the temperature of the initial stable solid-mix model. Based on pressure control technology, the pressure of the initial stable solid-mix model is stabilized to the target state by scaling the periodic cell volume in real time, so as to keep the total number of atoms in the initial stable solid-mix model unchanged. Combined with temperature control, the temperature and pressure of the initial stable solid-mix model are coordinated and regulated. Based on the thermodynamic constraints of the real curing environment of the hydration products under collaborative control, repeated simulations are performed according to a set number of times. During the iteration process, the temperature and pressure of the initial stable solid-mix model are recorded at fixed intervals to determine the energy and temperature change state of the initial stable solid-mix model.

[0034] It should be noted that step S2 is implemented by using Materials Studio 2020 software to perform energy minimization and molecular dynamics simulations on a simulated mixture model with multiple hydration products coexisting. The Forcite module of Materials Studio 2020 software is used for a three-level geometric optimization with a maximum of 500 steps. The COMPASS II force field and charge balance method is used throughout the process. Before optimization, charge balance verification, Si-O / Al-O covalent bond topological locking, and a periodic unit cell setting of at least 12.5 Å are performed. Van der Waals interactions are performed using the atom-based method (cutoff radius 12.5 Å), and long-range electrostatic interactions are performed using the PPM method. The core calculation rules are not changed throughout the process; only gradient optimization is performed by switching algorithms.

[0035] In Forcite Calculation, select Geometry Optimization as the Task. Setting the Quality to Medium or Fine is sufficient for most computational requirements. Click More, and in Algorithm, select Steepest descent, Conjugate gradient, and Quasi-Newton, or directly select the Smart algorithm to balance convergence speed and accuracy. Convergence tolerance usually does not need to be modified manually; just follow the default settings for Quality in Setup. Finally, set Max.iterations to 500, and click Run to complete the calculation.

[0036] After the entire process is optimized, a closed-loop verification and evaluation is performed. Therefore, the Reflex module in MS software is needed to calculate XRD to determine the crystallization status. The specific operation is as follows: select powder defraction in the toolbar, open it, click Calculate in the tab, observe the diffraction peaks, and compare them with the XRD diffraction peaks of the specimens in the subsequent experiments. This avoids over-optimization that may lead to structural distortion and ensures that the hydration products are consistent with the characteristics of the actual system. After all verifications are passed, a qualified optimized model that can be directly used for subsequent kinetic simulations is obtained.

[0037] The simulation results need to be compared with actual experimental values; therefore, the simulation environment should be close to the actual values. The simulation environment was set at 298K (room temperature) and a standard atmosphere. The NPT ensemble can control atomic number, pressure, temperature, and volume; therefore, the NPT ensemble was chosen for the simulation calculation. To make the microstructure of the model more closely resemble the macrostructure, periodic boundary conditions were applied as model boundaries. Then, the final molecular dynamics simulation was performed. During the molecular dynamics simulation, pre-simulation initialization and basic parameter verification were completed, verifying the atomic coordinates, bonding topology, and charge balance state within the system. The previously set periodic boundary conditions, all parameters of the COMPASS II force field, and the rules for calculating van der Waals forces using the Atombased method and long-range electrostatic interactions using the PPPM method were inherited. Simultaneously, based on the target initial temperature of 298K, the initial velocities of all atoms in the system were randomly generated according to the Maxwell-Boltzmann distribution to complete the simulation system initialization. Then, the time-domain integration core calculation was performed, using the Velocity Verlet algorithm adapted to inorganic gelled systems as the time-domain integration core algorithm. The formula is as follows: ; ; In the formula, Represents the three-dimensional position vector of an atom. Represents the three-dimensional velocity vector of an atom. Represents the three-dimensional acceleration vector of an atom. Indicates the current simulation time. This indicates the simulation time step size.

[0038] After updating all atomic positions in each integration step of the Velocity Verlet algorithm, the nanoscale structural parameters of the hydration product system, such as Si-O and Al-O characteristic bond lengths, Si-O-Si / Al characteristic bond angles, and interatomic radial distances, can be calculated in real time. At the same time, it provides a spatial coordinate basis for the recalculation of interatomic interactions and net external forces in the next step.

[0039] The atomic velocities obtained by solving directly determine the instantaneous temperature of the simulated system and are the core control object for achieving 298K isothermal control using the Nose hot bath method. Through real-time calibration and adjustment of the atomic velocities, the system temperature can be precisely stabilized at the preset target value. Furthermore, this atomic velocity data is the core data source for subsequent calculations of the mean square displacement (MSD) and diffusion coefficient, accurately characterizing the Na+ in the CASH and NASH coexisting system. + Ca 2+ OH - The migration behavior of plasma provides key quantitative support for elucidating the synergistic mechanism of the two hydration products.

[0040] Using a fixed integration step size of 1.0fs as set in this embodiment, the single-step iteration of updating atomic positions, solving for bonding and non-bonding interactions between atoms and the net external force, and updating atomic motion velocity is completed sequentially within each integration step. This iteration continues until the cumulative simulation time reaches 500ps, completely solving the motion trajectory of all atoms in the system throughout the entire period. During the full-cycle iterative calculation, the function synchronously executes real-time closed-loop control of the thermodynamic state under the NPT ensemble. The Nose hot bath method specified in this embodiment is used to adjust the atomic motion velocity in real time, stabilizing the system temperature at 298K. Simultaneously, the Berendsen pressure control method is used to stabilize the system pressure at 1 standard atmosphere by scaling the periodic unit cell volume in real time. The total number of atoms in the system is kept constant throughout the process, achieving coordinated control of temperature, pressure, and volume, restoring the thermodynamic constraints of the real curing environment of hydration products, and synchronously completing the system's original... The sampling and recording of sub-trajectory data and thermodynamic data such as total potential energy, temperature, pressure, and cell density provide a complete data source for subsequent system convergence determination and core parameter extraction. When the simulation time reaches 500 ps, ​​the function performs a system equilibrium steady-state convergence determination. The core criteria are that the system temperature fluctuates within ≤ ±5% near the target value, the total potential energy has no continuous rising or falling trend and fluctuates within ≤ ±3%, and the characteristic bond lengths and bond angles of Si-O and Al-O are stable. If the convergence requirements are not met, the simulation time is automatically extended until the system fully converges. Finally, the function outputs the equilibrium configuration file with the lowest energy and complete elimination of intermolecular internal stress, as well as the atomic trajectory and thermodynamic data files for the entire simulation cycle, completing the entire dynamic simulation process. The output results can be directly connected to the subsequent extraction of core parameters of the nanostructure, dynamics, and mechanical properties of hydration products, as well as the comparison and verification with experimental test data.

[0041] Specifically, the process of performing the initial solid-mixed model mechanical structure optimization and dynamic simulation requires clicking the Forcite module, selecting Forcite Calculation to redistribute the force field and charge, and selecting COMPASS II for the force field.

[0042] Specifically, click "Forcite Calculation" and select "Geometry Optimization" to perform geometric optimization on the solid-mixture model. This corrects the bond configuration and atomic charge distribution, making the structure of each model more reasonable and obtaining a low-energy configuration for the monomer molecule model. The calculation accuracy is set to "Medium," and the steepest, quasi-Newton, and Newton-Raphson algorithms are used. The maximum number of iterations is set to 500. The force field is set to "COMPASS II," and the electrostatic force is calculated using PPM, while the van der Waals force is calculated using an atom-based algorithm.

[0043] Open Forcite Calculation and select the Dynamics module to perform molecular dynamics simulation on the solid-mixture model. Select NPT ensemble to control the simulation process, select Random for the initial velocity, select 298K for the temperature, set the time step to 1fs, the total simulation time to 500ps, select Nose for the thermostat, select COMPASS II for the force field, use PPM to calculate the electrostatic force, and use Atom-based to calculate the van der Waals force.

[0044] In this simulation, the atom-based method was used to calculate van der Waals forces, and the PPM method was used to calculate electrostatic forces. These correspond to the two core terms of nonbonded interactions in the COMPASS II force field, respectively. Both methods have clear calculation formulas and calculation logic adapted to the periodic hydration product system, as detailed below: In the atom-based method for calculating van der Waals forces, the core formula for the COMPASS II force field is: ; In the formula, , Indicates non-bonded atoms within the system; Indicates the depth of the potential well for atoms, The collision diameter is calculated from the atomic parameters provided by COMPASS II according to the Lorentz-Berthelot mixing rule. It represents the instantaneous time interval between atoms.

[0045] When calculating electrostatic force using the PPM method, the core formula for the COMPASS II force field is: ; In the formula, , This represents the partial charge of atoms distributed by the COMPASS II force field. It represents the vacuum permittivity.

[0046] After the above optimization, the density of the obtained stable unit cell model converges, resulting in a stable solid-mixture model with the lowest energy and the least intermolecular stress.

[0047] Step S3: Extract the target parameter information of the stable solid-mix model and compare it with the experimental standard data. Verify the deviation state of the stable solid-mix model based on the comparison results. Optimize the stable solid-mix model based on the deviation state until the deviation state meets the requirements. Output the stable solid-mix model to describe the synergistic effect of hydration products.

[0048] In one embodiment, the target parameter information of the stable solid-mixture model is extracted and compared with experimental standard data. The deviation state of the stable solid-mixture model is verified based on the comparison results. The stable solid-mixture model is optimized based on the deviation state until the deviation state meets the requirements. The output stable solid-mixture model used to describe the synergistic effect of hydration products includes: Based on the stable solid-mix model, nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters of hydration products were extracted and characterized. Standard samples of cementitious materials with the same composition as the hydration products were prepared. The nanoscale structural parameters of the standard samples of cementitious materials were determined by X-ray diffraction and Fourier transform infrared spectroscopy. The atomic coordination and diffusion behavior of the standard samples of cementitious materials were characterized by rotating nuclear magnetic resonance instrument, and the atomic dynamics parameters were output. The mechanical property parameters of the cementitious material standard specimens were determined by in-situ mechanical testing. The nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters corresponding to the stable solid-mix model were quantitatively compared with the nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters of the cementitious material standard specimens. The relative deviation of the stable solid-mix model is evaluated based on the quantitative comparison results. The matching compliance of the stable solid-mix model is assessed, and the construction parameters of the stable solid-mix model are adjusted based on the matching compliance. The stable solid-mix model is iteratively corrected until the matching compliance meets the requirements, and a stable solid-mix model for describing the synergistic effect of hydration products is output.

[0049] It should be explained that the main purpose of step S3 is to extract the core parameters characterizing the synergistic effect of CASH and NASH in the molecular dynamics simulation system, measure the corresponding parameters of the actual system through experimental methods such as micro-nano mechanical performance testing, quantitatively compare the simulation parameters with the experimental test data, iteratively adjust the model construction parameters according to the deviation results, and repeatedly optimize until the parameter matching degree between the simulation system and the actual system meets the requirements, thus completing the accurate correction of the model.

[0050] Specifically, to achieve the above objectives, based on the model after achieving kinetic equilibrium, three types of core parameters characterizing the synergistic effect of multiple hydration products are extracted, including nanoscale structural parameters (characteristic bond lengths of Si-O and Al-O, characteristic bond angles of Si-O-Si, Si-O-Al, and O-Al-O, and radial distribution function RDF), atomic dynamics parameters (mean square displacement MSD of core atoms of Si, Al, Ca, and Na), and mechanical property parameters (elastic modulus of the system).

[0051] Simultaneously, key parameters of the simulation model need to be extracted, including bond angles, radial distribution function (RDF), mean square displacement of atoms (MSD), and elastic modulus. The above simulation parameters are compared with the bond lengths, bond angles, RDF, MSD, and elastic modulus data of the experimental specimens. If the deviation of any parameter exceeds 5%, the C / S ratio, water molecule content, or force field parameters are adjusted. The steps are repeated 2 to 4 times until the simulation results are consistent with the experimental data trend, ensuring that the model can accurately reflect the synergistic mechanism of CASH and NASH and the true structure and performance relationship of the solid-mixed system.

[0052] When testing the experimental baseline data, standard samples of CASH and NASH cementitious materials with the same chemical composition as the simulation system need to be prepared, and the experimental baseline data are obtained by corresponding standardized testing methods: the nanoscale structural parameters are determined by X-ray diffraction combined with Rietveld refinement and Fourier transform infrared spectroscopy; the atomic coordination and diffusion behavior are characterized by 29Si and 27Al rotating nuclear magnetic resonance instruments, and the corresponding MSD parameters are verified; the elastic modulus is determined by nanoscale mechanical characterization methods such as nanoindentation, atomic force microscopy, and in-situ mechanical testing based on high-resolution transmission electron microscopy.

[0053] When comparing data and determining deviations, the simulated parameters are quantitatively compared with the experimental baseline data one by one, and the relative deviation of each parameter is calculated according to the formula: relative deviation = |simulated value - experimental value| / experimental value × 100%; if the relative deviation of any parameter exceeds 5%, the model is deemed to be unsuitable for the actual system. During model iteration and correction, the model construction parameters are adjusted in a targeted manner according to the type of deviation. For deviations in nanoscale structural parameters, the calcium-silicon ratio (C / S), silicon-aluminum ratio (Si / Al), or sodium-aluminum ratio (Na / Al) are adjusted. For deviations in kinetic parameters, the amount and distribution of water molecules are adjusted. For deviations in mechanical and density parameters, the non-bonded interaction parameters of the force field are fine-tuned. After parameter adjustment, the steps of model construction, parameter configuration, geometric optimization, and kinetic equilibrium operation are repeated, and the parameters are compared again until the relative deviations of all core parameters are ≤5%, thus completing the final model correction.

[0054] Specifically, standard samples of CASH and NASH cementitious materials with chemical compositions consistent with the simulated system were prepared. The entire process of sample preparation, curing, and testing followed current national and industry standards. X-ray diffraction (XRD) was used to characterize the phase and crystal form of the powder samples. The testing conditions were Cu target, tube voltage 40 kV, tube current 40 mA, scanning range 5° to 90°, scanning step size 0.02°, and scanning speed 2° / min. The characteristic bond lengths and bond angles of Si-O and Al-O in the samples were obtained by fitting the XRD patterns using the Rietveld refinement method. Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy were used for auxiliary characterization, with a wavenumber range of 400 cm⁻¹ to 4000 cm⁻¹. The bonding characteristics of silicon-oxygen tetrahedra and aluminum-oxygen tetrahedra were verified by characteristic absorption peaks, and the bond length and bond angle test results were cross-validated. The radial distribution function of atomic pairs in the actual system was obtained by fitting the peak shape of the XRD patterns and converting the radial distribution function, and compared with the simulated RDF results.

[0055] The samples were tested using a magic-angle rotating NMR spectrometer for 29Si and 27Al. The resonance frequency of 29Si was 79.49 MHz, the magic-angle rotation speed was 10 kHz, and the relaxation delay time was 10 s. The resonance frequency of 27Al was 104.23 MHz, the magic-angle rotation speed was 12 kHz, and the relaxation delay time was 0.5 s. By peak fitting of the spectra, the coordination environment changes and migration characteristics of Si and Al atoms were quantitatively analyzed. Combined with quasi-elastic neutron scattering tests, the atomic diffusion coefficient was obtained, which verified the MSD curve and atomic diffusion behavior obtained from the simulation. The nanoindentation method was selected, and a Berkovich triangular diamond indenter adapted for nanoscale testing was used. After basic instrument calibration, the sample surface was observed using a matching optical microscope. Pure phase regions of hydration products without pores or cracks were selected. Test points were planned according to the grid method to ensure that there was no stress field superposition interference between adjacent indentation points. The automatic testing program was started to complete the indentation test at all points, and the load-displacement curve was recorded in real time. Abnormal data falling in defect areas were simultaneously eliminated. The Oliver-Pharr model was used to fit and calculate the effective test data to obtain the Young's modulus and nanoscale hardness of the sample at the nanoscale. The average value of the effective data was used as the experimental benchmark value to obtain experimental test results that directly match and verify the nanoscale elastic modulus parameters extracted by molecular dynamics simulation.

[0056] The Oliver-Pharr model is a classic contact mechanics model for quantitatively characterizing the nanoscale Young's modulus and nanoscale hardness of CASH and NASH hydration products in nanoindentation testing. It is also the core calculation framework of the ISO 14577 and ASTM E2546 nanoscale hardness testing standards. Based on Hertz elastic contact mechanics, it systematically extends the contact behavior of a pyramidal indenter in indenting elasto-plastic materials. By correcting the elastic recovery of the material during the indentation process through the initial stiffness of the unloading curve, it solves for the true contact depth and contact area, making it suitable for the nanoscale mechanical characterization needs of low-stiffness, porous viscoelastic-plastic materials such as hydrated gels.

[0057] The fixed parameters tested in this embodiment are: Berkovich indenter depth correction coefficient ε=0.75, geometric correction coefficient β=1.034, diamond indenter Young's modulus 1141GPa, Poisson's ratio 0.07, and hydration product Poisson's ratio ranging from 0.22 to 0.25. The specific fitting calculation process is as follows: First, the original load-displacement curve of the nanoindentation is preprocessed to remove abnormal data such as porosity and sudden jumps. For the unloading segment of the effective curve from 20% to 95% of the maximum load range, a power function is used for nonlinear fitting, and the contact stiffness at the maximum load is obtained by differentiation. Then, the actual contact depth is corrected and calculated, and the actual contact area is obtained by combining the pre-calibrated indenter area function. Finally, the nanohardness is calculated, and the sample nanoscale Young's modulus is converted. The average value of the effective data is used as the experimental benchmark value for comparison with the simulation results. During the test, the loading rate needs to be controlled to keep the creep effect of hydration products within 5% to ensure the calculation accuracy.

[0058] The extracted simulation parameters are quantitatively compared one-to-one with the baseline data obtained from the above experimental tests. If the relative deviation of all core parameters is ≤5%, the model is deemed to meet the matching standard with the actual hydration system. Based on the type of parameter deviation, the key parameters of the model construction are adjusted in a targeted manner to establish a precise correspondence between deviation type and adjusted parameters. If the deviation of nanoscale structural parameters (bond length, bond angle, RDF) exceeds the standard, the calcium-silicon ratio (C / S), silicon-aluminum ratio (Si / Al), or sodium-aluminum ratio (Na / Al) of the model is adjusted to optimize the coordination structure of the tetrahedral network. If the atomic dynamics parameters (MSD, diffusion coefficient) deviate beyond the standard, adjust the amount of water molecules added, their distribution state, and the proportion of water in the interlayer in the model to optimize the ion migration environment of the two-phase system; if the mechanical parameters deviate beyond the standard, fine-tune the nonbonded interaction parameters of the COMPASS II force field to optimize the calculation accuracy of van der Waals forces and electrostatic interactions within the system.

[0059] After the parameters are adjusted, repeat the entire process of initial model reconstruction, simulation system parameterization, three-level geometric optimization and NPT ensemble dynamic equilibrium. Extract the simulation parameters again and compare them with the experimental baseline data until the relative deviation of all core parameters is ≤5%, and complete the final correction of the model.

[0060] Although this embodiment demonstrates the operation using Materials Studio software, similar molecular dynamics simulations can be performed in other molecular dynamics software. Furthermore, while the Na2Si2O5 molecular model is selected as the initial model for the solid-mixture system, the applicability of the modeling method in this embodiment is not limited to this. Any molecular model that meets the bonding mode, tetrahedral coordination environment, and charge balance requirements of the CASH and NASH structures, and can provide stable skeletal support for their synergistic effect, can be used as the initial model for this solid-mixture system. Specifically, this includes, but is not limited to, sodium hydrate hydrate series molecules, aluminosilicate precursor molecules, and Ca / Na-containing aluminosilicate basic molecules. It is not limited by specific structural parameters such as single molecule composition, number of functional groups, or molecular chain length, significantly improving the versatility and adaptability of the method.

[0061] According to another embodiment of the present invention, a modeling system for the synergistic effects of multiple hydration products based on molecular dynamics simulations is also provided, the system comprising: The initial solid-mix model construction module is used to determine the atomic coordinates based on the molecular structure of the hydration products, determine the control range and proportion range through the atomic coordinates, and construct the initial solid-mix model by means of element substitution, charge balance and water molecule addition. The initial solid-mixed model optimization module is used to configure simulation environment parameters and force field values ​​for the initial solid-mixed model, and to perform mechanical structure optimization and dynamic simulation of the initial solid-mixed model based on the configuration results, and output a stable solid-mixed model based on the simulation results. The model validation, optimization, and correction module is used to extract the target parameter information of the stable solid-mix model and compare it with experimental standard data. Based on the comparison results, it verifies the deviation state of the stable solid-mix model, optimizes the stable solid-mix model based on the deviation state, until the deviation state meets the requirements, and outputs a stable solid-mix model to describe the synergistic effect of hydration products.

[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A modeling method for the synergistic effects of multiple hydration products based on molecular dynamics simulations, characterized in that, The method includes: Based on the molecular structure of the hydration products, atomic coordinates were determined. Using the atomic coordinates, the control range and proportion range were determined. An initial solid-mix model was constructed by using element substitution, charge balance and water molecule addition. Configure simulation environment parameters and force field values ​​for the initial solid-concrete model, and perform mechanical structure optimization and dynamic simulation of the initial solid-concrete model based on the configuration results. Output a stable solid-concrete model based on the simulation results. Extract the target parameter information of the stable solid-mix model and compare it with the experimental standard data. Verify the deviation state of the stable solid-mix model based on the comparison results. Optimize the stable solid-mix model based on the deviation state until the deviation state meets the requirements. Output a stable solid-mix model to describe the synergistic effect of hydration products.

2. The modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 1, characterized in that, The process involves determining atomic coordinates based on the molecular structure of hydration products, using these coordinates to define the control and proportion ranges, and constructing an initial solid-mix model using element substitution, charge balance, and water molecule addition. Obtain molecular structures with similar skeletal characteristics to hydration products, determine the constituent elements of the molecular structures, view the space groups corresponding to the molecular structures, find the lattice constants and angles of the molecular structures based on the space groups, and output the atomic coordinates corresponding to each constituent element. Atomic coordinates are introduced into the crystal lattice, and the molar ratio of each constituent element is determined according to the characterization requirements of X-ray fluorescence spectroscopy and X-ray diffraction. Based on the molar ratio, the control range and proportion range are defined. The molecular structure was adjusted by regulating the range and proportion of elements, charge balance, and water molecule addition, and the adjusted molecular structure was used as the initial solid-mix model.

3. The modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 1, characterized in that, The process involves configuring simulation environment parameters and force field values ​​for the initial solid-mixed model, and then performing mechanical structure optimization and dynamic simulation of the initial solid-mixed model based on the configuration results. The output of a stable solid-mixed model based on the simulation results includes: Based on the synergistic effect mechanism of hydration products, the atomic potential field of condensed matter optimized molecular simulation is selected as the force field, and the force field interaction potential is clarified by using the atomic potential field of condensed matter optimized molecular simulation, so as to assign values ​​to the force field of the initial solid-mix model to describe the bonding and non-bonding interactions between atoms. Set the temperature and pressure of the simulated environment to complete the core parameter setting of the thermodynamic simulation environment, configure the calculation rules for inter-particle interactions in the initial solid-mix model, and determine the calculation methods for van der Waals forces and electrostatic forces; Based on force field assignment, core parameter setting of thermodynamic simulation environment and calculation rule configuration, the parameter settings of the simulation calculation system are completed; Based on the simulation calculation system, a three-level geometric optimization method is used to eliminate the residual stress inside the initial solid-mix model. Then, isothermal and isobaric technology is used to perform molecular dynamics equilibrium simulation on the optimized initial solid-mix model. Based on the simulation results, a stable solid-mix model is output.

4. The modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 3, characterized in that, The force field potential includes bond energy diagonal terms, bond energy cross terms, and non-bond energy; The formula for calculating the potential of the force field is: ； In the formula, Represents the total potential energy. Indicates the diagonal terms of the valence key. Indicates valence key cross terms. Represents non-bonded interaction terms. This represents the stretching force constant of the bond. and This represents two adjacent covalent bonds. Indicates an index. Indicates the width parameter. Indicates the instantaneous bond length of a covalent bond. Indicates the equilibrium bond length of a covalent bond. Indicates the bond angle. The bending force constant representing the key angle. and These represent the deviations between the instantaneous angles and the equilibrium angles of the two bond angles, respectively. The torsional constant representing the dihedral angle. Indicates the sign factor, Indicates multiplicity, This represents the instantaneous torsional angle value of the dihedral angle. This represents the coordinates of all out-of-plane curvatures in the traversal system. The force constant representing out-of-plane bending, Indicates the depth of the potential well. Indicates the collision diameter. and Represents atoms and atoms Part of the charge, Represents atoms and atoms The instantaneous time interval.

5. The modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 4, characterized in that, The method for calculating van der Waals forces is to use the atomic basis set method to calculate van der Waals particles between particles, in order to describe the van der Waals attraction and repulsion between atoms; The electrostatic force is calculated using the particle grid method to determine the electrostatic force between particles, in order to describe the electrostatic interaction between ions and polar groups in the same skeleton of hydration products.

6. The modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 5, characterized in that, The process involves eliminating residual stress within the initial solid-mix model using a three-level geometric optimization method based on the simulation calculation system, and then performing molecular dynamics equilibrium simulations on the optimized initial solid-mix model using isothermal and isobaric techniques. Based on the simulation results, a stable solid-mix model is output, including: In the Forcite simulation computing technology application interface, select the geometry optimization function, and after the function selection is completed, import the initial solid-mix model, and set the optimization parameters according to the simulation computing system. Based on the imported results and optimization parameters, a three-level geometric optimization method is run to eliminate the energy peaks and atomic overlaps in the initial solid-mix model, and the configuration and atomic charge distribution of the initial solid-mix model are corrected after the elimination is completed. The initial solid-mix model after configuration and atomic charge distribution correction is subjected to atomic charge distribution and local bond angle distortion processing to complete the geometric optimization operation and obtain the geometrically optimized initial solid-mix model; Verify the rationality of the atomic arrangement and bond configuration of the geometrically optimized initial solid-mix model, and evaluate the elimination of residual stress inside the geometrically optimized initial solid-mix model based on the verification results. When the elimination is completely eliminated, output the initial stable solid-mix model with the lowest energy. Based on the Forcite simulation technology, an initial stable solid-mixture model is imported to perform molecular dynamics equilibrium simulation, and a stable solid-mixture model is output according to the energy stability and temperature convergence state of the simulation results.

7. The modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 6, characterized in that, The process of importing an initial stable solid-mixture model based on Forcite simulation technology to perform molecular dynamics equilibrium simulation, and outputting a stable solid-mixture model based on the energy stability and temperature convergence state of the simulation results, includes: In the Forcite simulation technology application interface, switch the geometry optimization function to the molecular dynamics simulation function and import the initial stable solid-mix model; Based on the imported results and simulation parameter settings, a molecular dynamics equilibrium simulation is initiated. Temperature and pressure during the simulation process are adjusted using temperature regulation and pressure control techniques. The energy and temperature change states of the initial stable solid-mix model are determined based on the simulation results. The energy stability and temperature convergence of the initial stable solid-mix model are judged based on the energy and temperature change state. According to the judgment result, the initial stable solid-mix model when the energy stability and temperature convergence tend to be stable and without fluctuation is output as the stable solid-mix model.

8. The modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 7, characterized in that, The process of initiating molecular dynamics equilibrium simulation based on the imported results and simulation parameter settings, and adjusting the temperature and pressure during the simulation process using temperature and pressure control techniques, and determining the energy and temperature change state of the initial stable solid-mixture model based on the simulation results includes: Verify the atomic coordinates, bonding topology, and charge balance state within the initial stable solid-mix model. Inherit the simulation calculation system based on the verification results, and randomly generate the initial motion velocities of all atoms within the initial stable solid-mix model according to the target initial temperature to complete the initial stable solid-mix model initialization process. Based on the initialization results, the atomic positions of the initial stable solid-mix model are updated using the velocity Verlet formula. The nanoscale structural parameters, including the characteristic bond lengths, characteristic bond angles, and interatomic radial distances of the hydration products, are calculated as the spatial coordinate basis for the calculation of interatomic interactions and net external forces. The instantaneous temperature of the initial stable solid-mix model is determined by calculating the atomic motion velocity based on spatial coordinates and temperature control technology. The atomic velocity is then calibrated to control the temperature of the initial stable solid-mix model. Based on pressure control technology, the pressure of the initial stable solid-mix model is stabilized to the target state by scaling the periodic cell volume in real time, so as to keep the total number of atoms in the initial stable solid-mix model unchanged. Combined with temperature control, the temperature and pressure of the initial stable solid-mix model are coordinated and regulated. Based on the thermodynamic constraints of the real curing environment of the hydration products under collaborative control, repeated simulations are performed according to a set number of times. During the iteration process, the temperature and pressure of the initial stable solid-mix model are recorded at fixed intervals to determine the energy and temperature change state of the initial stable solid-mix model.

9. A modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 8, characterized in that, The expression for the velocity Verlet formula is: ； The formula for calculating the initial velocity of the atom is: ； In the formula, Represents the three-dimensional position vector of an atom. Indicates the current simulation time. This indicates the simulation time step size. Represents the three-dimensional velocity vector of an atom. This represents the three-dimensional acceleration vector of an atom.

10. The modeling method for the synergistic effect of multiple hydration products based on molecular dynamics simulation according to claim 1, characterized in that, The process involves extracting target parameter information from the stable solid-mixture model, comparing it with experimental standard data, verifying the deviation state of the stable solid-mixture model based on the comparison results, optimizing the stable solid-mixture model based on the deviation state until the deviation state meets the requirements, and outputting a stable solid-mixture model to describe the synergistic effect of hydration products, including: Based on the stable solid-mix model, nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters of hydration products were extracted and characterized. Standard samples of cementitious materials with the same composition as the hydration products were prepared. The nanoscale structural parameters of the standard samples of cementitious materials were determined by X-ray diffraction and Fourier transform infrared spectroscopy. The atomic coordination and diffusion behavior of the standard samples of cementitious materials were characterized by rotating nuclear magnetic resonance instrument, and the atomic dynamics parameters were output. The mechanical property parameters of the cementitious material standard specimens were determined by in-situ mechanical testing. The nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters corresponding to the stable solid-mix model were quantitatively compared with the nanoscale structural parameters, atomic dynamics parameters and mechanical property parameters of the cementitious material standard specimens. The relative deviation of the stable solid-mix model is evaluated based on the quantitative comparison results. The matching compliance of the stable solid-mix model is assessed, and the construction parameters of the stable solid-mix model are adjusted based on the matching compliance. The stable solid-mix model is iteratively corrected until the matching compliance meets the requirements, and a stable solid-mix model for describing the synergistic effect of hydration products is output.

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