Method for constructing supercritical system polymer coarse graining force field

Abstract

The invention discloses a method for constructing a supercritical system polymer coarse graining force field. The method comprises the following steps: S1, carrying out all-atom benchmark modeling and chemical configuration parameter extraction; s2, performing coarse graining mapping and force field parameter optimization; and S3, iterative optimization is carried out. The method has the technical effects of making up for the loss of a coarse graining force field, improving the simulation efficiency and optimizing force field parameters, not only realizes efficient conversion from a full atom model to a coarse graining model, but also provides a reliable tool for simulation of a supercritical system and other complex polymer systems, and has important scientific value and engineering application potential.

Description

Technical Field

[0001] The present invention belongs to the technical field of the development of molecular simulation coarse-grained force fields, and particularly relates to a method for constructing a coarse-grained force field of polymers in a supercritical system. Background Art

[0002] Instantaneous pressure release spinning (such as flash spinning) is an advanced fiber preparation technology that uses supercritical fluids and organic solvents to trigger phase separation and rapid fiber formation of polymer solutions through rapid pressure release. However, the supercritical dissolution process involves complex multi-component interactions under high temperature and high pressure conditions, and the dissolution behavior and kinetic processes are difficult to directly observe by experimental means, resulting in limited in-depth understanding of molecular chain motion and configurational evolution. In addition, when the spinning solution is in a supercritical state, its physical and chemical properties show high nonlinearity in extreme environments, further increasing the difficulty of experimental research. Although traditional all-atom molecular dynamics simulations can provide details at the atomic level resolution, limited by computing resources, they are only applicable to systems with low molecular weights (not higher than 12 degrees of polymerization) and are difficult to effectively characterize large-scale segment behavior or long-time kinetic evolution. At the same time, there are deficiencies in the existing force fields for supercritical system simulations, and there is an urgent need to develop coarse-grained force fields suitable for such complex conditions to fill the technical gap and promote related research.

[0003] With the continuous development of coarse-grained modeling methods driven by the needs of multi-scale simulations and the reality, significant progress has been made in its theory and application, and two main strategies, "top-down" and "bottom-up", have evolved according to the diversity of research needs. The top-down coarse-grained method starts from macroscopic or mesoscopic properties and reproduces specific macroscopic characteristics of the system, such as thermodynamic behavior or experimentally observed phenomena, by parameterizing interactions. This method usually does not overly emphasize the direct verification of microscopic details, but rather focuses on whether the simulation results can accurately reflect the overall behavior of the system and match experimental data or theoretical predictions. In contrast, the bottom-up coarse-grained method starts from a fine-grained (FG) model and, relying on the principles of statistical mechanics, systematically maps atomic-scale information into a coarse-grained model by accurately reproducing microscopic statistical data to construct a simulation system that is more in line with the physical essence.

[0004] Bottom-up coarse-graining methods usually have higher precision and can better retain information at the microscopic level, while top-down methods focus more on describing the behavior of the overall system and often neglect some microscopic details. In bottom-up coarse-graining modeling, common methods include Iterative Boltzmann Inversion (IBI) and Force Matching (FM). IBI uses the radial distribution function of all-atom simulations as a benchmark and iteratively optimizes the coarse-grained interaction potential so that the coarse-grained model can accurately reproduce the structural characteristics of the all-atom system. This method gradually improves the model accuracy by repeatedly adjusting the potential energy function to match the target radial distribution, and is especially suitable for characterizing the static structure statistical properties of the system. In contrast, FM constructs an effective coarse-grained force field based on the variational principle by minimizing the difference between the calculated interaction forces in the coarse-grained model and the real forces in the fine-grained system. This method often simplifies the three-body interaction potential to a two-body interaction potential to improve the calculation efficiency, but its construction process is relatively complex and the calculation efficiency is low. Therefore, in practical applications, IBI is often regarded as a better choice due to its relative simplicity and high efficiency. Summary of the Invention

[0005] The object of the present invention is to provide a method for constructing a coarse-grained force field of polymers in a supercritical system, which can make up for the lack of coarse-grained force fields, improve the simulation efficiency and optimize the force field parameters.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions: A method for constructing a coarse-grained force field of polymers in a supercritical system, comprising the following steps:

[0007] S1. All-atom reference modeling and extraction of chemical configuration parameters;

[0008] S2. Coarse-grained mapping and optimization of force field parameters;

[0009] S3. Iterative optimization.

[0010] In step S1, a molecular modeling software is used to construct a mixed system of multi-phase molecules containing polymer chains (such as polyethylene, PE), supercritical carbon dioxide (scCO2, temperature ≥ 31.1 °C, pressure ≥ 7.38 MPa), and organic solvents; an all-atom model of the polymer-solvent mixed system is constructed. Step S1 is developed for supercritical fluid systems and achieves efficient configuration relaxation through staged energy optimization and ensemble control. Its core steps and innovative features specifically include: 1) Energy optimization, 2) Dynamic temperature regulation, and 3) NPT equilibration and data generation.

[0011] The energy optimization specifically includes the following processes:

[0012] A. Two-stage convergence: Using the conjugate gradient method (min_style cg), setting the convergence parameters of energy and force to 1e-5, or the total number of iterations to 50000 steps, can achieve energy optimization;

[0013] B. Output control: Output thermodynamic data (energy, pressure, density) every 100 steps and generate an optimized structure file mini.data.

[0014] Dynamic temperature control includes the following processes: ① NVT four-stage control: ② Pre-relaxation: Constant temperature at 300K (stable structure); ③ Gradual heating: 300 → 500K (0.2K / ps), 500 → 1000K (0.5K / ps); ④ High-temperature annealing: Constant temperature at 1000K (extended configuration sampling); ⑤ Quenching and cooling: 1000 → 500K (-0.3K / ps, freezing the amorphous state); ⑥ Trajectory recording: Output configuration trajectories every 1000 steps (relax_NVT_*.lammpstrj);

[0015] NPT equilibration and data generation include the following processes: Ⅰ Pressure coupling: Apply an isotropic pressure of 100 atm at a constant temperature of 500K (fix npt iso), with a damping coefficient of 1000 steps; Ⅱ High-precision output: Record thermodynamic data every 100 steps, generate an equilibrium file balance.data, construct configuration data at each interval step, and construct a multi-frame chemical configuration dataset (data file).

[0016] Step S2 realizes the coarse-grained mapping of the all-atom simulation data file and optimizes the force field parameters of the coarse-grained model through a systematic method. Step S2 first processes the equilibrium data file (data file) generated in Step S1, reads the multi-frame chemical configuration dataset using a Python script, and calculates the standard structural characteristics of the system, including the radial distribution function (RDF), bond length distribution function, bond angle distribution function, and dihedral angle distribution function; these distribution functions respectively characterize the statistical properties of the interatomic distance, bond length, bond angle, and dihedral angle, providing a basis for the development of coarse-graining.

[0017] The specific process of Step S2 is as follows:

[0018] a) Non-bonded interaction optimization: Based on the calculated radial distribution function, fit the initial non-bonded interaction potential energy U0(r); use the Lennard-Jones (LJ) potential function (Formula 1) for fitting. The form of the LJ potential function is:

[0019]

[0020] Through the fitting process, extract the energy parameter ∈ (depth of the potential well) and distance parameter σ (characteristic distance) of the LJ potential to describe the non-bonded interaction between atoms;

[0021] b) Bond interaction: Based on the bond length distribution function, calculate the initial bond potential energy U0(l); use the harmonic potential function (Equation 2) for fitting:

[0022]

[0023] Obtain the bond force constant k and the equilibrium bond length l0 through fitting;

[0024] c) Bond angle interaction optimization: Based on the bond angle distribution function, calculate the initial angle potential energy U0(θ); use the harmonic angle potential function (Equation 3) for fitting:

[0025]

[0026] Extract the angle force constant k θ and the equilibrium angle θ0 through fitting;

[0027] d) Dihedral angle interaction optimization: Based on the dihedral angle distribution function, calculate the initial dihedral angle potential energy U0(φ); use the periodic dihedral angle potential function (Equation 4) for fitting:

[0028] U(φ) = K[1 + d cos(nφ)] Equation 4

[0029] Obtain the dihedral angle energy parameter K, the phase factor d, and the dimensionless period parameter n through fitting.

[0030] The specific process of step S3 is as follows: Compare the above-mentioned initially fitted distribution functions (radial distribution function, bond length distribution function, bond angle distribution function, dihedral angle distribution function) with the corresponding distribution functions of the all-atom simulation; if there are significant deviations, adjust the force field parameters through an iterative method; take the radial distribution function as an example for the specific iterative process: Use the coarse-grained model to generate a new radial distribution function, compare it with the all-atom result, adjust the Lennard-Jones (LJ) potential function parameters ∈ and σ, repeat the simulation and fitting until the coarse-grained distribution function is consistent with the all-atom distribution function; similarly, perform iterative optimization on the bond, angle, and dihedral angle parameters, and finally obtain a set of coarse-grained force field parameters that are highly consistent with the all-atom simulation.

[0031] Adopting the above technical solutions, the present invention proposes a method for developing a coarse-grained force field based on molecular dynamics simulation. Taking the polyethylene-dichloromethane-supercritical carbon dioxide system as an example, through all-atom simulation, coarse-grained mapping, and iterative optimization, a high-precision coarse-grained force field is developed. Its main technical effects are as follows:

[0032] 1. Make up for the lack of coarse-grained force field: This method solves the key problem of the lack of coarse-grained force field in the existing technology, constructs force field parameters suitable for complex systems, enables molecular dynamics simulation to be expanded to a larger time and space scale, and effectively characterizes the motion behavior of polymer chain segments.

[0033] 2. Improve simulation efficiency: Coarse-grained modeling overcomes the limitations of low molecular weight and short molecular chains in all-atom simulations, significantly accelerates the calculation process, and improves the efficiency of molecular dynamics simulations.

[0034] 3. Force field parameter optimization: Using classical molecular dynamics simulation and empirical force field to generate trajectories, combined with Python analysis and iterative fitting, we extract the atomic interaction parameters suitable for coarse-grained simulation to ensure that the structural accuracy is consistent with the full-atom simulation (deviation less than 10 -5 ).

[0035] 4. Application of supercritical system: It lays a theoretical foundation for the study of the dissolution behavior of polymer chain segments under supercritical conditions, especially in the analysis of the motion state of molecular chains under different component ratios, showing significant advantages and broad application prospects.

[0036] In summary, the present invention not only realizes the efficient conversion from the all-atom to the coarse-grained model through the above-mentioned technical effects, but also provides a reliable tool for the simulation of supercritical systems and other complex polymer systems, which has important scientific value and engineering application potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of radial distribution function process iteration;

[0038] Figure 2 It is a diagram of the construction of the all-atom model;

[0039] Figure 3 It is the coarse-grained model construction diagram;

[0040] Figure 4 This is a comparison of the RDF distribution of the coarse-grained and all-atom models after iteration;

[0041] Figure 5 This is a comparison of bond distributions between the iterative coarse-grained and all-atom models;

[0042] Figure 6 It is the comparison diagram of the angular distribution of the coarse-grained and all-atom models obtained after iteration;

[0043] Figure 7 It is a comparison diagram of dihedral angle distribution between coarse-grained and full-atom models obtained after iteration;

[0044] Figure 8 Time comparison chart of different simulation methods. DETAILED DESCRIPTION

[0045] The preferred embodiments of the present invention will be described in detail below in conjunction with the embodiments. It should be understood that the following embodiments are given only for the purpose of illustration and are not used to limit the scope of the present invention. Those skilled in the art can make various modifications and substitutions to the present invention without departing from the purpose and spirit of the present invention.

[0046] As Figure 1 shown, in this embodiment, a polyethylene-dichloromethane-supercritical carbon dioxide system is taken as an example to construct and optimize its coarse-grained force field. The specific steps are as follows:

[0047] S1. Construction of all-atom model ( Figure 2 shown)

[0048] Through molecular dynamics modeling software, an all-atom physical model of the polyethylene-dichloromethane-supercritical carbon dioxide system is constructed, and initial force field parameters are specified for the interactions between atoms. Preferably, the same force field can be used for all atoms, or different force fields (mixed force fields) can be assigned according to the atom type.

[0049] S2. Preparation for LAMMPS simulation

[0050] The LAMMPS software package is used for molecular dynamics simulation. The simulation input includes two files: the all-atom model file (data file) and the control parameter file (in file).

[0051] S3. Simulation under supercritical conditions and trajectory sampling

[0052] Use LAMMPS to read the model file generated in S1 and perform simulations under supercritical carbon dioxide conditions (temperature higher than 31.1 °C, pressure higher than 7 MPa). After the simulation is completed, extract the equilibrium state trajectory file, sample the trajectory through a Python script, and perform coarse-grained mapping on polymer molecules, dichloromethane molecules, and carbon dioxide molecules respectively. To accurately describe the behavior of the solvent and polymer in the supercritical system, it is preferred to coarse-grain each solvent molecule into a single bead to generate an initial coarse-grained model.

[0053] S4. Calculation of distribution function

[0054] Use a Python script to analyze the configuration of the coarse-grained model generated in step S3, and calculate its radial distribution function (RDF), bond length distribution function, bond angle distribution function, and dihedral angle distribution function to characterize the statistical properties of the coarse-grained system.

[0055] S5. Potential energy conversion and initial parameter fitting

[0056] Convert the distribution function obtained in step S4 into its corresponding potential energy form, including non-bonded interaction potential energy U0(r), bond length potential energy U0(l), bond angle potential energy U0(θ), and dihedral angle potential energy U0(φ). Fit the initial force field parameters using appropriate potential functions (such as Lennard-Jones potential, harmonic potential, etc.).

[0057] S6. Coarse-grained iterative optimization ( Figure 3 as shown)

[0058] Use the coarse-grained model from step S5 as the input for the new round of simulation. Generate a new trajectory file through LAMMPS, and then calculate the updated distribution function using a Python script. Compare this distribution function with the distribution function of the all-atom simulation. If the deviation between the two is greater than 10 -5 , then adjust the force field parameters and repeat the simulation and fitting processes from S3 to S5 until convergence. Preferably, in the fitting process, the potential function formulas given in this specification can be used, or other forms can be selected according to needs.

[0059] S7. Output of force field parameters

[0060] After multiple iterations of optimization, when the deviation between the distribution functions (including RDF, bond parameters, angle parameters, dihedral angle parameters) of the all-atom and coarse-grained models is less than 10 -5 , output the parameters obtained in the 7th round of the final force field. The following are the parameter examples obtained in this embodiment:

[0061] Non-bonded interaction parameters (pair_coeff type1 type2 epsilon sigma)

[0062] pair_coeff 1 1 0.4399 5.2680

[0063] pair_coeff 1 2 0.3001 4.5117

[0064] pair_coeff 1 3 0.3405 5.2423

[0065] pair_coeff 2 3 0.4195 5.0000

[0066] pair_coeff 3 3 0.2601 5.0000

[0067] # Bond parameters (bond_coeff type k l_0)

[0068] bond_coeff 1 4.5302 4.9000

[0069] # Angle parameter (angle_coeff type k_theta theta_0)

[0070] angle_coeff 1 1.9233 165.0000

[0071] # Dihedral angle parameter (dihedral_coeff type K d n)

[0072] dihedral_coeff 1 0.4567 -1 1

[0073] S8. Coarse-grained force field verification and effect comparison

[0074] Using the force field parameters output in step S7, a new round of molecular dynamics simulation is performed on the coarse-grained model in LAMMPS to generate an equilibrium trajectory file. Analyze the trajectory through a Python script, calculate the radial distribution function (RDF), bond length distribution function, bond angle distribution function, and dihedral angle distribution function of the coarse-grained model, and compare them with the corresponding distribution functions of the all-atom simulation. The verification results are as Figures 4 to 7 shown: The radial distribution function ( Figure 4 ), bond length distribution function ( Figure 5 ), bond angle distribution function ( Figure 6 ), and dihedral angle distribution function ( Figure 7 ) after iterative optimization are highly consistent with the all-atom simulation results, with a deviation less than 10 -5 , indicating that the coarse-grained force field can effectively reproduce the structural accuracy of the all-atom model. In addition, through the comparison of different simulation methods ( Figure 8 ), the coarse-grained model significantly improves the calculation efficiency while maintaining the accuracy, verifying the superiority of this method.

[0075] The above embodiments illustrate the basic principles and characteristics of the present invention. However, the above only illustrates the preferred embodiments of the present invention and is not limited by the described embodiments. Those of ordinary skill in the art, inspired by this patent, can also make many forms of deformation and improvement without departing from the spirit and scope protected by the claims of the present invention. These all fall within the protection scope of the present invention. Therefore, the patent and protection scope of the present invention should be subject to the appended claims.

Claims

1. A method for constructing a polymer coarse-grained force field in a supercritical system, characterized in that: It includes the following steps: S1. All-atom reference modeling and extraction of chemical configuration parameters; S2. Coarse-grained mapping and optimization of force field parameters; S3. Iterative optimization.

2. The method for constructing a polymer coarse-grained force field in a supercritical system according to claim 1, wherein: In step S1, a molecular modeling software is used to construct a multiphase molecular system containing polymer chains (such as polyethylene PE), supercritical carbon dioxide (scCO2, temperature ≥ 31.1 °C, pressure ≥ 7.38 MPa), and organic solvents; an all-atom model of the polymer-solvent mixture system is constructed. Step S1 is developed for supercritical fluid systems and achieves efficient configuration relaxation through staged energy optimization and ensemble control. Its core steps and innovative features specifically include: 1) energy optimization, 2) dynamic temperature regulation, and 3) NPT equilibration and data generation.

3. The method for constructing a polymer coarse-grained force field in a supercritical system according to claim 2, wherein: The energy optimization specifically includes the following processes: A. Two-stage convergence: Using the conjugate gradient method (min_style cg), setting the convergence of energy and force to 1e-5, or the total number of iteration steps to 50000 steps, can achieve energy optimization; B. Output control: Output thermodynamic data (energy, pressure, density) every 100 steps, and generate an optimized structure file mini.data.

4. The method for constructing the supercritical system polymer coarse-grained force field according to claim 2, characterized in that: The dynamic temperature regulation includes the following processes: ① NVT four-stage control: ② Pre-relaxation: Constant temperature at 300 K (stable structure); ③ Gradual temperature increase: 300 → 500 K (0.2 K / ps), 500 → 1000 K (0.5 K / ps); ④ High-temperature annealing: Constant temperature at 1000 K (extended configuration sampling); ⑤ Quenching and cooling: 1000 → 500 K (-0.3 K / ps, freezing the amorphous state); ⑥ Trajectory recording: Output the configuration trajectory every 1000 steps (relax_NVT_*.lammpstrj).

5. The method for constructing a polymer coarse-grained force field in a supercritical system according to claim 2, wherein: The NPT equilibration and data generation include the following processes: Ⅰ Pressure coupling: Apply an isotropic pressure of 100 atm at a constant temperature of 500 K (fix npt iso), with a damping coefficient of 1000 steps; Ⅱ High-precision output: Record thermodynamic data every 100 steps, generate an equilibrium file balance.data, construct configuration data at each interval step size, and construct a multi-frame chemical configuration dataset (data file).

6. The method for constructing a polymer coarse-grained force field in a supercritical system according to claim 1, wherein: In step S2, the coarse-grained mapping of the all-atom simulation data file is realized, and the force field parameters of the coarse-grained model are optimized by a systematic method. Step S2 first processes the equilibrium data file (data file) generated in step S1, reads the multi-frame chemical configuration dataset using a Python script, and calculates the standard structural characteristics of the system, including the radial distribution function (RDF), bond length distribution function, bond angle distribution function, and dihedral angle distribution function; these distribution functions respectively characterize the statistical properties of the interatomic distance, bond length, bond angle, and dihedral angle, providing a basis for the development of coarse-graining.

7. The method for constructing the supercritical system polymer coarse-grained force field according to claim 6, characterized in that: The specific process of step S2 is as follows: a) Non-bonded interaction optimization: Based on the calculated radial distribution function, fit the initial non-bonded interaction potential energy U0(r); Use the Lennard-Jones (LJ) potential function (formula 1) for fitting. The form of the LJ potential function is: Through the fitting process, the energy parameter ∈ (depth of the potential well) and the distance parameter σ (characteristic distance) of the LJ potential are extracted to describe the non-bonded interactions between atoms; b) Bond interactions: Based on the bond length distribution function, the initial bond potential energy U0(l) is calculated; The harmonic potential function (Equation 2) is used for fitting: The bond force constant k and the equilibrium bond length l0 are obtained through fitting; c) Bond angle interaction optimization: Based on the bond angle distribution function, the initial angle potential energy U0(θ) is calculated; The harmonic angle potential function (Equation 3) is used for fitting: Extract the angular force constant k by fitting θ and the equilibrium angle θ0; d) Dihedral angle interaction optimization: Based on the dihedral angle distribution function, the initial dihedral angle potential energy U0(φ) is calculated; The periodic dihedral angle potential function (Equation 4) is used for fitting: U(φ) = K[1 + d cos(nφ)] Equation 4 The dihedral angle energy parameter K, the phase factor d, and the dimensionless period parameter n are obtained through fitting.

8. The method for constructing a polymer coarse-grained force field in a supercritical system according to claim 3, wherein: The specific process of step S3 is as follows: The above-mentioned distribution functions obtained from the preliminary fitting (radial distribution function, bond length distribution function, bond angle distribution function, dihedral angle distribution function) are compared with the corresponding distribution functions of the all-atom simulation; If there are significant deviations, the force field parameters are adjusted by an iterative method; The specific iterative process takes the radial distribution function as an example: A new radial distribution function is generated using the coarse-grained model, compared with the all-atom results, and the parameters ∈ and σ of the Lennard-Jones (LJ) potential function are adjusted. The simulation and fitting are repeated until the coarse-grained distribution function is consistent with the all-atom distribution function; Similarly, the bond, angle, and dihedral angle parameters are iteratively optimized, and finally a set of coarse-grained force field parameters highly consistent with the all-atom simulation are obtained.

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