High-flux molecular modeling method for asphalt binder

Abstract

The invention discloses a high-flux molecular modeling method of an asphalt binder. The method comprises the following steps: creating an asphalt binder component molecular library; according to given modeling requirements, component molecules of the asphalt binder component molecule library are randomly extracted, and an asphalt binder model is generated through modeling; on the basis of topological information of an asphalt binder model, according to a molecular simulation CVFF empirical force field, determining bonding categories of component atoms of the asphalt binder so as to determine atomic simulation force field parameters; and finally, outputting a data file of the asphalt binder model according to a set format requirement. According to the method, the initial model construction of the asphalt binder model and the automatic generation of the atomic scale simulation file are realized, the workload of manual modeling is reduced, the modeling requirement can be automatically processed, the asphalt binder molecular model can be automatically generated, and the model creation speed is improved.

Description

Technical Field

[0001] The present invention relates to the field of molecular modeling and atomic scale simulation, and in particular to a high-throughput molecular modeling method for asphalt binder. Background Art

[0002] The road performance of asphalt binders is directly related to their physical and chemical properties, which are all affected by their microstructure and components. In order to develop a more widely applicable and accurate molecular model of asphalt binders, which can help study the physical and chemical properties of asphalt and develop high-performance asphalt binders, further research is needed on the molecular characterization and model construction of asphalt. Asphalt is a heterogeneous and complex organic material, containing hydrocarbons such as alkanes, cycloalkanes and aromatic hydrocarbons, as well as heterocyclic non-hydrocarbon compounds containing heteroatoms such as sulfur, nitrogen and oxygen. In addition, the molecular types of asphalt components have reached 10 6 At present, there are only about 100 molecules that have appeared or are used to establish asphalt models, and there are less than 10 types of molecules in a model. In simulating the real properties of asphalt, some models have great limitations. It is difficult to adjust some components to adapt to the real components of asphalt, and it is difficult to take into account the various physical and chemical properties of asphalt in the simulation. This also hinders us from using simulation methods to help the development and property research of asphalt binders. Therefore, high-throughput establishment of asphalt component models and development of asphalt models that are more in line with asphalt experimental data provide an important foundation and support for the subsequent calculation of the impact of asphalt components on physical and chemical properties and performance prediction.

[0003] Atomic-scale simulation can be used to study the physical properties of asphalt binders, and is also widely used in studies on asphalt modification and aging. However, for the complex molecular system of asphalt binders, the model system currently studied is still very small, and the modeling method is cumbersome and time-consuming. Therefore, it is particularly important to establish an automated high-throughput asphalt binder molecular model to help subsequent component property research. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a high-throughput molecular modeling method for asphalt binder, and the specific technical scheme is as follows:

[0005] A high-throughput molecular modeling method for asphalt binders comprises the following steps:

[0006] S1: Based on the existing asphalt binder molecular model literature, the molecular chemical formulas or model files of asphalt binder components are collected. Considering the four components of asphalt binder, four asphalt binder component molecular libraries are established, including asphaltene molecular library, cycloalkane molecular library, polar aromatic hydrocarbon molecular library and saturated hydrocarbon molecular library. At the same time, the molecular average molar mass of the asphalt binder component in each molecular library is calculated;

[0007] S2: calling the four asphalt binder component molecule libraries created in step S1, randomly calling the asphalt binder component molecules according to the size of the asphalt binder model, the total number of component molecules, the ratio of the number of component molecules and the number of modeling times, and creating a random asphalt binder model;

[0008] S3: Based on the asphalt binder model established in step S2, read the molecular information of the model, including the atomic element name, charge, coordinates, and chemical bond chain information, and judge the atomic bonding types in different molecules according to the chemical environment of different atoms; read the atomic bonding category information and chemical bond force field parameter information of the molecular simulation CVFF empirical force field, match the atomic bonding categories in different molecules of the asphalt binder model established in step S2, and thus obtain the corresponding molecular force field parameters;

[0009] S4: Based on the molecular force field parameters obtained in step S3 and according to user settings, a model of the asphalt binder is automatically generated and a model file is output.

[0010] Furthermore, step S1 specifically includes the following sub-steps:

[0011] S11: Based on the existing literature on asphalt binder molecular models, collect the molecular chemical formulas or model files of asphalt binder components. If there are only chemical formulas, draw the corresponding molecular models using Material Studio or Avogadro software. The topological information of each molecule is finally stored in pdb file format.

[0012] S12: according to the component molecules of the asphalt binder, an asphaltene molecule library, a cycloalkane molecule library, a polar aromatic hydrocarbon molecule library and a saturated hydrocarbon molecule library are established, and each component molecule is named according to the molecular component category and molecular formula to form an asphalt binder component molecule library and a retrieval index of the molecule;

[0013] S13: Count all the molecular chemical formulas entered into the four component molecular libraries, calculate the average molar mass of the corresponding four component molecules, and store them in the corresponding molecular libraries.

[0014] Furthermore, step S2 specifically includes the following sub-steps:

[0015] S21: setting the size, total number of component molecules, ratio of component molecules and number of modeling times of the asphalt binder model, and calculating the theoretical volume of the asphalt binder model according to the average molar mass of the four-component molecular model in S12; comparing the set volume and theoretical volume of the asphalt binder model, if the set volume is less than 20% of the theoretical volume, returning to correct the size of the set asphalt binder model, otherwise, randomly retrieving the corresponding component molecules from the asphalt binder component molecule library created in S12 according to the set conditions;

[0016] S22: Generate an input file for the open source software Packmol based on the set parameters of the asphalt binder model in S21 and the retrieved component molecules, so as to call Packmol to create a random asphalt binder model and output a pdb topology file of the asphalt binder model.

[0017] Furthermore, step S3 specifically includes the following sub-steps:

[0018] S31: read the pdb topology file of the asphalt binder model to obtain the molecular information of the model, including the bond chain information of the chemical bond, the element name, charge and coordinate of the atom;

[0019] S32: according to the molecular chemical formula of the asphalt binder component, establishing a chemical bonding type judgment function of the atoms of the asphalt binder component, including an aromaticity judgment function for the atoms of the asphalt binder component and a judgment function for the bonding types of all atoms in the atoms of the asphalt binder component;

[0020] S33: Read the molecular simulation CVFF empirical force field, extract the atomic bonding category in the empirical force field, and store it in the atomic bonding category retrieval array; extract the bond length, bond angle, torsion angle, and out-of-plane interaction parameters of the chemical bond, and store them in the corresponding force field parameter retrieval array; extract the long-range van der Waals and electrostatic field interaction parameters, and store them in the corresponding force field parameter retrieval array;

[0021] S34: judging the bonding type of atoms in each component molecule of the asphalt binder model according to the chemical bonding type judgment function in S32 and the atomic bonding category retrieval array of the empirical force field in S33, and storing the result in the atomic bonding category array;

[0022] S35: According to the atomic bonding category array of the asphalt binder component, the chemical bonding parameters of the asphalt binder component molecules are obtained through the force field parameter retrieval array of S33; for the chemical bonds not in the force field parameter retrieval array, according to the bonding information of the chemical bonds, an equivalent replacement atomic bonding category is selected and updated to the atomic bonding category array, and finally all the chemical bonding parameters of the asphalt binder component molecules are obtained and stored in the component molecule force field parameter array;

[0023] S36: looping steps S32-S35, obtaining the molecular force field parameters of all components in the asphalt binder model, and storing them in the molecular force field parameter array of the corresponding components.

[0024] Furthermore, step S4 specifically includes the following sub-steps:

[0025] S41: according to the component molecules of the asphalt binder model and the force field parameters of the component molecules established in S36, a model file of the asphalt binder is output according to user settings, including topological information of the asphalt binder model and force field parameter information of the component molecules;

[0026] S42: According to the model requirements input by the user, the steps S2-S41 are cycled to automatically generate all the required asphalt binder models to achieve automated high-throughput modeling.

[0027] Furthermore, the bond chain information of the chemical bond includes atomic neighbor information of each chemical bond, including pairwise atomic information corresponding to the bond length, three atomic information corresponding to the bond angle, and four atomic information corresponding to the torsion angle and out-of-plane interaction.

[0028] Furthermore, the aromaticity judgment function of atoms takes into account the chemical bonding environment of six-ring atoms and five-ring atoms.

[0029] The beneficial effects of the present invention are as follows:

[0030] 1) The present invention realizes the initial model construction of the asphalt binder model and the automatic generation of atomic scale simulation files through the identification of atomic bonding categories and automatic matching of empirical force field parameters, thereby reducing the workload of manual modeling.

[0031] 2) By establishing a component molecular database, the present invention can automatically process modeling requirements and generate asphalt binder molecular models, thereby improving the speed of model creation and providing strong support for subsequent asphalt binder high-throughput simulation calculations and asphalt informatics fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The flowchart of the high-throughput molecular modeling method of asphalt binder of the present invention.

[0033] Figure 2 These are two random asphalt binder models created in the embodiments of the present invention.

[0034] Figure 3 This is a relationship diagram of the influence of the total number of asphalt binder modeling molecules and model size on the modeling time in the present invention. DETAILED DESCRIPTION

[0035] The present invention will be described in detail below based on the accompanying drawings and preferred embodiments, and the purpose and effects of the present invention will become more clear. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] The present invention mainly includes four modules: creating a molecular library of asphalt binder components, creating an asphalt binder model, identifying and matching the asphalt binder molecular force field, and high-throughput modeling. Figure 1As shown. The asphalt binder component molecular library in the present invention can be drawn and generated by Material Studio or Avogadro software. First, the modeling requirements are given, including the asphalt binder model size, the total number of component molecules, the ratio of the number of component molecules and the number of modeling times. The component molecules of the asphalt binder component molecular library are randomly extracted, and the open source software Packmol is called to carry out modeling and generate the asphalt binder model; then the asphalt binder model topology information is read, and the bonding category of the asphalt binder component atoms is determined according to the molecular simulation CVFF empirical force field, thereby determining the atomic simulation force field parameters; finally, all the topological information of the asphalt binder model is integrated, and the data file of the asphalt binder model is output according to the set format requirements. The molecular files in the database used in this embodiment are all drawn and generated in Material Studio, and the relevant programs are compiled and run under Python.

[0037] like Figure 1 As shown, the high-throughput molecular modeling method of asphalt binder provided in this embodiment includes the following steps:

[0038] S1: Create a molecular library of asphalt binder components

[0039] S11: Based on the relevant literature on the existing asphalt binder molecular model, collect the molecular formula or model file of the asphalt binder component. This example takes the 12-molecule asphalt binder model of the literature [Chemical compositions of improved model asphalt systems for molecular simulations, DDLi, MLGreenfield, Fuel 2014, 115, 347] as an example, including three asphaltene molecules (C 42 H 54 O.C 66 H 81 N, C 51 H 64 S), two cycloalkane molecules (C 35 H 44 , C 30 H 46 ), 5 polar aromatic molecules (C 40 H 59 N, C 36 H 57 N, C 18 H 10 S2, C 29 H 50 O.C 40 H 64 S) and two saturated hydrocarbon molecules (C 30 H62 , C 35 H 62 ), the corresponding molecular model is drawn in Avogadro software based on its chemical formula, and the topological information of each molecule is finally stored in pdb file format.

[0040] S12: A database is established based on the above component molecules of asphalt binders, and four sub-molecule libraries are created considering the four components of asphalt, including asphaltene molecule library, cycloalkane molecule library, polar aromatic molecule library and saturated hydrocarbon molecule library. Each component molecule is named according to the molecular component category and molecular formula to form an asphalt binder component molecule library and a retrieval index of molecules.

[0041] S13: Count all the input molecular chemical formulas of the four sub-molecule libraries, calculate the average molar mass of the corresponding four component molecules, and store them in the corresponding molecule library.

[0042] In this embodiment, S12-S13 can be implemented by C++ or python programming.

[0043] S2: Creating a random asphalt binder model

[0044] S21: The molecular ratio of asphaltene / cycloalkanes / polar aromatics / saturated hydrocarbons is set to change linearly from 0.5 / 0.2 / 0.2 / 0.1 to 0.4 / 0.3 / 0.1 / 0.2, the total number of molecules is 24, and the asphalt binder model size is For a periodic cube, the number of random modeling is 2 times (i.e., 2 asphalt binder models that meet the conditions are generated, with molecular ratios of 0.5 / 0.2 / 0.2 / 0.1 and 0.4 / 0.3 / 0.1 / 0.2 respectively). The theoretical volume of the asphalt binder model is calculated based on these settings, and it is determined whether the input volume is greater than 20% of the theoretical volume. If the determination condition is met, 12 asphaltene molecules, 5 cycloalkane molecules, 5 polar aromatic molecules and 2 saturated hydrocarbon molecules as well as 10 asphaltene molecules, 7 cycloalkane molecules, 2 polar aromatic hydrocarbon molecules and 5 saturated hydrocarbon molecules are randomly retrieved from the asphalt binder component molecule library created in S11.

[0045] S22: Generate an input file for the open source software Packmol based on the set parameters of the asphalt binder model in S21 and the retrieved component molecular model, thereby calling Packmol to create two asphalt binder models and output the pdb topology file of the asphalt binder model. Figure 2 The 2 random asphalt binder models generated are shown.

[0046] In this embodiment, S2 can be implemented by C++ or python programming.

[0047] S3: Identification and matching of asphalt binder molecular force fields

[0048] S31: Read the pdb topology file of the asphalt binder model to obtain the molecular information of the model, including the bond chain information of chemical bonds, the element name, charge, and coordinates of atoms. The bond chain information of chemical bonds is the atomic neighbor information of each chemical bond, including the two-atom information corresponding to the bond length, the three-atom information corresponding to the bond angle, the torsion angle, and the four-atom information corresponding to the out-of-plane interaction. These molecular information are stored by defining different arrays.

[0049] S32: According to the molecular chemical formula of the asphalt binder component, a judgment function of the chemical bonding type of the asphalt binder component atoms is established, including a judgment function of the aromaticity of the asphalt binder component atoms and a judgment function of the bonding type of all atoms in the asphalt binder component atoms, including C, H, O, etc. The judgment function of the aromaticity of the atoms mainly needs to consider the chemical bonding environment of the six-ring atoms and the five-ring atoms, and the judgment of the chemical bonding type of the atoms depends on the molecular information extracted by S31.

[0050] S33: Read the molecular simulation CVFF empirical force field, extract the atomic bonding category in the empirical force field, and store it in the atomic bonding category retrieval array; extract the bond length, bond angle, torsion angle, and out-of-plane interaction parameters of the chemical bond, and store them in the corresponding force field parameter retrieval array; extract the long-range van der Waals and electrostatic field interaction parameters, and store them in the corresponding force field parameter retrieval array.

[0051] S34: Based on the point chemical bonding type judgment function of S32 and the empirical force field atomic bonding category retrieval array of S33, the bonding type of atoms in each component molecule of the asphalt binder model is judged, and the results are stored in the atomic bonding category array.

[0052] S35: According to the atomic bonding category array of the asphalt binder component, combined with the chemical bond chain information of S31 and the force field parameter retrieval array of S33, the chemical bond parameters of the asphalt binder component molecules are determined. For chemical bonds that are not in the force field parameter retrieval array, the equivalent replacement atomic bonding category is selected according to the bonding information of the chemical bond, and updated to the atomic bonding category array. Finally, all the chemical bond parameters of the asphalt binder component molecules are obtained and stored in the component molecule force field parameter array.

[0053] S36: looping steps S32-S35, obtaining the molecular force field parameters of all components in the asphalt binder model, and storing them in the molecular force field parameter array of the corresponding components.

[0054] In this embodiment, S3 can be implemented by C++ or python programming.

[0055] S4: High-throughput modeling

[0056] S41: Based on the component molecules of the asphalt binder model and the force field parameters of the component molecules established in S36, the model file of the asphalt binder is output according to the initial settings, including the topological information of the asphalt binder model and the force field parameter information of the component molecules.

[0057] S42: According to the model requirements initially input, steps S2-S41 are cycled to automatically generate the two required asphalt binder models to achieve automated high-throughput modeling.

[0058] like Figure 3 As shown, the automated modeling time is directly related to the asphalt binder model size based on the molecular ratio and total number of asphalt binder components input by the user.

[0059] In this embodiment, S4 can be implemented by C++ or python programming.

[0060] It should be understood that the force field used in the high-throughput molecular modeling method of asphalt binder involved in the present invention is not limited to the CVFF empirical force field, and the molecular library of asphalt binder components can also be continuously expanded during use.

[0061] Those skilled in the art can understand that the above are only preferred examples of the invention and are not intended to limit the invention. Although the invention is described in detail with reference to the above examples, those skilled in the art can still modify the technical solutions recorded in the above examples or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, etc. made within the spirit and principle of the invention shall be included in the protection scope of the invention.

Claims

1. A high-throughput molecular modeling method for asphalt binders, characterized in that: The steps include: S1: Based on the existing asphalt binder molecular model literature, the molecular chemical formulas or model files of asphalt binder components are collected. Considering the four components of asphalt binder, four asphalt binder component molecular libraries are established, including asphaltene molecular library, cycloalkane molecular library, polar aromatic hydrocarbon molecular library and saturated hydrocarbon molecular library. At the same time, the molecular average molar mass of the asphalt binder component in each molecular library is calculated; S2: calling the four asphalt binder component molecule libraries created in step S1, randomly calling the asphalt binder component molecules according to the size of the asphalt binder model, the total number of component molecules, the ratio of the number of component molecules and the number of modeling times, and creating a random asphalt binder model; S3: Based on the asphalt binder model established in step S2, read the molecular information of the model, including the atomic element name, charge, coordinates, and chemical bond chain information, and judge the atomic bonding types in different molecules according to the chemical environment of different atoms; read the atomic bonding category information and chemical bond force field parameter information of the molecular simulation CVFF empirical force field, match the atomic bonding categories in different molecules of the asphalt binder model established in step S2, and thus obtain the corresponding molecular force field parameters; S4: Based on the molecular force field parameters obtained in step S3 and according to user settings, a model of the asphalt binder is automatically generated and a model file is output.

2. The high-throughput molecular modeling method for asphalt binders according to claim 1, characterized in that: Step S1 specifically includes the following sub-steps: S11: Based on the existing literature on asphalt binder molecular models, collect the molecular chemical formulas or model files of asphalt binder components. If there are only chemical formulas, draw the corresponding molecular models using Material Studio or Avogadro software. The topological information of each molecule is finally stored in pdb file format. S12: according to the component molecules of the asphalt binder, an asphaltene molecule library, a cycloalkane molecule library, a polar aromatic hydrocarbon molecule library and a saturated hydrocarbon molecule library are established, and each component molecule is named according to the molecular component category and molecular formula to form an asphalt binder component molecule library and a retrieval index of the molecule; S13: Count all the molecular chemical formulas entered into the four component molecular libraries, calculate the average molar mass of the corresponding four component molecules, and store them in the corresponding molecular libraries.

3. The high-throughput molecular modeling method for asphalt binders according to claim 2, characterized in that: Step S2 specifically includes the following sub-steps: S21: setting the size, total number of component molecules, ratio of component molecules and number of modeling times of the asphalt binder model, and calculating the theoretical volume of the asphalt binder model according to the average molar mass of the four-component molecular model in S12; Compare the set volume of the asphalt binder model with the theoretical volume. If the set volume is less than 20% of the theoretical volume, return to correct the size of the set asphalt binder model. Otherwise, randomly call the corresponding component molecules from the asphalt binder component molecule library created in S12 according to the set conditions. S22: Generate an input file for the open source software Packmol based on the set parameters of the asphalt binder model in S21 and the retrieved component molecules, so as to call Packmol to create a random asphalt binder model and output a pdb topology file of the asphalt binder model.

4. The high-throughput molecular modeling method for asphalt binders according to claim 1, characterized in that: Step S3 specifically includes the following sub-steps: S31: read the pdb topology file of the asphalt binder model to obtain the molecular information of the model, including the bond chain information of the chemical bond, the element name, charge and coordinate of the atom; S32: according to the molecular chemical formula of the asphalt binder component, establishing a chemical bonding type judgment function of the atoms of the asphalt binder component, including an aromaticity judgment function for the atoms of the asphalt binder component and a judgment function for the bonding types of all atoms in the atoms of the asphalt binder component; S33: Read the molecular simulation CVFF empirical force field, extract the atomic bonding category in the empirical force field, and store it in the atomic bonding category retrieval array; extract the bond length, bond angle, torsion angle, and out-of-plane interaction parameters of the chemical bond, and store them in the corresponding force field parameter retrieval array; extract the long-range van der Waals and electrostatic field interaction parameters, and store them in the corresponding force field parameter retrieval array; S34: judging the bonding type of atoms in each component molecule of the asphalt binder model according to the chemical bonding type judgment function in S32 and the atomic bonding category retrieval array of the empirical force field in S33, and storing the result in the atomic bonding category array; S35: According to the atomic bonding category array of the asphalt binder component, the chemical bonding parameters of the asphalt binder component molecules are obtained through the force field parameter retrieval array of S33; for the chemical bonds not in the force field parameter retrieval array, according to the bonding information of the chemical bonds, an equivalent replacement atomic bonding category is selected and updated to the atomic bonding category array, and finally all the chemical bonding parameters of the asphalt binder component molecules are obtained and stored in the component molecule force field parameter array; S36: looping steps S32-S35, obtaining the molecular force field parameters of all components in the asphalt binder model, and storing them in the molecular force field parameter array of the corresponding components.

5. The high-throughput molecular modeling method of asphalt binder according to claim 4, characterized in that: Step S4 specifically includes the following sub-steps: S41: according to the component molecules of the asphalt binder model and the force field parameters of the component molecules established in S36, a model file of the asphalt binder is output according to user settings, including topological information of the asphalt binder model and force field parameter information of the component molecules; S42: According to the model requirements input by the user, steps S2-S41 are cycled to automatically generate all required asphalt binder models to achieve automated high-throughput modeling.

6. The high-throughput molecular modeling method for asphalt binders according to claim 4, characterized in that: The bond chain information of the chemical bond includes atomic neighbor information of each chemical bond, including pairwise atomic information corresponding to the bond length, three atomic information corresponding to the bond angle, and four atomic information corresponding to the torsion angle and out-of-plane interaction.

7. The high-throughput molecular modeling method for asphalt binders according to claim 4, characterized in that: The aromaticity judgment function of an atom takes into account the chemical bonding environment of six-ring atoms and five-ring atoms.

Images