A high-throughput molecular modeling method for asphalt binders

Abstract

The present invention discloses a high-throughput molecular modeling method for asphalt binders, comprising: creating an asphalt binder component molecular library; generating an asphalt binder model by randomly extracting component molecules from the asphalt binder component molecular library according to given modeling requirements; determining the bonding categories of the asphalt binder component atoms based on the molecular simulation CVFF empirical force field based on the asphalt binder model's topological information, thereby determining the atomic simulation force field parameters; and finally, outputting a data file of the asphalt binder model in accordance with a specified format. The present invention achieves automated construction of the initial asphalt binder model and generation of atomic-scale simulation files, reducing the workload of manual modeling. It also automates the processing of modeling requirements and the generation of asphalt binder molecular models, thereby increasing the speed of model creation.

Description

Technical Field

[0001] The present invention relates to the fields of molecular modeling and atomic scale simulation, and in particular to a high-throughput molecular modeling method for asphalt binders. 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, thereby helping to 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 However, there are only about 100 molecules currently available or used to build asphalt models, and a single model can contain fewer than 10 molecular species. Some models have significant limitations in simulating the true properties of asphalt, making it difficult to adjust certain components to accommodate the actual composition of asphalt, and to account for the diverse physical and chemical properties of asphalt in simulations. This also hinders the use of simulations to aid in the development and property research of asphalt binders. Therefore, high-throughput asphalt component modeling and the development of asphalt models that better align with experimental data provide a critical foundation and support for subsequent calculations of the impact of asphalt components on physical and chemical properties, as well as for performance prediction.

[0003] Atomic-scale simulations can be used to study the physical properties of asphalt binders and are widely used in research on asphalt modification and aging. However, the model systems currently used for studying the complex molecular systems of asphalt binders are still small, and the modeling methods are cumbersome and time-consuming. Therefore, it is particularly important to establish automated, high-throughput molecular models of asphalt binders to facilitate subsequent component property studies. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a high-throughput molecular modeling method for asphalt binders. The specific technical solution is as follows:

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

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

[0007] S2: Call the four asphalt binder component molecule libraries created in step S1, randomly select 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 required, and create 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 determine the atomic bonding type 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 category 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 existing literature on asphalt binder molecular models, collect the chemical formulas or model files of asphalt binder component molecules. If only the chemical formula is available, draw the corresponding molecular model using Material Studio or Avogadro software. Finally, store the topological information of each molecule in the pdb file format.

[0012] S12: Based on 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. 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 based on the average molar mass of the four-component molecular model in S12; comparing the set volume and the 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: Based on the set parameters of the asphalt binder model in S21 and the retrieved component molecules, an input file for the open source software Packmol is generated, thereby calling Packmol to create a random asphalt binder model and outputting the 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 coordinates of the atom;

[0019] S32: establishing a chemical bonding type judgment function for the atoms of the asphalt binder component according to the molecular chemical formula 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 type 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 search 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 search array; extract the long-range van der Waals and electrostatic field interaction parameters, and store them in the corresponding force field parameter search 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: Based on the atomic bonding category array of the asphalt binder component, the chemical bond parameters of the asphalt binder component molecules are obtained through the force field parameter retrieval array of S33; for chemical bonds that are not in the force field parameter retrieval array, an equivalent replacement atomic bonding category is selected based on the bonding information of the chemical bond, and the equivalent replacement atomic bonding category is updated to the atomic bonding category array, and finally all chemical bond parameters of the asphalt binder component molecules are obtained and stored in the component molecule force field parameter array;

[0023] S36: Loop steps S32-S35 to obtain the molecular force field parameters of all components in the asphalt binder model and store them in the corresponding molecular force field parameter arrays.

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

[0025] S41: Based on the component molecules of the asphalt binder model and the component molecule force field parameters 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: Based on the model requirements input by the user, steps S2-S41 are looped to automatically generate all required asphalt binder models, thereby realizing 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 an atom 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 construction of the initial model 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 Flowchart of the high-throughput molecular modeling method for asphalt binders 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 showing 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 with reference to the accompanying drawings and preferred embodiments, and the purpose and effects of the present invention will become more apparent. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended 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 to 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, the topology information of all asphalt binder models 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 for 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 existing literature on asphalt binder molecular models, collect the molecular formulas or model files of asphalt binder components. This example takes the 12-molecule asphalt binder model of the literature [Chemical compositions of improved model asphalt systems for molecular simulations, DD Li, ML Greenfield, 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 was drawn in Avogadro software based on its chemical formula, and the topological information of each molecule was finally stored in the pdb file format.

[0040] S12: Build a database based on the above asphalt binder component molecules. Considering the four components of asphalt, create four sub-molecule libraries: an asphaltene library, a cycloalkane library, a polar aromatic hydrocarbon library, and a saturated hydrocarbon library. Name each component molecule according to its molecular category and molecular formula to form an asphalt binder component library and a molecular search index.

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

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

[0043] S2: Creating a random asphalt binder model

[0044] S21: Set the molecular ratio of asphaltene / cycloalkanes / polar aromatics / saturated hydrocarbons 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 judgment condition is met, 12 asphaltene molecules, 5 cycloalkane molecules, 5 polar aromatic hydrocarbon 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: Based on the set parameters of the asphalt binder model in S21 and the retrieved component molecular model, an input file for the open source software Packmol is generated, thereby calling Packmol to create two asphalt binder models and outputting the pdb topology file of the asphalt binder model. Figure 2 Two random asphalt binder models generated are shown.

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

[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 model's molecular information, including chemical bond chain information, atomic element names, charges, and coordinates. The chemical bond chain information is the atomic neighbor information for each chemical bond, including pairwise information corresponding to bond lengths, three-atom information corresponding to bond angles, and four-atom information corresponding to torsion angles and out-of-plane interactions. This molecular information is stored by defining different arrays.

[0049] S32: Based on the molecular formula of the asphalt binder component, establish a function to determine the chemical bonding type of the asphalt binder component atoms. This includes a function to determine the aromaticity of the asphalt binder component atoms and a function to determine the bonding type of all atoms in the asphalt binder component, including C, H, and O atoms. The aromaticity function primarily considers the chemical bonding environments of six-ring and five-ring atoms. The determination of the chemical bonding type of atoms relies on the molecular information extracted in 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: Based on 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 based on 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: Loop steps S32-S35 to obtain the molecular force field parameters of all components in the asphalt binder model and store them in the corresponding molecular force field parameter arrays.

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

[0055] S4: High-throughput modeling

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

[0057] S42: Based on the model requirements initially input, steps S2-S41 are looped to automatically generate the two required asphalt binder models, thereby achieving 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 programming in C++ or python.

[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 will understand that the foregoing descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. Although the invention has been described in detail with reference to the foregoing examples, those skilled in the art will still be able to modify the technical solutions described in the foregoing examples or substitute equivalents for some of the technical features therein. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the invention shall be included within the scope of protection 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 literature on asphalt binder molecular models, collect the molecular chemical formulas or model files of asphalt binder components. Considering the four components of asphalt binder, establish four asphalt binder component molecular libraries, including asphaltene molecular library, cycloalkane molecular library, polar aromatic hydrocarbon molecular library and saturated hydrocarbon molecular library. At the same time, calculate the molecular average molar mass of the asphalt binder components in each molecular library. S2: Call the four asphalt binder component molecule libraries created in step S1, randomly select asphalt binder component molecules based on the size, total number of component molecules, component molecule ratio, and modeling times of the asphalt binder model, and create a random asphalt binder model. 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; Comparing the set volume of the asphalt binder model with the theoretical volume, 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; S22: Based on the set parameters of the asphalt binder model in S21 and the retrieved component molecules, an input file for the open source software Packmol is generated, thereby calling Packmol to create a random asphalt binder model and outputting a pdb topology file of the 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 type 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 category in different molecules of the asphalt binder model established in step S2, and thus obtain the corresponding molecular force field parameters; 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 coordinates of the atom; S32: establishing a chemical bonding type judgment function for the atoms of the asphalt binder component according to the molecular chemical formula 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 type 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 search 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 search array; extract the long-range van der Waals and electrostatic field interaction parameters, and store them in the corresponding force field parameter search 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: Based on the atomic bonding category array of the asphalt binder component, the chemical bond parameters of the asphalt binder component molecules are obtained through the force field parameter retrieval array of S33; for chemical bonds that are not in the force field parameter retrieval array, an equivalent replacement atomic bonding category is selected based on the bonding information of the chemical bond, and the equivalent replacement atomic bonding category is updated to the atomic bonding category array, and finally all chemical bond parameters of the asphalt binder component molecules are obtained and stored in the component molecule force field parameter array; S36: looping steps S32-S35 to obtain 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 component; 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 existing literature on asphalt binder molecular models, collect the chemical formulas or model files of asphalt binder component molecules. If only the chemical formula is available, draw the corresponding molecular model using Material Studio or Avogadro software. Finally, store the topological information of each molecule in the pdb file format. S12: Based on 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. 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 1, characterized in that: Step S4 specifically includes the following sub-steps: S41: Based on the component molecules of the asphalt binder model and the component molecule force field parameters 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: Based on the model requirements input by the user, steps S2-S41 are looped to automatically generate all required asphalt binder models, thereby realizing automated high-throughput modeling.

4. The high-throughput molecular modeling method for asphalt binders according to claim 1, 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-atom information corresponding to the bond angle, and four-atom information corresponding to the torsion angle and out-of-plane interaction.

5. The high-throughput molecular modeling method for asphalt binders according to claim 1, 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.

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