Evaluation method of surface adsorption efficiency of steel bar rust inhibitor based on molecular dynamics and quantum chemistry theory

Abstract

The present invention belongs to the field of steel bar rust inhibitors, and specifically relates to a method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory. The rust inhibition efficiency of different types of organic rust inhibitors is quantified by molecular dynamics and quantum chemistry analysis methods. Molecular dynamics simulation includes adsorption process simulation, binding energy and radial distribution calculation of organic rust inhibitors relative to the steel bar surface, and quantum chemistry simulation includes frontier orbital and surface electrostatic potential calculation in quantum chemistry. The rust inhibition efficiency of the rust inhibitor can be quantified by comprehensively evaluating the results of molecular dynamics and quantum chemistry simulations. The present invention can simulate the adsorption behavior of various organic rust inhibitor molecules on the steel bar surface under different solution environments, determine the adsorption sites of the rust inhibitor molecules, calculate the adsorption capacity of the rust inhibitor molecules, and quantify the rust inhibition performance of the organic rust inhibitor, thereby providing a basis for predicting the durability life of steel bars in corrosive environments.

Description

Technical Field

[0001] The invention belongs to the technical field of steel bar rust inhibitors, and particularly relates to a method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory. Background Art

[0002] Rebar is widely used in numerous fields, but it is susceptible to corrosion, which significantly impacts its service life as well as the performance and safety of related products. Rust inhibitors are often used to slow down the corrosion of rebar. Early versions of these inhibitors were primarily inorganic, such as inorganic nitrites. However, as the environmental limitations of these inhibitors have become increasingly prominent, organic inhibitors have gained increasing attention due to their environmental friendliness and adaptability.

[0003] However, the adsorption behavior of rust inhibitors on the surface of steel bars is closely related to the molecular type of the rust inhibitor. When developing and screening organic rust inhibitors, the traditional method of evaluating their rust inhibition performance through experiments is often time-consuming, labor-intensive, and costly, requiring a large amount of sample preparation and long-term corrosion testing. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for the adsorption and rust inhibition performance of organic rust inhibitors on the surface of steel bars based on the combination of molecular dynamics and quantum chemical simulation, which provides a new way to quickly, efficiently and accurately analyze the adsorption behavior of organic rust inhibitors on the surface of steel bars and then infer their rust inhibition performance, thereby overcoming the many shortcomings of the existing technology in relying on traditional experiments to evaluate the rust inhibition performance of organic rust inhibitors.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory includes the following steps:

[0007] (1) Construction of molecular dynamics model:

[0008] Establish a molecular model of the steel bar surface, a molecular model of the rust inhibitor, and a molecular model of the salt solution; the molecular model of the salt solution includes a molecular model of the rust inhibitor, a molecular model of water, and a model of anions and cations; place the molecular model of the steel bar surface below the molecular model of the salt solution to form an initial model, and relax under the NVT ensemble so that the various parts of the initial model are close to each other to form a stable molecular structure;

[0009] (2) Perform molecular dynamics simulation: apply force to each atom in the molecular model, set initial conditions, perform molecular dynamics simulation, and obtain the atomic motion coordinate trajectory;

[0010] (3) According to the atomic motion coordinate trajectory obtained in step (2), the position and adsorption configuration of the organic rust inhibitor molecules in the salt solution at different times are obtained, the correlation curve between the adsorption distance and time of the organic rust inhibitor on the steel bar surface is drawn, and the radial distribution curve of the organic rust inhibitor adsorbed on the steel bar surface is analyzed to obtain the corresponding adsorption mode; and by calculating the overall energy of the adsorption model of the organic rust inhibitor on the steel bar surface, whether the model is stable is determined; if the model is stable, its binding energy is calculated to determine the adsorption performance;

[0011] (4) Perform quantum chemical simulation: establish the molecular configuration of the organic rust inhibitor, perform geometric optimization on its structure, and obtain a stable molecular configuration;

[0012] (5) performing energy calculations based on the stable molecular configurations obtained in step (4) and using natural bond orbital analysis at the GGA / BLYP level to obtain the atomic charges of the corresponding molecules;

[0013] (6) Based on the atomic charge of the rust inhibitor molecule obtained in step (5), the HOMO and LUMO orbital energies of the rust inhibitor molecule are calculated using molecular frontier orbital theory, and the molecular reactivity α is calculated;

[0014] (7) According to the atomic charge of the rust inhibitor molecule obtained in step (5), the surface electrostatic potential of the molecule is analyzed, and the acid-base region of the rust inhibitor molecular model is marked using Lewis acid-base theory to predict the adsorption site of the rust inhibitor molecular model on the steel bar surface molecular model.

[0015] Furthermore, the steel bar surface molecular model selects the Fe(111) surface atomic layer thickness greater than The molecular model of the salt solution fits closely with the molecular model of the steel bar surface, and contains a height greater than vacuum layer.

[0016] Furthermore, the constructed rust inhibitor molecular model is identical to the actual organic rust inhibitor molecular structure; the salt solution model is constructed by mixing anions, cations and water molecules, with the ratio of the number of anions and cations satisfying the chemical molecular formula, thereby obtaining a salt solution of a certain concentration.

[0017] Furthermore, in step (2), during the molecular dynamics simulation, the two layers of atoms on the surface of the steel bar are relaxed, and all the iron atoms below the two layers on the surface are fixed.

[0018] Furthermore, in step (3) E ads The binding energy is:

[0019] E ads =E total -E iron -E inhibitor ;

[0020] In formula (2), Eads represents the binding energy, E total Represents the total energy of the simulation system, E iron Represents the energy of the steel bar surface when it exists alone in the simulation system, E inhibitor Represents the energy of the rust inhibitor molecule when it exists alone in the simulation system.

[0021] Furthermore, in step (2), the Nose-Hoover constant temperature calculation method is used to ensure the stability of the system during the simulation process, and the Smart algorithm is used to perform molecular dynamics simulation to obtain the atomic motion coordinate trajectory.

[0022] Furthermore, in step (2), during the molecular dynamics simulation, the thermodynamic parameters and the size of the box are output once every 1 ps, and the coordinates of all atoms are output once, with a total of 200 frames of atomic coordinates output.

[0023] Furthermore, in step (4), the molecular configuration of the organic rust inhibitor is established, and the BLYP generalized gradient approximation of the GGA functional is used to perform geometric optimization on the structure without restricting the symmetry of the configuration to obtain a stable molecular configuration.

[0024] Furthermore, the method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory also includes step (8), comparing the binding energy, HOMO value, reactivity, and Lewis alkaline region of multiple organic rust inhibitors, wherein the organic rust inhibitor with the largest binding energy, reactivity, and the most Lewis alkaline region has the best rust inhibition performance.

[0025] The principle of the present invention is: molecular dynamics simulation includes adsorption process simulation, binding energy and radial distribution calculation of rust inhibitor relative to the steel bar surface; quantum chemistry simulation includes frontier orbital and surface electrostatic potential calculation in quantum chemistry. By comprehensively evaluating the molecular dynamics and quantum chemistry simulation results, the rust inhibition efficiency of the rust inhibitor can be quantified. The present invention can simulate the adsorption behavior of various organic rust inhibitor molecules on the steel bar surface under different solution environments, determine the adsorption sites of the rust inhibitor molecules, calculate the adsorption capacity of the rust inhibitor molecules, and quantify the rust inhibition performance of the organic rust inhibitor, thereby providing a basis for predicting the durability life of steel bars in corrosive environments.

[0026] The beneficial effects of the present invention are:

[0027] The present invention is suitable for evaluating the adsorption effect of all organic rust inhibitors. It can reveal the interaction mechanism between organic rust inhibitors and steel bar surfaces at the molecular scale, determine the adsorption form of organic rust inhibitor molecules on the steel bar surface and predict the adsorption sites. By calculating the parameters related to the adsorption performance, the adsorption efficiency and rust inhibition performance of organic rust inhibitors on the steel bar surface are evaluated, providing a new way to quickly, efficiently and accurately analyze the adsorption behavior of organic rust inhibitors on the steel bar surface and further infer their rust inhibition performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. Among them:

[0029] Figure 1 Schematic diagram of the molecular model of the steel bar surface according to an embodiment of the present invention.

[0030] Figure 2 Schematic diagram of the molecular model of benzotriazole according to an embodiment of the present invention.

[0031] Figure 3 Schematic diagram of the molecular model of the salt solution according to an embodiment of the present invention.

[0032] Figure 4 This is the adsorption model of small molecules of organic rust inhibitor on the surface of steel bars according to an embodiment of the present invention.

[0033] Figure 5 This is a simulation of the adsorption process of the benzotriazole molecule in Example 1 of the present invention.

[0034] Figure 6 This is the RDF curve of the benzotriazole molecule of Example 1 of the present invention.

[0035] Figure 7 This is the electrostatic potential distribution diagram of the benzotriazole molecule of Example 1 of the present invention.

[0036] Figure 8 This is a simulation of the adsorption process of the proline molecule in Example 2 of the present invention.

[0037] Figure 9 This is the RDF curve of the proline molecule of Example 2 of the present invention.

[0038] Figure 10 This is the electrostatic potential distribution diagram of the proline molecule in Example 2 of the present invention.

[0039] Figure 11 This is a simulation of the adsorption process of niacin molecules in Example 3 of the present invention.

[0040] Figure 12 This is the RDF curve of the niacin molecule of Example 3 of the present invention.

[0041] Figure 13 This is the electrostatic potential distribution diagram of the niacin molecule of Example 3 of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0043] The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory includes the following steps:

[0044] (1) Construction of molecular dynamics model:

[0045] Establish a molecular model of the steel bar surface, a molecular model of the rust inhibitor, and a molecular model of the salt solution. The molecular model of the salt solution includes a molecular model of the rust inhibitor, a molecular model of water, and a model of anions and cations. The salt solution model is constructed by mixing anions and cations with water molecules so that the ratio of the number of anions and cations satisfies the chemical formula, thereby obtaining a salt solution of a certain concentration.

[0046] The steel bar surface molecular model is placed under the salt solution molecular model (referred to as the salt solution model) to form an initial model. The initial model is relaxed for 100 to 200 ps under the NVT ensemble to allow the various parts of the initial model to approach each other and form a stable molecular structure.

[0047] Among them, the steel bar surface is Fe(111) surface, and the atomic layer thickness is greater than The molecular model of the salt solution containing the rust inhibitor should be closely fitted with the atoms of the molecular model of the steel bar surface, and a height greater than vacuum layer; the molecular model of the rust inhibitor is the same as the molecular structure of the actual organic rust inhibitor; the salt solution is at least one of sodium chloride solution, sodium hydroxide solution, calcium hydroxide solution, and potassium hydroxide solution.

[0048] (2) Perform molecular dynamics simulation:

[0049] A force is applied to each atom in the molecular model of the initial model constructed in step (1) (including the steel bar surface molecular model, the rust inhibitor molecular model, and the salt solution molecular model). The force field describes the potential energy driving the atomic motion in the system, which is converted into the acceleration of each atomic motion through Newton's second law. Given the initial position and velocity, the coordinates of each atom at each time point can be calculated. The force field adopts an empirical force field, and its basic form is shown in formula (1), including the bond energy between atoms, the van der Waals force, and the long-range Coulomb force:

[0050] E total =E bond +Eangle +E torsion +E vdw +E coul (1);

[0051] Where, E total represents the total potential energy, E bond represents the bond stretching potential energy, E angle represents the bond angle bending potential energy, E torsion represents the dihedral angle distortion potential energy, E vdw represents the van der Waals force, E coul represents the Coulomb electrostatic potential energy;

[0052] Set initial conditions: Given a preset ambient temperature, calculation time step, and the outer edge of the overall molecular model as a periodic boundary condition; use the Nose-Hoover constant temperature calculation method to ensure system stability during the simulation process; use the Smart algorithm to perform molecular dynamics simulation to obtain the atomic motion coordinate trajectory.

[0053] (3) according to the atomic motion coordinate trajectory obtained in step (2), the position and adsorption configuration of the organic rust inhibitor molecules in the salt solution at different times are obtained, and the correlation curve between the adsorption distance and time of the organic rust inhibitor on the steel bar surface is drawn, and the radial distribution curve of the organic rust inhibitor adsorbed on the steel bar surface is analyzed to obtain the corresponding adsorption mode;

[0054] According to the atomic motion coordinate trajectory obtained in step (2), the position and adsorption configuration of the organic rust inhibitor molecules in the salt solution at different simulation times are obtained, and the overall energy of the organic rust inhibitor adsorption model on the steel bar surface is calculated to determine whether the model is stable; when the overall energy is below the convergence value and the change is less than 5%, and the temperature is stable within 10% of the set temperature, the model is considered stable, and its binding energy can be calculated to determine the adsorption performance;

[0055] The binding energy calculation is to calculate the binding energy between the rust inhibitor and the steel bar surface after deleting the solvent molecules in the simulation system. The formula is:

[0056] E ads =E total -E iron -E inhibitor (2);

[0057] In formula (2), E ads represents the binding energy, E total Represents the total energy of the simulation system, E iron Represents the energy of the steel bar surface when it exists alone in the simulation system, E inhibitor Represents the energy of the rust inhibitor molecule when it exists alone in the simulation system.

[0058] (4) Perform quantum chemical simulation:

[0059] The molecular configuration of the organic rust inhibitor was established, and the BLYP generalized gradient approximation of the GGA functional was used to perform geometric optimization of its structure without restricting its symmetry to obtain a stable molecular configuration.

[0060] (5) performing energy calculations based on the stable molecular configurations obtained in step (4) and using natural bond orbital (NBO) analysis at the GGA / BLYP level to obtain the atomic charges of the corresponding molecules;

[0061] (6) Based on the atomic charge of the rust inhibitor molecule obtained in step (5), the HOMO and LUMO orbital energies of the rust inhibitor molecule are calculated using molecular frontier orbital theory, and the molecular reactivity α is calculated;

[0062] α = ELUMO-EHOMO;

[0063] Among them, the larger the value of α, the more stable the molecule and the worse the reaction activity, and vice versa;

[0064] (7) Analyzing the molecular surface electrostatic potential (MEP) based on the atomic charge of the inhibitor molecule obtained in step (5), marking the acid and base regions of the inhibitor molecular model using Lewis acid-base theory, and predicting the adsorption sites of the inhibitor molecular model on the steel bar surface molecular model;

[0065] Among them, the steel bar surface is regarded as Lewis acid, and the area with positive electrostatic potential on the surface of the organic rust inhibitor molecular model is marked as the acidic area, and the area with negative electrostatic potential is marked as the alkaline area.

[0066] The present invention can simulate the adsorption behavior of various organic rust inhibitor molecules on the surface of steel bars under different solution environments, determine the adsorption sites of the rust inhibitor molecules, calculate the adsorption capacity of the rust inhibitor molecules, and quantify the rust inhibition performance of the organic rust inhibitors, thereby providing a basis for predicting the durability life of steel bars in corrosive environments. Compared with the existing technology that relies on traditional experiments to evaluate the rust inhibition performance of organic rust inhibitors, the present invention can quickly, efficiently and accurately evaluate and compare the rust inhibition performance of organic rust inhibitors.

[0067] Next, the rust inhibition properties of benzotriazole, proline and nicotinic acid are used as examples to illustrate.

[0068] Example 1

[0069] The actual simulation calculation process of the present invention is specifically described using benzotriazole as an example, comprising the following steps:

[0070] (1) First establish a molecular dynamics model:

[0071] The steel bar surface model is as follows Figure 1As shown in the figure, the highly active Fe(111) surface is used as the adsorption surface, and the surface structural parameters of the cut steel bar are: Salt solution model Figure 3 As shown, the model size is Mix 500 water molecules, 1 sodium ion, 1 hydroxide ion and 1 benzotriazole (BTA) to obtain a 0.1 mol / L rust inhibitor solution with a pH of 13. Figure 2 As shown, the benzotriazole model is the same as the actual molecular structure, and the molecular formula is C6H5N3.

[0072] (2) Perform molecular dynamics simulation:

[0073] After applying the force field to each atom, simulation calculations were performed in Material Studio. The system temperature was selected to be 300K, the pressure to be one atmosphere, and the time step to be 1 fs. Periodic boundaries were set in the X, Y, and Z directions, and the initial velocity of each atom was randomly generated according to the initial temperature. The Smart algorithm was used to calculate the position of the atom at the next moment, and the long-range force cutoff radius was The simulation process consists of three steps: first, all atoms below the top two layers of the rebar are fixed. Then, the structure's geometry is optimized through energy minimization. Finally, the entire system is moved for 200 picoseconds (ps) under a canonical ensemble (NVT) to achieve equilibrium in each component. During the molecular dynamics simulation, thermodynamic parameters such as temperature and pressure, as well as the dimensions of the box, are output every 1 ps, along with the coordinates of all atoms, for a total of 200 frames of atomic coordinate output.

[0074] (3) The process of rust inhibitor adsorption on the steel bar surface is as follows Figure 4 As shown in the figure, the radial distribution curves (RDF) of the rust inhibitor molecules and the two layers of atoms on the surface of the steel bar at different times are calculated based on the atomic coordinates. The adsorption type is determined by analyzing the adsorption radius of the densest atoms. The corresponding frame with the lowest energy in the simulation process is found and output, and the binding energy during adsorption is calculated. The binding energy of benzotriazole with the steel bar surface is -2.589eV.

[0075] (4) Perform quantum chemical simulation:

[0076] When performing quantum mechanics simulation, the molecular structure of the rust-inhibiting molecule - benzotriazole - is first established, and the geometric structure is optimized through the Dmol3 module of MaterialStudio. The calculation parameters are GGA functional and BLYP basis set.

[0077] (5) The energy of the rust inhibitor molecules after geometric structure optimization was calculated, and the natural bond orbital (NBO) analysis was performed at the GGA / BLYP level to obtain the atomic charges of the corresponding molecules.

[0078] (6) Analyze the rust inhibitor molecules after energy calculation, obtain their LUMO and HOMO values, and calculate the reaction activity α.

[0079] (7) Finally, the rust inhibitor molecules after energy calculation are analyzed to obtain their surface electrostatic potential distribution. According to the Lewis acid-base theory, the acidic and alkaline regions of the rust inhibitor molecules are marked to determine the adsorption sites of the rust inhibitor molecules on the steel bar surface.

[0080] In this Example 1, the binding energy of benzotriazole with the steel bar surface is -2.589 eV, the HOMO value is -6.4639 eV, the LUMO value is -2.1093 eV, and the reaction activity α is 4.3546 eV. Figure 5 This is a simulation diagram of the adsorption process of benzotriazole molecules in Example 1. Figure 6 : This is the radial distribution curve (RDF) of the benzotriazole molecules and the atoms in the second layer of the steel bar surface in Example 1. Figure 7 This is the surface electrostatic potential distribution of benzotriazole molecule BTA. The red color represents the Lewis acidic region and the blue color represents the Lewis basic region.

[0081] Example 2

[0082] The organic rust inhibitor in Example 1 was replaced with proline, and the rest was the same as in Example 1.

[0083] In this Example 2, the binding energy of proline to the steel bar surface is -2.095 eV, the HOMO value is -5.5553 eV, the LUMO value is -0.6820 eV, and the reaction activity α is 4.8733 eV. Figure 8 This is a simulation diagram of the adsorption process of proline molecules. Figure 9 is the radial distribution curve (RDF) of proline molecules and the two layers of atoms on the surface of the steel bar. Figure 10 is the surface electrostatic potential distribution of proline.

[0084] Example 3

[0085] The rust inhibitor in Example 1 was replaced with nicotinic acid, and other specific implementation methods were the same as those in Example 1.

[0086] In this Example 2, the binding energy of proline to the steel bar surface is -2.095 eV, the HOMO value is -5.5553 eV, the LUMO value is -0.6820 eV, and the reaction activity α is 4.8733 eV. Figure 8 This is a simulation diagram of the adsorption process of proline molecules. Figure 9 is the radial distribution curve (RDF) of proline molecules and the two layers of atoms on the surface of the steel bar. Figure 10 is the surface electrostatic potential distribution of proline.

[0087] Table 1 Results of Examples 1 to 3

[0088] Binding energy HOMO value LUMO Reactivity α Benzotriazole -2.589eV -6.4639eV -2.1093eV 4.3546eV Proline -2.095eV -5.5553eV -0.6820eV 4.8733eV niacin -2.871eV -6.0655eV -2.6393eV 3.4262eV

[0089] The results of Examples 1-3 are summarized in Table 1. Comparing Example 1, Example 2, and Example 3, from the perspective of RDF, the nicotinic acid molecule The range has the highest number of bound atoms with iron atoms, indicating that its adsorption process is the most significant. From the binding energy point of view, nicotinic acid molecules have the largest binding energy with the steel bar surface, indicating that its adsorption is the most stable. From the frontier orbital theory, it can be concluded that benzotriazole BTA has the largest HOMO value (absolute value), indicating that it has the strongest electron-donating ability among the three rust inhibitors, while nicotinic acid molecules have the smallest LUMO-HOMO value, indicating that its reaction activity is the greatest. From the perspective of surface electrostatic potential, nicotinic acid molecules have the most Lewis basic regions, indicating that they have the most binding sites when adsorbed on the steel bar surface. In summary, based on the method combining molecular dynamics and quantum chemistry theory, it can be inferred that nicotinic acid molecules have the best adsorption efficiency on steel bars among the three rust inhibitor molecules, indicating that they have the best rust inhibition performance.

[0090] 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 are within the scope of protection of the pending claims of the present invention.

Claims

1. A method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory, characterized in that: The following steps are involved: (1) Construction of molecular dynamics model: Establish a molecular model of the steel bar surface, a molecular model of the rust inhibitor, and a molecular model of the salt solution; the molecular model of the salt solution includes a molecular model of the rust inhibitor, a molecular model of water, and a model of anions and cations; place the molecular model of the steel bar surface below the molecular model of the salt solution to form an initial model, and relax under the NVT ensemble so that the various parts of the initial model are close to each other to form a stable molecular structure; (2) Perform molecular dynamics simulation: apply force to each atom in the molecular model, set initial conditions, perform molecular dynamics simulation, and obtain the atomic motion coordinate trajectory; (3) According to the atomic motion coordinate trajectory obtained in step (2), the position and adsorption configuration of the organic rust inhibitor molecules in the salt solution at different times are obtained, the correlation curve between the adsorption distance and time of the organic rust inhibitor on the steel bar surface is drawn, and the radial distribution curve of the organic rust inhibitor adsorbed on the steel bar surface is analyzed to obtain the corresponding adsorption mode; and by calculating the overall energy of the adsorption model of the organic rust inhibitor on the steel bar surface, whether the model is stable is determined; if the model is stable, its binding energy is calculated to determine the adsorption performance; (4) Perform quantum chemical simulation: establish the molecular configuration of the organic rust inhibitor, perform geometric optimization on its structure, and obtain a stable molecular configuration; (5) performing energy calculations based on the stable molecular configurations obtained in step (4) and using natural bond orbital analysis at the GGA / BLYP level to obtain the atomic charges of the corresponding molecules; (6) Based on the atomic charge of the rust inhibitor molecule obtained in step (5), the HOMO and LUMO orbital energies of the rust inhibitor molecule are calculated using molecular frontier orbital theory, and the molecular reactivity α is calculated; (7) According to the atomic charge of the rust inhibitor molecule obtained in step (5), the surface electrostatic potential of the molecule is analyzed, and the acid-base region of the rust inhibitor molecular model is marked using Lewis acid-base theory to predict the adsorption site of the rust inhibitor molecular model on the steel bar surface molecular model.

2. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory according to claim 1, characterized in that: The steel bar surface molecular model is selected with the Fe(111) surface atomic layer thickness greater than The molecular model of the salt solution fits closely with the molecular model of the steel bar surface, and contains a height greater than vacuum layer.

3. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory according to claim 1, characterized in that: The constructed rust inhibitor molecular model is the same as the actual organic rust inhibitor molecular structure; the salt solution model is constructed by mixing anions, cations and water molecules, and the ratio of the number of anions and cations satisfies the chemical molecular formula, so that a salt solution of a certain concentration can be obtained.

4. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory according to claim 1, characterized in that: In step (2), during the molecular dynamics simulation, the two layers of atoms on the surface of the steel bar are relaxed, and all the iron atoms below the two layers on the surface are fixed.

5. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory according to claim 1, characterized in that: In step (3), E ads The binding energy is: AND ads =And total -AND iron -AND inhibitor ; In formula (2), E ads represents the binding energy, E total Represents the total energy of the simulation system, E iron Represents the energy of the steel bar surface when it exists alone in the simulation system, E inhibitor Represents the energy of the rust inhibitor molecule when it exists alone in the simulation system.

6. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory according to claim 1, characterized in that: In step (2), the Nose-Hoover constant temperature calculation method is used to ensure the stability of the system during the simulation process, and the Smart algorithm is used to perform molecular dynamics simulation to obtain the atomic motion coordinate trajectory.

7. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory according to claim 1, characterized in that: In step (2), during the molecular dynamics simulation, the thermodynamic parameters and the size of the box are output once every 1 ps, and the coordinates of all atoms are output once, with a total of 200 frames of atomic coordinates output.

8. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory according to claim 1, characterized in that: In step (4), the molecular configuration of the organic rust inhibitor is established, and the BLYP generalized gradient approximation of the GGA functional is used to perform geometric optimization on the structure without restricting the symmetry of the configuration to obtain a stable molecular configuration.

9. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitors based on molecular dynamics and quantum chemistry theory according to claim 1, characterized in that: The method further comprises step (8), comparing the binding energy, HOMO value, reactivity and Lewis basic region of a plurality of organic rust inhibitors, wherein the organic rust inhibitor with the largest binding energy, reactivity and the most Lewis basic region has the best rust inhibition performance.

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