Steel bar corrosion inhibitor surface adsorption efficiency evaluation method based on molecular dynamics and quantum chemistry theories

Abstract

The invention belongs to the field of steel bar corrosion inhibitors, and particularly relates to a steel bar corrosion inhibitor surface adsorption efficiency evaluation method based on molecular dynamics and quantum chemistry theories, which comprises the following steps: quantifying the corrosion inhibition efficiency of different organic corrosion inhibitors through a molecular dynamics and quantum chemistry analysis method; the molecular dynamics simulation comprises adsorption process simulation and radial distribution calculation of binding energy and an organic corrosion inhibitor relative to the surface of the steel bar, and the quantum chemistry simulation comprises front track and surface electrostatic potential calculation in quantum chemistry; the quantification of the corrosion inhibition efficiency of the corrosion inhibitor can be realized by comprehensively evaluating the simulation results of molecular dynamics and quantum chemistry. Adsorption behaviors of various organic corrosion inhibitor molecules on the surface of the steel bar in different solution environments can be simulated, adsorption sites of the corrosion inhibitor molecules can be judged, the adsorption capacity of the corrosion inhibitor molecules can be calculated, and the corrosion inhibition performance of the organic corrosion inhibitor can be quantified, so that a basis is provided for predicting the durability life of the steel bar in a corrosion environment.

Description

Technical Field

[0001] The invention belongs to the technical 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. Background Art

[0002] Steel bars are widely used in many fields, but they are susceptible to corrosion, which greatly affects their service life as well as the performance and safety of related products. In order to delay the corrosion of steel bars, rust inhibitors are usually used. The main types of early steel bar rust inhibitors were inorganic rust inhibitors, such as inorganic nitrite rust inhibitors. However, as the environmental defects of these rust inhibitors have become increasingly prominent, organic rust inhibitors have received more and more attention due to their good environmental protection and adaptability.

[0003] However, the adsorption behavior of rust inhibitors on the surface of steel bars is closely related to the molecular types of the rust inhibitors. 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 operations. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art 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, so as to overcome the many shortcomings of the prior art when 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 inhibitor based on molecular dynamics and quantum chemistry theory includes the following steps:

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

[0008] Establish a steel bar surface molecular model, a rust inhibitor molecular model and a salt solution molecular model; the salt solution molecular model includes a rust inhibitor molecular model, a water molecular model and an anion and cation model; place the steel bar surface molecular model below the salt solution molecular model 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 coordinate trajectory of atomic motion;

[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 calculation based on the stable molecular configuration obtained in step (4), using natural bond orbital analysis at the GGA / BLYP level to obtain the atomic charges of the corresponding molecules;

[0013] (6) according to 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 molecular surface electrostatic potential 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 of vacuum layer.

[0016] Furthermore, 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.

[0017] Furthermore, in step (2), during the molecular dynamics simulation, two layers of atoms on the surface of the steel bar are relaxed, and all 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 geometrically optimize its structure without restricting the symmetry of its configuration, so as 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 chemical simulation includes frontier orbit and surface electrostatic potential calculation in quantum chemistry, and the rust inhibition efficiency of the rust inhibitor can be quantified by comprehensively evaluating the molecular dynamics and quantum chemical simulation results. 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 rust inhibitor molecules, calculate the adsorption capacity of rust inhibitor molecules, and quantify the rust inhibition performance of organic rust inhibitors, 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 the surface of steel bars at the molecular scale, determine the adsorption form of organic rust inhibitor molecules on the surface of steel bars and predict the adsorption sites. It can evaluate the adsorption efficiency and rust inhibition performance of organic rust inhibitors on the surface of steel bars by calculating the parameters related to the adsorption performance, thus providing a new way to quickly, efficiently and accurately analyze the adsorption behavior of organic rust inhibitors on the surface of steel bars and further infer their rust inhibition performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper 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 of 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] Fig. 9 This is the RDF curve of the proline molecule of Example 2 of the present invention.

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

[0039] Fig.11 This is a simulation of the adsorption process of nicotinic acid molecules in Example 3 of the present invention.

[0040] Fig.12 This is the RDF curve of the nicotinic acid molecule of Example 3 of the present invention.

[0041] Fig.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 inhibitor based on molecular dynamics and quantum chemistry theory includes the following steps:

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

[0045] Establish the steel bar surface molecular model, the rust inhibitor molecular model and the salt solution molecular model; the salt solution molecular model includes the rust inhibitor molecular model, the water molecular model and the anion and cation model. The salt solution model is constructed by mixing anions and cations with water molecules, and the ratio of the number of anions and cations satisfies the chemical molecular formula, so as to obtain 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, and the various parts of the initial model are relaxed for 100 to 200 ps under the NVT ensemble to make them close to each other and form a stable molecular structure.

[0047] Among them, the steel bar surface is selected as Fe(111) surface, and the atomic layer thickness is greater than The molecular model of the salt solution containing the rust inhibitor should fit closely with the atoms of the molecular model of the steel bar surface, and contain 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 a sodium chloride solution, a sodium hydroxide solution, a calcium hydroxide solution, and a potassium hydroxide solution.

[0048] (2) Conduct 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 atom's 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] In the formula, 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 the 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, and 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 adsorption model of the organic rust inhibitor 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 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 on its structure without restricting its symmetry, thus obtaining a stable molecular configuration.

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

[0061] (6) according to 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 is and the worse the reaction activity is, and vice versa;

[0064] (7) analyzing the molecular surface electrostatic potential (MEP) according to the atomic charge of the rust inhibitor molecule obtained in step (5), marking the acid-base region of the rust inhibitor molecular model using Lewis acid-base theory, and predicting the adsorption site of the rust 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 rust inhibitor molecules, calculate the adsorption capacity of rust inhibitor molecules, and quantify the rust inhibition performance of organic rust inhibitors, thereby providing a basis for predicting the durability life of steel bars in a corrosive environment; compared with the prior art 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 performance of benzotriazole, proline and nicotinic acid is explained as an example.

[0068] Example 1

[0069] Taking benzotriazole as an example, the actual simulation calculation process of the present invention is specifically described, which includes 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 crystal surface Fe(111) 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 500 water molecules, 1 sodium ion, 1 hydroxide ion and 1 benzotriazole (BTA) are mixed 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) Conduct molecular dynamics simulation:

[0073] After applying the force field to each atom, the simulation calculation is performed in Material Studio. The system temperature is selected as 300K, the pressure is one atmosphere, and the time step is 1fs. Periodic boundaries are set in the X, Y, and Z directions. The initial velocity of each atom is randomly generated according to the initial temperature. The Smart algorithm is used to calculate the position of the atom at the next moment. The long-range force cutoff radius is The simulation process is divided into three steps: first, fix all atoms below the top 2 layers of the steel bar, then optimize the geometry of the structure by minimizing the energy; finally, let the whole system move for 200 ps (picoseconds) under the canonical ensemble (NVT) to achieve the balance of each part of the structure. During the molecular dynamics simulation, the thermodynamic parameters such as temperature and pressure 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.

[0074] (3) The process of rust inhibitor adsorption on the steel bar surface is as follows Figure 4 As shown, 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 according to 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 and 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 is calculated, and the natural bond orbital (NBO) analysis is 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. The acidic and alkaline regions of the rust inhibitor molecules are marked according to the Lewis acid-base theory 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 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. Fig. 9 is the radial distribution curve (RDF) of proline molecules and the two layers of atoms on the surface of the steel bar. Fig.10 is the surface electrostatic potential distribution of proline.

[0084] Example 3

[0085] The rust inhibitor in Example 1 is replaced with nicotinic acid, and other specific implementation methods are 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. Fig. 9 is the radial distribution curve (RDF) of proline molecules and the two layers of atoms on the surface of the steel bar. Fig.10 is the surface electrostatic potential distribution of proline.

[0087] Table 1 Results of Example 1-Example 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. By comparing Example 1, Example 2, and Example 3, from the perspective of RDF, the niacin molecule The range has the highest number of atoms bound to 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 it has 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 inhibitor based on molecular dynamics and quantum chemistry theory, characterized in that: The following steps are involved: (1) Construction of molecular dynamics model: Establish a steel bar surface molecular model, a rust inhibitor molecular model and a salt solution molecular model; the salt solution molecular model includes a rust inhibitor molecular model, a water molecular model and an anion and cation model; place the steel bar surface molecular model below the salt solution molecular model 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 coordinate trajectory of atomic motion; (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 calculation based on the stable molecular configuration obtained in step (4), using natural bond orbital analysis at the GGA / BLYP level to obtain the atomic charges of the corresponding molecules; (6) according to 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 molecular surface electrostatic potential 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 inhibitor based on molecular dynamics and quantum chemistry theory according to claim 1 is 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 of the vacuum layer.

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

5. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitor based on molecular dynamics and quantum chemistry theory according to claim 1 is characterized in that: 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 inhibitor based on molecular dynamics and quantum chemistry theory according to claim 1 is 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 inhibitor based on molecular dynamics and quantum chemistry theory according to claim 1 is 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 inhibitor based on molecular dynamics and quantum chemistry theory according to claim 1 is 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 geometrically optimize its structure without restricting the symmetry of its configuration to obtain a stable molecular configuration.

9. The method for evaluating the surface adsorption efficiency of steel bar rust inhibitor based on molecular dynamics and quantum chemistry theory according to claim 1 is characterized in that: The method further comprises step (8), comparing the binding energy, HOMO value, reactivity and Lewis basic region of various 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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