Customized generation method of non-corrosive ionic liquid lubricant

Abstract

The invention discloses a customized generation method of a non-corrosive ionic liquid lubricant, and aims to solve the problem of negative correlation between corrosion and lubricating performance of ionic liquid in application of a metal friction pair. The ionic liquid has the characteristics of low volatility, high thermal stability and the like, but the electrochemical activity of zwitterions of the ionic liquid easily causes metal surface corrosion, and the corrosion and the lubricating property are in a negative correlation relationship. According to the method, through constructing a corrosion-lubrication multi-modal database, combining with the steps of feature engineering, collaborative prediction model training, non-corrosion formula directional generation, molecular dynamics Monte Carlo coupling verification, multi-objective optimization and the like, the whole-process intelligence from molecular structure and performance prediction to formula optimization is realized; and an ionic liquid formula with good lubricating performance and no corrosion is efficiently screened. According to the method, the research and development efficiency of the ionic liquid lubricant is remarkably improved, the research and development cost is reduced, and engineering application of the ionic liquid lubricant in key fields such as spaceflight, military industry and extreme manufacturing is promoted.

Description

Technical Field

[0001] This invention belongs to the field of high-performance lubricating material development technology, and is a customized method for generating a non-corrosive ionic liquid lubricant. Background Technology

[0002] Ionic liquids are molten salt systems composed entirely of positive and negative ions that are liquid at or near room temperature. They are typically formed by the combination of specific, relatively large organic cations and relatively small inorganic or organic anions through Coulomb forces. Their operating temperature range can reach -150℃ to 480℃, and they generally exhibit non-flammability, non-volatility, high thermal and chemical stability. These characteristics closely match the desired performance of ideal lubricants, giving them the potential to become novel lubricants. Furthermore, the tunability of cations and anions provides ionic liquids with a wider range of structural and performance options.

[0003] However, despite the excellent properties of ionic liquids such as low volatility and high thermal stability, the electrochemical activity of their cations and anions can easily induce corrosion on metal surfaces. For example, halogen-containing anions (such as [AlCl4]-, Br-) can cause corrosion. - In the presence of trace amounts of water, pitting corrosion on copper surfaces can reach a depth of 12.8 micrometers. More seriously, corrosion and lubrication performance are negatively correlated: low-viscosity acetate-based ionic liquids (<100 cP) increase the copper corrosion rate to 1.8 mg / cm³. 2 / day, while the coefficient of friction for high-corrosion-resistant formulations often exceeds 0.15. Therefore, how to quickly screen out ideal lubricants that combine non-corrosiveness, suitable viscosity, and good lubrication performance remains a major challenge in current research and development.

[0004] Traditional methods rely on repeated trial and error, requiring researchers to invest significant time and resources to individually formulate ionic liquids with different components and conduct tedious lubrication and corrosion resistance tests under various conditions. This process is not only time-consuming and resource-intensive, but also presents extremely limited experimental coverage when dealing with thousands of potential combinations, making comprehensive and efficient screening difficult. Therefore, there is an urgent need for an intelligent design method that integrates molecular structure expression, deep learning modeling, performance prediction, and formulation generation and optimization. This method would enable the rapid screening of non-corrosive and high-performance ionic liquid lubricant formulations, achieving intelligent design and optimization of ionic liquid lubricants from the "data-structure-performance" perspective. This would significantly improve the R&D efficiency of ionic liquid lubricants, reduce R&D costs, and promote their engineering applications in key fields such as aerospace, military, and extreme manufacturing. Summary of the Invention

[0005] This invention aims to provide a customized generation method for non-corrosive ionic liquid lubricants based on deep learning and intelligent optimization algorithms. Specifically, it involves a method for generating non-corrosive ionic liquid lubricants based on multimodal databases and artificial intelligence collaborative optimization. This method integrates computational electrochemistry, molecular dynamics simulations, and multi-objective optimization algorithms to solve the challenge of corrosion-lubrication synergistic regulation of ionic liquids in metal friction pair applications. It achieves intelligent processing throughout the entire process, from molecular structure and performance prediction to formulation optimization, efficiently screening ionic liquid formulations that combine good lubrication performance with non-corrosiveness. This method significantly improves the R&D efficiency of ionic liquid lubricants, reduces R&D costs, and promotes their engineering applications in key fields such as aerospace, military, and extreme manufacturing.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for custom-producing a non-corrosive ionic liquid lubricant, comprising the following steps:

[0008] (1) Construction of a corrosion-lubrication multimodal database: Ionic liquid data that simultaneously meet the requirements of electrochemical corrosion parameters and lubrication performance parameters are filtered using the structured query language SQL. The electrochemical corrosion parameters include corrosion potential E. corr Corrosion current density I ≥-0.2V relative to silver chloride electrode corr ≤0.1mA / cm 2 The material must also be a metal compatibility label containing at least one of steel, copper, and aluminum; the lubrication performance parameters include kinematic viscosity of 100–500 cP, coefficient of friction <0.08, and wear volume ≤0.5 mm. 3 The selected data were integrated according to the five-element classification model of ionic structure, environmental parameters, metal surface properties, lubrication performance and corrosion parameters, and the data source type and confidence level were marked.

[0009] (2) Corrosion inhibition feature engineering: The ionic structure is encoded using the simplified molecular linear input canonical SMILES format; basic molecular descriptors are extracted, including the number of hydrogen bond donors and van der Waals surface area; electrochemical descriptors are extracted, including the lowest unoccupied molecular orbital energy level of anions, the predicted value of double layer capacitance, and the adsorption energy of metal surface.

[0010] (33) Training of the corrosion-lubrication synergistic prediction model: Constructing a three-branch graph attention network model: The first branch predicts the lubrication performance parameter set composed of friction coefficient, wear rate and viscosity; the second branch predicts the corrosion current density I. corr Corrosion potential E corr The third branch outputs the metal compatibility probability values ​​for steel, copper, and aluminum; a dynamic loss weighting strategy is adopted: the weight coefficient for corrosion prediction is 1.3 times the base value;

[0011] (4) Targeted generation of non-corrosive formulations: The simulated annealing algorithm is used to search the ion combination space and select combinations of borate ester anions, bis(trifluoromethanesulfonyl)imide anions, and bis(oxalateborate) anions;

[0012] (5) Metal-solution interface corrosion verification: Molecular dynamics Monte Carlo coupled simulation was performed on the candidate formulations: metal-solution interface models with iron 110 crystal plane, copper 111 crystal plane, and aluminum 100 crystal plane were constructed; a ten-nanosecond trajectory was run under isothermal and isobaric ensemble, and the depth of corrosion pits and the thickness of passivation film were statistically analyzed; the acceptance criteria were as follows: And the corrosion pit density is ≤0.1nm 2 ;

[0013] (6) Multi-objective optimization output: The non-dominated sorting genetic algorithm NSGA-III is used for multi-objective optimization: the optimization objectives include minimizing the friction coefficient, minimizing the corrosion current density, and making the viscosity close to the target value; the output is a simplified molecular linear input specification SMILES structure and performance prediction report of the Pareto optimal solution.

[0014] Furthermore, in step 1, the electrochemical corrosion parameters are sourced from the International Society of Corrosion Engineers (ICSE) standard corrosion test library, with test standards ISO 9223 and ISO 9224, as well as measured data from a three-electrode system at a scan rate of 0.5 mV / s and an electrolyte of 0.1 mol / L sodium sulfate solution. Additionally, the parameters include copper sheet weight loss ≤ [value missing] from metal weight loss literature.

[0015] 0.5 mg / cm 2 Daily weight loss of steel sheet ≤1.2mg / cm³ 2 Daily data.

[0016] Furthermore, in step 3: the prediction of the metal compatibility probability value employs an attention enhancement mechanism, the specific steps of which are as follows:

[0017] The surface roughness Ra and charge density σ of the metal are individually thermally encoded. Key corrosion suppression structures are identified using an attention weight allocation formula for the graph attention network layer. This formula concatenates the metal feature vector and the ion feature vector and then performs Softmax normalization. The specific calculation formula is as follows:

[0018] Furthermore, in step 4, a corrosion barrier screening mechanism is introduced when generating a non-corrosive formulation. Specifically, the dissolution barrier ΔE of metal atoms is calculated through transition state search, and only ionic liquids with a dissolution barrier ΔE ≥ 1.5 eV are retained in the optimization process.

[0019] Furthermore, in step 5: the molecular dynamics Monte Carlo coupled simulation includes an electrochemical environment module: specifically, applying an electrode potential of -0.5V to 0.5V relative to the standard hydrogen electrode, while simultaneously monitoring the chloride ion concentration permeability, requiring the chloride ion concentration to not exceed 10. -14 mol / L.

[0020] Furthermore, in step 6: the output of the performance prediction report includes the Corrosion Protection Effectiveness Index (CPEI), which is calculated using the following formula:

[0021]

[0022] If the CPEI index is ≥ 0.08, it is marked as a non-corrosive certified formula.

[0023] In the formula I corr d represents the current density during the metal corrosion process; d represents the pit depth, which is the depth of the pit formed on the metal surface due to corrosion.

[0024] Compared with the prior art, the present invention has the following significant advantages:

[0025] 1) Achieve efficient screening of non-corrosive ionic liquid lubricants: Construct a graph neural network model to replace traditional trial-and-error experiments, improving the formulation generation efficiency by more than an order of magnitude.

[0026] 2) Strong structure-performance modeling capability: Based on GAT and multi-task prediction network, it models the complex nonlinear relationship between molecular structure and non-corrosive and lubricating properties, and the prediction accuracy is higher than that of traditional linear modeling or regression models.

[0027] 3) Significantly enhanced intelligent optimization capabilities: The introduction of simulated annealing and non-dominated sorting genetic algorithms, combined with user-input customized targets, enables intelligent recipe matching for non-corrosive environments.

[0028] 4) Highly interpretable results: Outputs the SMILES structure and predictive performance corresponding to the formulation, and combines a visual interface to show the formulation screening path and performance comparison, enhancing the feasibility of engineering implementation.

[0029] 5) It has good scalability: the method framework is general and can be adapted to more complex material screening tasks by extending descriptors, improving model structure or adding other lubrication performance indicators. Attached Figure Description

[0030] Figure 1 This is a flowchart of a custom production method for non-corrosive ionic liquid lubricants. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0032] A specific embodiment of the method for custom-producing a non-corrosive ionic liquid lubricant according to the present invention includes the following steps:

[0033] A custom preparation method for a non-corrosive ionic liquid lubricant, the method comprising:

[0034] 1) Construct a corrosion-lubrication multimodal database: Ionic liquid data that simultaneously meet both electrochemical corrosion parameters and lubrication performance parameters are filtered using Structured Query Language (SQL). Electrochemical corrosion parameters include corrosion potential E. corr Corrosion current density I ≥-0.2V relative to silver chloride electrode corr ≤0.1mA / cm 2 The material must be compatible with metals containing at least one of steel, copper, or aluminum; lubrication performance parameters include kinematic viscosity of 100-500 cP, coefficient of friction ≤0.08, and wear volume ≤0.5 mm. 3 The selected data were integrated according to the five-element classification model of "ionic structure-environmental parameters-metal surface properties-lubrication performance-corrosion parameters". Each data point was labeled with its source type (such as public database, experimental measurement, literature report, etc.) and confidence level (such as high, medium, low) to ensure the reliability and traceability of the data.

[0035] 2) Corrosion Suppression Feature Engineering: The initial dataset was preprocessed to supplement missing data, and the ionic liquid structure was encoded using the Simplified Molecular Linear Input Specification (SMILES) format. Various molecular descriptors were extracted using molecular modeling tools, including but not limited to basic molecular descriptors such as the number of hydrogen bond donors, the number of hydrogen bond acceptors, van der Waals surface area, and molecular polar surface area, as well as electrochemical descriptors such as the lowest unoccupied molecular orbital energy level of anions, predicted double-layer capacitance, and adsorption energy on metal surfaces. This standardized and structured the data, providing high-quality input for subsequent model training.

[0036] 3) Corrosion-Lubrication Co-prediction Model Training: A Graph Attention Network (GAT) model was constructed based on the PyTorch Geometric framework, with 8 attention heads configured in each layer. The molecular structure diagram encoded by SMILES was used as the input nodes, and the node features included atom types and molecular descriptors. The model adopted a three-branch output structure: the first branch predicted the lubrication performance parameter set composed of friction coefficient, wear rate, and viscosity; the second branch predicted the corrosion current density I. corr Corrosion potential E corrThe third branch outputs the metal compatibility probability values ​​for steel, copper, and aluminum. A dynamic loss weighting strategy is adopted, setting the weight coefficients for the corrosion prediction task to 1.3 times the base value to emphasize the importance of corrosion performance. During training, the AdamW optimizer and cosine annealing strategy are used to dynamically adjust the learning rate, and pre-trained parameters from the materials domain are loaded to initialize the network, improving the model's training effect and generalization ability. The training, validation, and test sets are divided in a 7:1.5:1.5 ratio to ensure a balanced distribution of samples from various working conditions.

[0037] 4) Targeted generation of non-corrosion formulations: Using a trained model and a simulated annealing algorithm, candidate ionic liquid formulations are searched in a structural database. The range of cation and anion categories is limited, excluding combinations that may induce corrosion, such as those containing chloride ions (Cl). - ), bromide ions (Br) - ), tetrachloroaluminate ions (AlCl4) - ), tetrachloroferrate ion (FeCl4) - The anions of ) and the acetate ion (CH3COO) - Ion pairs consisting of phosphate dihydrogen ions (H2PO4-) are preferred. Combinations of borate ester anions, bis(trifluoromethanesulfonyl)imide anions, and bis(oxalate-borate) anions are preferred to generate candidate formulations that meet the requirements of non-corrosiveness and lubrication performance.

[0038] 5) Metal-solution interface corrosion verification: Molecular dynamics Monte Carlo coupled simulations were performed on the selected candidate formulations to construct metal-solution interface models with iron (110) crystal plane, copper (111) crystal plane, and aluminum (100) crystal plane. The models were run for 10 nanoseconds (ns) under isothermal and isobaric ensembles, and the depth of corrosion pits and the thickness of the passivation film were statistically analyzed. The acceptance criterion was a passivation film thickness ≥ 5 angstroms. Furthermore, the density of corrosion pits is ≤0.1 per square nanometer (nm). 2 To verify the corrosion inhibition effect of the candidate formulation on metal surfaces.

[0039] 6) Multi-objective optimization output: The non-dominated sorting genetic algorithm NSGA-III is used for multi-objective optimization, with optimization objectives including minimizing the friction coefficient, minimizing the corrosion current density, and ensuring the viscosity approaches the target value. The output includes a simplified molecular linear input specification SMILES structure and performance prediction report for the Pareto optimal solution.

[0040] The present invention provides a specific solution as follows: In step 1, the electrochemical corrosion parameters are sourced from the International Society of Corrosion Engineers (ISC) standard corrosion test library, with test standards ISO 9223 and ISO 9224, as well as measured data from a three-electrode system. The scan rate is 0.5 mV / s, the electrolyte is 0.1 mol / L sodium sulfate solution, and the parameters also include copper sheet weight loss ≤ 0.5 mg / cm³ from metal weight loss rate literature. 2 Daily weight loss of steel sheet ≤1.2mg / cm³ 2 Daily data.

[0041] This invention provides a specific solution as follows: In step 3, the prediction of the metal compatibility probability value adopts an attention enhancement mechanism. Specifically, the metal surface roughness Ra and charge density σ are individually encoded; key corrosion suppression structures are identified through the attention weight allocation formula of the graph attention network layer. This formula concatenates the metal feature vector and the ion feature vector and then performs Softmax normalization. The specific formula is shown below:

[0042] e ij =LeakReLU(a T [Wh i ||Wh j ])

[0043]

[0044] hi and hj are the feature vectors of node i and node j, respectively.

[0045] W is a learnable weight matrix used to perform linear transformations on node features.

[0046] II represents the vector concatenation operation.

[0047] 'a' is a learnable attention vector used to calculate the attention score.

[0048] LeakyReLU is an activation function that allows a small number of negative values ​​to pass through, thus preventing neuron death.

[0049] Ni represents the set of neighboring nodes of a node.

[0050] αij represents the attention weight from node i to node j.

[0051] The present invention provides a specific solution as follows: In step 4, when generating a non-corrosive formulation, a corrosion barrier screening mechanism is introduced, specifically: the dissolution barrier ΔE of metal atoms is calculated by transition state search, and only ionic liquids with a dissolution barrier ΔE ≥ 1.5 eV are retained to enter the optimization stage.

[0052] This invention provides a specific solution as follows: In step 5: the molecular dynamics Monte Carlo coupling simulation includes an electrochemical environment module: specifically, an electrode potential of -0.5V to 0.5V relative to the standard hydrogen electrode is applied, while simultaneously monitoring the chloride ion concentration and permeability, requiring the chloride ion concentration to not exceed 10. -14 mol / L.

[0053] This invention provides a specific solution as follows: In step 6: the output of the performance prediction report includes the Corrosion Protection Effectiveness Index (CPEI), which is calculated using the following formula:

[0054]

[0055] In the formula I corr d represents the current density during the metal corrosion process; d represents the pit depth, which is the depth of the pit formed on the metal surface due to corrosion.

[0056] If the CPEI index is ≥ 0.08, it is marked as a non-corrosive certified formula.

[0057] The present invention will be further described in detail below with reference to embodiments, comparative examples and accompanying drawings.

[0058] Example 1:

[0059] To verify the effectiveness of the corrosion-lubrication multi-parameter data modeling and optimization method described in this invention, the following explanation is based on specific working conditions and data.

[0060] This embodiment focuses on the corrosion-lubrication performance of ionic liquid lubricants on copper and aluminum metal surfaces, and carries out structural feature extraction, dataset construction, multi-task modeling, and non-corrosive ion recommendation operations. Specifically, it includes the following steps:

[0061] 1. Raw data acquisition and preprocessing

[0062] Data on ionic liquids that simultaneously meet both electrochemical corrosion and lubrication performance parameters were filtered using Structured Query Language (SQL). A total of 820 sets of lubricant data were collected from public databases and experimental literature, including anionic and cationic SMILES structures, friction coefficients, wear volumes, and corrosion current densities (IL). corr ), corrosion potential (E) corr Information such as the maximum weight loss rate was obtained. The original structural data was parsed in SMILES format using RDKit, and a total of 68-dimensional molecular descriptors were extracted, including the number of hydrogen bond donors, polar surface area, and electron loading centers. At the same time, nine environmental variables, including metal type (Cu, Al, Fe), temperature, loading, and paired metal information, were used in conjunction with these parameters.

[0063] 2. Database structure standardization and hierarchical classification

[0064] According to the corrosion level (I) corr E corr A five-element classification label was constructed based on the maximum weight loss rate and lubrication performance level (friction coefficient μ, wear volume), defined as "low corrosion-high lubrication", "low corrosion-low lubrication", etc. All performance parameters were uniformly normalized using the Z-score standardization method to facilitate subsequent model training.

[0065] 3. Methods for establishing predictive models based on corrosion-lubrication synergy

[0066] A three-branch collaborative model is constructed using a message-passing neural network (MPNN). The first branch handles friction-related parameters, and the second branch handles electrochemical corrosion parameters (If). corr E corr The third branch inputs ion molecular diagram structure and metal-compatible features (combined with metal-anion affinity matrices). The three branches share a feature layer followed by a fully connected layer, outputting dual-task predictions. The model uses the ReLU activation function, has an output dimension of 128, and employs a weighted MSE loss function, with the corrosion prediction loss weight set to 1.3 times that of the lubrication prediction loss.

[0067] 4. Directed generation of non-corrosive ionic liquid formulations

[0068] After model training, a GNN inverse search method was used to traverse and filter the target molecular space. A regular filter containing common anions such as phosphate, tetrafluoroborate, and bis(trifluoromethanesulfonamide) was constructed to remove highly corrosive anionic components and preferentially retain low-corrosive ionic structures with large adsorption depths and uniform electron densities on copper and aluminum surfaces. Finally, 24 molecules were selected that met the requirement of "corrosion current density less than 0.5 μA / cm²". 2 Candidate structures with a friction coefficient less than 0.08.

[0069] 5. Metal-solution interface corrosion verification simulation

[0070] Lubricant-metal interface models were constructed on Cu(111) and Al(100) surfaces using representative structures (such as trialkyl phosphate cations + sulfonic acid anions). The lubricant adsorption process was simulated using LJ potential and electrostatic potential. Steady-state adsorption thickness and electronic coupling strength were calculated, and the tendency for corrosion site formation was observed. Simulation results show that this structural system exhibits no significant accumulation of electronic defects and a low corrosion tendency under conditions of 100℃ and 10MPa.

[0071] 6. Multi-objective optimization and recommendation output

[0072] The NSGA-III multi-objective optimization algorithm was employed, with friction coefficient, corrosion current density, and metal compatibility as objective functions, to optimize and generate the structural space, outputting the optimal non-dominated solution boundary (Pareto Front). The final recommended structure was output in SMILES format for reference in subsequent experiments or product design.

Claims

1. A method for custom-producing a non-corrosive ionic liquid lubricant, characterized in that, Includes the following steps: (1) Construction of a corrosion-lubrication multimodal database: Ionic liquid data that simultaneously meet the requirements of electrochemical corrosion parameters and lubrication performance parameters are filtered using the structured query language SQL. The electrochemical corrosion parameters include corrosion potential E. corr Corrosion current density I ≥-0.2V relative to silver chloride electrode corr ≤0.1mA / cm 2 The material must also be a metal compatibility label containing at least one of steel, copper, and aluminum; the lubrication performance parameters include kinematic viscosity of 100–500 cP, coefficient of friction <0.08, and wear volume ≤0.5 mm. 3 The selected data were integrated according to the five-element classification model of ionic structure, environmental parameters, metal surface properties, lubrication performance and corrosion parameters, and the data source type and confidence level were marked. (2) Corrosion inhibition feature engineering: The ionic structure is encoded using the simplified molecular linear input canonical SMILES format; basic molecular descriptors are extracted, including the number of hydrogen bond donors and van der Waals surface area; electrochemical descriptors are extracted, including the lowest unoccupied molecular orbital energy level of anions, the predicted value of double layer capacitance, and the adsorption energy of metal surface. (3) Training of the corrosion-lubrication synergistic prediction model: Constructing a three-branch graph attention network model: The first branch predicts the lubrication performance parameter set composed of friction coefficient, wear rate and viscosity; the second branch predicts the corrosion current density I. corr Corrosion potential E corr The third branch outputs the metal compatibility probability values ​​for steel, copper, and aluminum; a dynamic loss weighting strategy is adopted: the weight coefficient for corrosion prediction is 1.3 times the base value; (4) Targeted generation of non-corrosive formulations: The simulated annealing algorithm is used to search the ion combination space and select combinations of borate ester anions, bis(trifluoromethanesulfonyl)imide anions, and bis(oxalateborate) anions; (5) Metal-solution interface corrosion verification: Molecular dynamics Monte Carlo coupled simulation was performed on the candidate formulations: metal-solution interface models with iron 110 crystal plane, copper 111 crystal plane, and aluminum 100 crystal plane were constructed; a ten-nanosecond trajectory was run under isothermal and isobaric ensemble, and the depth of corrosion pits and the thickness of passivation film were statistically analyzed; the acceptance criterion was the thickness of the passivation film. And the corrosion pit density is ≤0.1nm 2 ; (6) Multi-objective optimization output: The non-dominated sorting genetic algorithm NSGA-III is used for multi-objective optimization: the optimization objectives include minimizing the friction coefficient, minimizing the corrosion current density, and making the viscosity close to the target value; the output is a simplified molecular linear input specification SMILES structure and performance prediction report of the Pareto optimal solution.

2. The method according to claim 1, characterized in that, In step 1, the electrochemical corrosion parameters are sourced from the International Society of Corrosion Engineers (ICSE) standard corrosion test library, with test standards ISO 9223 and ISO 9224, as well as measured data from a three-electrode system. The scan rate is 0.5 mV / s, the electrolyte is 0.1 mol / L sodium sulfate solution, and the parameters also include copper sheet weight loss of ≤0.5 mg / cm³ from metal weight loss literature. 2 Daily weight loss of steel sheet ≤1.2mg / cm³ 2 Daily data.

3. The method according to claim 1, characterized in that, In step 3: the prediction of the metal compatibility probability value adopts the attention enhancement mechanism. The specific steps are as follows: the surface roughness Ra and charge density σ of the metal are individually encoded; the key corrosion inhibition structure is identified by the attention weight allocation formula of the graph attention network layer. This formula performs Softmax normalization after concatenating the metal feature vector and the ion feature vector.

4. The method according to claim 1, characterized in that, In step 4, a corrosion barrier screening mechanism is introduced when generating a non-corrosive formulation. Specifically, the dissolution barrier ΔE of metal atoms is calculated through transition state search, and only ionic liquids with a dissolution barrier ΔE ≥ 1.5 eV are retained in the optimization process.

5. The method according to claim 1, characterized in that, In step 5: the molecular dynamics Monte Carlo coupled simulation includes an electrochemical environment module: specifically, an electrode potential of -0.5V to 0.5V relative to the standard hydrogen electrode is applied, while simultaneously monitoring the chloride ion concentration permeability, requiring the chloride ion concentration to not exceed 10. -14 mol / L.

6. The method according to claim 1, characterized in that, In step 6: the output of the performance prediction report includes the Corrosion Protection Effectiveness Index (CPEI), which is calculated using the following formula: In the formula I corr d represents the current density during the metal corrosion process; d represents the pit depth, which is the depth of the pit formed on the metal surface due to corrosion. If the CPEI index is ≥ 0.08, it is marked as a non-corrosive certified formula.

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