Method and system for screening co2 capture molecular sieve adsorbents based on molecular simulations

Abstract

The present application belongs to the technical field of computer-aided gas adsorption material screening, and particularly relates to a CO2 capture molecular sieve adsorbent screening method and system based on molecular simulation. The method comprises the following steps: constructing and verifying a molecular model library related to a target molecular sieve, determining a force field-charge parameter combination by comparing with experimental data; performing simulation screening based on the verified model, comprehensively evaluating the influence of operating conditions and molecular sieve structure on CO2 adsorption performance, and screening an advantage framework structure; optimizing the silicon-aluminum ratio parameter for the selected advantage framework structure, determining the optimal silicon-aluminum ratio; performing cation modification design based on the optimal silicon-aluminum ratio, and constructing a cation-modified molecular sieve model; and comprehensively comparing the performance of each modification scheme to screen the optimal molecular sieve adsorbent. The present application greatly reduces the number of experiments and material costs, improves the screening efficiency, and realizes rapid, low-cost and high-precision adsorbent screening.

Description

Technical Field

[0001] This invention belongs to the field of computer-aided gas adsorption material screening technology, and particularly relates to a method and system for screening CO2 capture molecular sieve adsorbents based on molecular simulation. Background Technology

[0002] With the increasingly severe global climate change problem, CO2 capture and storage technology has become one of the key approaches to achieving carbon emission reduction targets. Among the many CO2 capture technologies, physical adsorption has attracted widespread attention due to its advantages such as simple operation, low energy consumption, and recyclable materials. Zeolite molecular sieves, with their regular pore structure and tunable surface properties, have become one of the important materials for CO2 adsorption.

[0003] Currently, the methods for screening CO2 adsorption molecular sieve materials mainly rely on experimental approaches. Researchers need to prepare molecular sieves with different silica-to-alumina ratios and different cation modifications, and then test their adsorption performance under different temperature, pressure, and humidity conditions. The entire process is time-consuming, material-intensive, and costly. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this invention provides a method and system for screening CO2 capture molecular sieve adsorbents based on molecular simulation. By replacing part of the experiment with computer simulation, targeted improvements have been made in charge calculation methods, simulation condition settings, and performance evaluation indicators, which significantly reduces the number of experiments and material costs, improves screening efficiency, and achieves rapid, low-cost, and high-precision adsorbent screening.

[0005] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a method for screening molecular sieve adsorbents for CO2 capture based on molecular simulation.

[0006] A molecular simulation-based method for screening molecular sieve adsorbents for CO2 capture includes the following steps: Construct and validate a molecular model library related to the target molecular sieve, and determine the combination of force field-charge parameters by comparing with experimental data; Based on the validated model, simulation screening was conducted to comprehensively evaluate the effects of operating conditions and molecular sieve structure on CO2 adsorption performance, and to screen out the superior framework structure. For the selected advantageous framework structure, the silicon-to-aluminum ratio parameter is optimized to determine the optimal silicon-to-aluminum ratio; Based on the optimal silicon-to-aluminum ratio, a cationic modification design was carried out to construct a cationic modified molecular sieve model; By comprehensively comparing the performance of various modification schemes, the optimal molecular sieve adsorbent was selected.

[0007] As a preferred technical solution, a molecular model library related to the target molecular sieve is constructed and validated, and the combination of force field-charge parameters is determined by comparing it with experimental data, specifically including: Based on crystallographic databases or standard molecular structure parameters, initial three-dimensional structural models of the target molecular sieve, CO2 molecule, N2 molecule, H2O molecule, and other background gases are constructed. The target molecular sieve includes one or more of the FAU, BEA, or MFI type topologies. Under the selected force field, the geometry of all constructed models is optimized to eliminate unreasonable bond lengths and bond angles, and the stable configuration with the lowest energy is obtained. Electronic structure calculations are performed on gas molecules to obtain atomic charges and accurately describe the electrostatic potential distribution of molecules. For molecular sieve frameworks, charge balance method is used to distribute charges. Using the Monte Carlo method, the adsorption isotherm of CO2 on a standard molecular sieve was simulated under specific temperature and pressure. The simulation results were compared with publicly published experimental data. By fine-tuning the atomic charge of the gas molecules, the error range between the simulated adsorption amount and the experimental value, as well as the error range of key pressure points, were made to meet the preset requirements. A set of high-precision force field-charge parameter combinations that have been verified by experiments was determined.

[0008] As a preferred technical solution, the other background gases are a mixture of CO2 / N2 / O2 / H2O, wherein the volume fraction of CO2 is 1% to 30%, the volume fraction of N2 is 50% to 95%, the volume fraction of O2 is 0 to 10%, and the relative humidity of H2O is 0 to 90% RH.

[0009] As a preferred technical solution, the force field selected in the geometric structure optimization is one of the Universal force field, Dreiding force field, COMPASS force field, or COMPASS II force field.

[0010] As a preferred technical solution, the electronic structure calculation adopts density functional theory, with GGA and PBE as the functionals, DNP as the basis set, and Mulliken as the population analysis, in order to obtain high-precision atomic charge distribution and accurately describe the strong quadrupole moment characteristics of CO2 molecules.

[0011] As a preferred technical solution, the standard molecular sieve is a 13X molecular sieve or other FAU type molecular sieves.

[0012] As a preferred technical solution, the atomic charge of the gas molecules is fine-tuned, with an adjustment range of ±0.05 to ±0.5 electron charge.

[0013] As a preferred technical solution, simulation screening is conducted based on the validated model to comprehensively evaluate the impact of operating conditions and molecular sieve structure on CO2 adsorption performance, and to screen out advantageous framework structures, specifically including: With the molecular sieve structure fixed, the operating conditions, including temperature, pressure, relative humidity, and gas background, were varied, especially for the CO2 / N2 / O2 / H2O mixed gas conditions under actual flue gas conditions, to simulate and calculate the adsorption capacity and adsorption selectivity of CO2. Under fixed operating conditions, different molecular sieve topology models were used to repeat adsorption simulations. The adsorption performance of different framework structures was compared, and the superior framework structure was screened out.

[0014] As a preferred technical solution, the temperature range is 0 to 200°C, the pressure range is 0.01 to 200 kPa CO2 partial pressure, and the relative humidity range is 0 to 100% RH.

[0015] As a preferred technical solution, for the selected advantageous framework structure, the silicon-to-aluminum ratio parameter is optimized to determine the optimal silicon-to-aluminum ratio, specifically including: A series of molecular sieve models with different silica-to-alumina ratios were constructed on the selected dominant framework structure, and the modeling process followed Lowenstein's rule. Under defined operating conditions, the CO2 adsorption performance of a series of molecular sieve models with different silicon-to-aluminum ratios was simulated and calculated. The optimal silicon-to-aluminum ratio was determined by comparing the adsorption capacity, selectivity, and isothermal heat of adsorption.

[0016] As a preferred technical solution, the silicon-to-aluminum ratio ranges from 0.5 to 10.0.

[0017] As a preferred technical solution, cationic modification design is carried out based on the optimal silicon-to-aluminum ratio to construct a cationic modified molecular sieve model, specifically including: Cations were introduced and the structure was re-optimized. Based on the determined optimal silica-alumina ratio molecular sieve model, metal cations were used to replace the protons or sodium ions that compensated for the charge in the framework, and cation-modified molecular sieve models were constructed. Geometric optimization and charge balance calculations were performed again for each new model. The metal cation includes one or more of alkali metals, alkaline earth metals, noble metals, or rare earth metals; the alkali metal includes Li. + Na + K + One or more of the following; the alkaline earth metals include Mg 2+ Ca 2+ One or more of the following; the precious metal includes Ag + Cu 2+ One or more of them.

[0018] As a preferred technical solution, the performance of various modification schemes was comprehensively compared, and the optimal molecular sieve adsorbent was selected, specifically including: Under target operating conditions, CO2 adsorption simulations were performed on the constructed cation-modified models. Their saturated adsorption capacity, CO2 / N2 selectivity, isothermal heat of adsorption, and resistance to water vapor were compared. Finally, the molecular sieve adsorbent with the best overall performance, a specific silica-alumina ratio, and specific cation modification was selected.

[0019] As a preferred technical solution, Ca-FAU with a silicon-to-aluminum ratio of 2.5 is the molecular sieve adsorbent with the best overall performance.

[0020] A second aspect of the present invention provides a molecular sieve adsorbent screening system for CO2 capture based on molecular simulation.

[0021] A molecular simulation-based CO2 capture molecular sieve adsorbent screening system includes: The molecular model library construction module is configured to: construct and validate a molecular model library related to the target molecular sieve, and determine the combination of force field-charge parameters by comparing with experimental data; The dominant framework screening module is configured to: perform simulation screening based on the validated model, comprehensively evaluate the influence of operating conditions and molecular sieve structure on CO2 adsorption performance, and screen out dominant framework structures. The optimal silicon-to-aluminum ratio screening module is configured to: optimize the silicon-to-aluminum ratio parameters for the selected advantageous framework structure and determine the optimal silicon-to-aluminum ratio. The modified molecular sieve construction module is configured to: design cationic modification based on the optimal silica-alumina ratio to construct a cationic modified molecular sieve model; The optimal molecular sieve determination module is configured to comprehensively compare the performance of various modification schemes and screen out the optimal molecular sieve adsorbent.

[0022] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps in the molecular simulation-based CO2 capture molecular sieve adsorbent screening method described in the first aspect of the present invention.

[0023] The fourth aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the molecular simulation-based CO2 capture molecular sieve adsorbent screening method as described in the first aspect of the present invention.

[0024] The above one or more technical solutions have the following beneficial effects: This invention provides a method and system for screening molecular sieve adsorbents for CO2 capture based on molecular simulation. It achieves adsorbent screening through molecular simulation, and makes targeted improvements in charge determination methods, simulation condition settings, and performance evaluation indicators. These technical details are crucial for achieving high-precision prediction of CO2 adsorption performance. In particular, this invention clarifies the DFT calculation parameters, charge adjustment range, verification error standards, and key evaluation indicators such as CO2 / N2 selectivity, isothermal heat of adsorption, and resistance to water vapor. This enables more accurate prediction of the CO2 capture performance of molecular sieves under flue gas conditions, providing a more reliable theoretical basis for the rational design of CO2 capture materials.

[0025] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0027] Figure 1 This is the CO2 molecular structure model of Embodiment 1 of the present invention.

[0028] Figure 2 This is the structural model of the FAU-type molecular sieve in Embodiment 1 of the present invention.

[0029] Figure 3 This is a flowchart illustrating the method in an embodiment of the present invention. Detailed Implementation

[0030] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0032] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0033] I. Terminology Explanation To facilitate understanding of the technical solution of this invention, the key terms involved in this invention will first be defined and explained.

[0034] Molecular simulation refers to a method that uses computer-aided techniques, based on the principles of classical or quantum mechanics, to simulate and calculate the structure, properties, and behavior of molecular systems. The molecular simulation in this invention mainly includes three levels: molecular mechanics optimization, density functional theory calculation, and Monte Carlo adsorption simulation.

[0035] The Monte Carlo method is a statistical simulation method based on random sampling. In adsorption simulation, the Monte Carlo method randomly generates the positions and conformations of gas molecules in the pores of the adsorbent, and accepts or rejects these conformations according to energy criteria, ultimately obtaining the equilibrium adsorption amount of the system. The Metropolis Monte Carlo method used in this invention is its classic implementation.

[0036] Force field: A force field is a set of potential energy functions that describe the interactions between atoms in a molecular system. Force fields include bonding interactions (bond stretching, bond angle bending, dihedral torsion) and non-bonding interactions (van der Waals forces, electrostatic interactions). The Universal force field and COMPASS II force field used in this invention are parameterized general force fields that can accurately describe the interactions between gas molecules and the molecular sieve framework.

[0037] Density functional theory (DFT) is a first-principles calculation method based on quantum mechanics that obtains the electron density distribution and energy of a system by solving the Kohn-Sham equations. This invention uses the DFT method to calculate the electronic structure and atomic charges of gas molecules to accurately describe the electrostatic potential distribution of the molecules.

[0038] Mulliken charge is a population analysis method that distributes the total charge of a molecule across its individual atoms. It is obtained by decomposing the electron density of molecular orbitals according to atomic orbital basis functions. In this invention, Mulliken charge is used to describe the partial charge of each atom in a gas molecule and is an important parameter for calculating electrostatic interactions.

[0039] Hirshfeld charge: This is another method of atomic charge distribution, which allocates the electron density of a molecule according to the concept of "atoms within a molecule". Compared to Mulliken charge, Hirshfeld charge is less dependent on the basis set and is more reasonable in some cases.

[0040] The silica-alumina ratio (SAR) refers to the molar ratio of silicon-oxygen tetrahedra to aluminum-oxygen tetrahedra in the molecular sieve framework, usually expressed as the SiO2 / Al2O3 molar ratio. The silica-alumina ratio is a key structural parameter determining the acidity, polarity, and adsorption performance of a molecular sieve. A lower silica-alumina ratio indicates a higher aluminum content in the molecular sieve framework, a greater negative charge density, a greater need for cations to achieve charge balance, and a stronger adsorption capacity for polar molecules.

[0041] Lowenstein's rule, one of the fundamental rules of zeolite molecular sieve structural chemistry, states that two aluminum-oxygen tetrahedra cannot be directly connected by an oxygen bridge; that is, Al-O-Al bonds cannot exist. This rule limits the maximum aluminum content in molecular sieves, ensuring that the silica-alumina ratio cannot be lower than 1.0. Lowenstein's rule must be strictly followed when constructing molecular sieve models with different silica-alumina ratios.

[0042] Adsorption isotherms are curves that show the relationship between the amount of adsorbate adsorbed by an adsorbent and the partial pressure or concentration of the adsorbate in the gas phase at a constant temperature. Adsorption isotherms are fundamental data characterizing adsorbent performance, from which important parameters such as saturated adsorption capacity and adsorption affinity can be obtained.

[0043] Isothermal heat of adsorption refers to the heat released per mole of adsorbate adsorbed from the gas phase to the surface of the adsorbent at a constant temperature. It reflects the strength of the interaction between the adsorbate and the adsorbent and is an important indicator for evaluating the energy consumption of adsorbent regeneration. A higher isothermal heat of adsorption indicates stronger adsorption, but also requires more energy for regeneration.

[0044] CO2 / N2 selectivity refers to the ratio of CO2 adsorbed by the adsorbent to N2 adsorbed in a gas mixture. In practical applications such as flue gas analysis, CO2 / N2 selectivity is a core indicator for evaluating the separation performance of adsorbents. Higher selectivity indicates a stronger selective adsorption capacity for CO2 by the adsorbent and higher separation efficiency.

[0045] Cation modification refers to replacing the original compensating cations (usually H+ or Na+) in the molecular sieve framework with specific metal cations (such as Li+, Na+, K+, Mg2+, Ca2+, etc.) through methods such as ion exchange to regulate the adsorption performance of the molecular sieve. Different cations have different radii, charge densities, and polarization abilities, resulting in varying strengths of electrostatic interactions and induction effects on CO2 molecules, thus affecting the adsorption capacity and selectivity of CO2.

[0046] FAU-type molecular sieves are zeolite molecular sieves with a three-dimensional twelve-membered ring pore system, and their topological designation is FAU (Faujasite). FAU-type molecular sieves have a pore size of approximately 7.4 Å and a supercage structure, making them one of the most widely studied molecular sieves in the field of CO2 adsorption. Common FAU-type molecular sieves include X-type molecular sieves (silicon-to-aluminum ratio 1.0-1.5) and Y-type molecular sieves (silicon-to-aluminum ratio greater than 1.5).

[0047] BEA-type molecular sieve: This is a zeolite molecular sieve with a three-dimensional twelve-membered ring pore system, and its topological designation is BEA (Beta). The pore system of BEA-type molecular sieve is relatively complex, with two sets of mutually perpendicular straight channels, with pore sizes of approximately 6.6 × 6.7 Å and 5.6 × 5.6 Å.

[0048] MFI-type molecular sieves: These are zeolite molecular sieves with a three-dimensional ten-membered ring pore system, and their topological designation is MFI (Mordenite Framework Inverted). A typical example of an MFI-type molecular sieve is the ZSM-5 molecular sieve, whose pore system includes straight pores (5.3 × 5.6 Å) and zigzag pores (5.1 × 5.5 Å), with smaller pore sizes than FAU-type molecular sieves.

[0049] II. Technical Background and Problem Definition

[0050] In existing technologies, the screening of CO2 adsorption molecular sieve materials mainly relies on experimental methods. Researchers need to prepare molecular sieves with different silica-to-alumina ratios and different cation modifications, and then test their adsorption performance under different temperature, pressure, and humidity conditions. The entire process requires repeated material preparation, experimental system construction, and adjustment of operating parameters.

[0051] Existing experimental screening methods suffer from the following technical drawbacks: First, each change in conditions (such as temperature, pressure, humidity, or gas composition) necessitates repeating the experiment, resulting in a large workload, long cycle time, and high cost. Second, the material synthesis and modification processes are complex, time-consuming, and resource-intensive, and experimental errors are difficult to completely avoid. Third, they cannot quickly predict the adsorption performance of new materials or under new conditions, leading to low screening efficiency.

[0052] An existing technology provides a method for screening VOCs molecular sieve adsorbents based on molecular simulation. This method uses Material Studio software to construct a molecular sieve model, optimizes its structure using quantum chemical methods, and then obtains the optimal molecular sieve adsorbent structure through Monte Carlo simulation. However, this method is mainly aimed at the field of VOCs adsorption, and its model validation and parameter optimization processes are not designed for the specific characteristics of CO2 capture.

[0053] Although molecular simulation methods have been applied to the screening of adsorbents for gases such as VOCs, existing methods have not been optimized for the technical characteristics of CO2 capture. In particular, there is a lack of systematic simulation and evaluation methods for the selective adsorption, resistance to water vapor interference, and adsorption heat characteristics of CO2 / N2 mixed gases under actual working conditions such as flue gas. This makes it difficult to provide effective guidance for the rational design of molecular sieve adsorbents for CO2 capture.

[0054] III. Overview of Technical Implementation Approach

[0055] The core concept of this invention is to construct an accurate and efficient "computational experimental platform" that uses molecular simulation to replace or guide a large number of repetitive and costly physical experiments, thereby enabling systematic and rapid screening of molecular sieve adsorbent structures (silicon-to-aluminum ratio, cation type) and operating conditions (temperature, pressure, humidity, gas composition).

[0056] The technical implementation path of the method of this invention consists of five key steps: The first step is to construct and validate an accurate molecular model library, and determine a high-precision combination of force field-charge parameters by comparing it with experimental data. The second step is to conduct systematic simulation screening based on the validated model to comprehensively evaluate the impact of operating conditions and molecular sieve structure on adsorption performance. The third step is to systematically optimize the silicon-to-aluminum ratio parameters for the selected advantageous framework structure. The fourth step is to design cationic modification based on the optimal silicon-to-aluminum ratio; The fifth step is to comprehensively compare the performance of each modification scheme and select the optimal adsorbent.

[0057] Compared with the prior art, the distinguishing technical features of the present invention are mainly reflected in three aspects: First, a model validation method for CO2 capture was established. By fine-tuning the atomic charges of gas molecules, the simulation results were made to be in high agreement with the experimental data, thus ensuring the accuracy of the prediction. Second, for actual working conditions such as flue gas, the selective adsorption, water vapor resistance and isothermal adsorption heat of CO2 / N2 mixed gas were systematically evaluated, and a complete performance evaluation system was established. Third, by incorporating the optimization of the silicon-to-aluminum ratio and the design of cation modification into the systematic screening process, the rational design of adsorbents was achieved.

[0058] IV. Example Environment Description.

[0059] Several embodiments of the present invention can be implemented in a computer-aided materials design platform environment. This environment includes the following components: Users: Materials researchers, responsible for setting screening targets, inputting technical parameters, analyzing simulation results, and making decisions.

[0060] Computing equipment: High-performance computing workstations or server clusters, configured with multi-core processors (such as Intel Xeon or AMD EPYC series), large-capacity memory (128GB or more), and high-speed storage systems. The computing equipment runs Linux or Windows operating systems to provide computational resources for the molecular simulation software.

[0061] Material Studio software platform: A molecular simulation software suite developed by BioVia, it is the core software tool of this invention. Material Studio provides complete molecular modeling, structure optimization, property calculation, and adsorption simulation capabilities.

[0062] The Visualizer module is a visual modeling module in Material Studio used to build three-dimensional structural models of molecular sieves and gas molecules, and to view, edit, and analyze these models.

[0063] Forcite module: The molecular mechanics module in Material Studio, which performs geometric optimization, energy calculation and molecular dynamics simulation of molecular systems based on classical force fields.

[0064] DMol3 module: Material Studio's density functional theory calculation module, used for quantum chemical calculations to obtain the electronic structure, atomic charge, and electronic properties of molecules.

[0065] Sorption module: Material Studio's adsorption simulation module, which simulates the adsorption behavior of gases in porous materials based on the Monte Carlo method, and calculates performance parameters such as adsorption isotherms, adsorption heat, and selectivity.

[0066] The silicon-to-aluminum ratio adjustment module is used to adjust the ratio of silicon and aluminum atoms in the molecular sieve model, generating molecular sieve structure models with different silicon-to-aluminum ratios. This module ensures that the generated models satisfy Lowenstein's rule.

[0067] In practical applications, users interact with the Material Studio software platform through a graphical user interface to set simulation parameters and start calculation tasks. The computing device performs molecular simulation calculations and generates result data. Users analyze the result data and select the optimal adsorbent scheme based on performance evaluation indicators.

[0068] Example 1 The following describes specific embodiments and appendices. Figure 3 This paper details the technical implementation process of the CO2 capture molecular sieve adsorbent screening method based on molecular simulation of the present invention.

[0069] 1: Establish and validate an accurate molecular model for CO2 adsorption simulation.

[0070] This step aims to establish a model of CO2, N2, H2O molecules and FAU-type molecular sieves that can accurately reflect the actual adsorption behavior, and to verify its accuracy by comparing it with experimental data. It includes four sub-steps: S1.1 to S1.4.

[0071] Step S101: Initial model construction.

[0072] The purpose of this step is to construct an initial three-dimensional structural model of the molecular sieve and gas molecules, laying the foundation for subsequent optimization and simulation.

[0073] Building a FAU-type molecular sieve model: Launch Material Studio software and open the Visualizer module. Select the FAU topology from the software's built-in zeolite structure database, or download the crystallographic information file (CIF format) of the FAU-type molecular sieve from the International Zeolite Association (IZA) database. After importing the CIF file, the software automatically generates a three-dimensional crystal structure model of the FAU-type molecular sieve. (Details follow...) Figure 2 As shown.

[0074] The unit cell parameters of the FAU-type molecular sieve are: cubic crystal system, space group Fd-3m, unit cell constant a=b=c≈24.85Å, α=β=γ=90°. The unit cell contains 192 T atoms (T represents the central Si or Al tetrahedral atom) and 384 O atoms. In this embodiment, the initial FAU model has a silicon-to-aluminum ratio of 1.5, i.e., a SiO2 / Al2O3 molar ratio of 1.5. Based on this silicon-to-aluminum ratio, the number of Si atoms in the unit cell is 115, and the number of Al atoms is 77.

[0075] To meet the electroneutrality requirement, compensating cations need to be added to the molecular sieve framework. Since the aluminum-oxygen tetrahedron carries a single negative charge, 77 monovalent cations are needed for charge balance. In this embodiment, Na+ is chosen as the compensating cation. Using the "Add Atoms" function of the Visualizer module, 77 Na+ ions can be manually added to the cation-preferred sites such as the supercages and sodalite cages in the molecular sieve, or the "Build Layers" function can be used to automatically distribute Na+ ions at appropriate locations.

[0076] To perform simulations under periodic boundary conditions, a supercell of appropriate size needs to be constructed. Considering the balance between computational efficiency and model representativeness, this embodiment constructs a 2×2×2 FAU supercell, containing 8 primitive cells, totaling 1536 T atoms, 3072 O atoms, and 616 Na+ ions. The supercell has an edge length of approximately 49.7 Å, which is sufficient to accommodate a sufficient number of gas molecules for adsorption simulation.

[0077] CO2 Molecular Model Construction: In the Visualizer module, use the "Sketch" tool or the "Build Molecule" function under the "Build" menu to construct the CO2 molecule. The CO2 molecule has a linear structure and the chemical formula O=C=O. Based on experimentally determined molecular geometry parameters, the CO bond length is set to 1.162 Å, and the OCO bond angle is set to 180°. After construction, save the CO2 molecular model as a separate structure file, such as... Figure 1 As shown.

[0078] N2 Molecular Model Construction: The N2 molecule was constructed using the same method. The N2 molecule has a linear structure and the chemical formula N≡N. Based on experimental data, the N-N bond length was set to 1.098 Å. After construction, it was saved as a separate structure file.

[0079] H2O Molecular Model Construction: A molecular model of H2O was constructed. The H2O molecule has a curved structure and the chemical formula is HOH. Based on experimental data, the OH bond length was set to 0.957 Å, and the HOH bond angle to 104.5°. The completed model was saved as a separate structure file.

[0080] Construction of other background gas molecule models: To simulate real flue gas conditions, an O2 molecule model also needs to be constructed. The O2 molecule has a linear structure, with the chemical formula O=O and an O0 bond length of 1.208 Å. After construction, it should be saved as a separate structure file.

[0081] At this point, the initial three-dimensional structural models of FAU-type molecular sieves, CO2 molecules, N2 molecules, H2O molecules, and O2 molecules have been completed. The bond length and bond angle parameters of these models are based on standard molecular structure data, but energy optimization has not yet been performed, and there may be unreasonable geometric configurations, which need to be optimized in the next step.

[0082] Step S102: Geometric optimization.

[0083] The purpose of this step is to optimize the geometry of all models constructed in step S101, eliminate unreasonable bond lengths and bond angles, and obtain the stable configuration with the lowest energy.

[0084] Force Field Selection: Geometric structure optimization based on molecular mechanics methods requires the selection of a suitable force field. This embodiment selects the Universal Force Field. The Universal Force Field is a general force field whose parameters cover most elements in the periodic table, and can effectively describe the structure and properties of inorganic materials and organic molecules. The potential energy function of the Universal Force Field includes terms related to bond stretching, bond bending, dihedral torsion, van der Waals interactions, and electrostatic interactions.

[0085] Optimize task settings: Open the Forcite module and select the "Geometry Optimization" task type. In the "Setup" tab, make the following settings: Forcefield: Select Universal.

[0086] Charges: For gas molecules, the default force field charge is used for now, and a more accurate charge will be obtained through DFT calculation later; for molecular sieves, the charge is automatically allocated using the "Charge equilibration" method.

[0087] Summation methods: For van der Waals interactions, the atm-based method was used with a cutoff radius of 12.5 Å; for electrostatic interactions, the Ewald method was used with an accuracy of 1 × 10⁻⁵ kcal / mol. The Ewald method is a standard method for handling long-range electrostatic interactions in periodic systems and can accurately calculate electrostatic energies in crystal structures.

[0088] Algorithm: Select a Smart algorithm. Smart algorithms automatically select the most suitable optimization algorithm (such as Steepest descent, Conjugate gradient, or Newton-Raphson method) based on the characteristics of the system.

[0089] Energy calculation accuracy (Quality): Select Fine level. Fine level corresponds to stricter convergence criteria: energy change less than 2×10-5 kcal / mol, maximum force less than 0.001 kcal / mol / Å, and maximum displacement less than 1×10-5 Å.

[0090] Maximum iterations: Set to 5000 steps to ensure the optimization can converge sufficiently.

[0091] FAU zeolite optimization: The constructed FAU supercell model was imported into the Forcite module, and the above optimization settings were applied to initiate the geometry optimization task. During the optimization process, the software iteratively adjusted the atomic positions to gradually reduce the total energy of the system. After approximately 2000 iterations, the optimization converged. In the optimized FAU zeolite structure, the Si-O bond length is approximately 1.61 Å, the Al-O bond length is approximately 1.75 Å, and the TOT bond angle (T represents Si or Al) is in the range of 140° to 180°, consistent with the experimentally determined FAU structural parameters. The cell parameters were slightly adjusted, a=b=c≈24.92 Å, with a deviation from the experimental values ​​of less than 1%.

[0092] Gas molecule optimization: Geometric optimization was performed sequentially on the molecular models of CO2, N2, H2O, and O2. Due to the simple structure of gas molecules, the optimization converged quickly. The optimized molecular geometric parameters are as follows: CO2 molecule: CO bond length 1.163 Å, OCO bond angle 180.0°, basically consistent with experimental values.

[0093] N2 molecule: NN bond length 1.098 Å, which is completely consistent with the experimental value.

[0094] The H2O molecule has an OH bond length of 0.957 Å and a HOH bond angle of 104.6°, which is basically consistent with the experimental values.

[0095] O2 molecule: OO bond length is 1.208 Å, consistent with experimental values.

[0096] After geometric optimization, all models achieved the stable configuration with the lowest energy, laying a reliable structural foundation for subsequent charge calculations and adsorption simulations.

[0097] Step S103: Charge distribution calculation and optimization.

[0098] The purpose of this step is to obtain the precise atomic charges of gas molecules through quantum chemical calculations, in order to accurately describe the electrostatic potential distribution of the molecules. Electrostatic interactions are an important component of the interactions between gas molecules and the molecular sieve framework, and accurate atomic charges are key to improving the accuracy of adsorption simulations.

[0099] Density functional theory calculation settings: Open the DMol3 module and select the "Geometry Optimization" task type (although named geometry optimization, its main purpose is to obtain electronic structure information). In the "Setup" tab, make the following settings: Functional: Select the PBE functional under the GGA (Generalized Gradient Approximation) category. The PBE functional is one of the most widely used GGA functionals, offering a good balance between accuracy and computational efficiency in describing molecular structures and properties.

[0100] Basis set: Select the DNP (Double Numerical plus Polarization) basis set. The DNP basis set is a numerical atomic orbital basis set, equivalent to the 6-31G(d,p) level in the Gaussian group, which can provide high computational accuracy.

[0101] Population analysis: Select the Mulliken method. Mulliken population analysis is the most commonly used method for calculating atomic charges. It obtains the net charge of each atom by decomposing the electron density of molecular orbitals according to atomic orbital basis functions.

[0102] SCF tolerance: set to 1×10⁻⁶ Ha to ensure full convergence of the self-consistent field iteration.

[0103] Integration accuracy: Select Fine level.

[0104] CO2 molecule charge calculation: Import the optimized CO2 molecule model from step S102 into the DMol3 module, apply the above calculation settings, and start the DFT calculation. After the calculation is complete, locate the Mulliken atomic charges section in the output file and read the Mulliken charge for each atom.

[0105] The calculation results show that the Mulliken charge distribution of the CO2 molecule is as follows: the C atom has a charge of +0.576e, and both O atoms have a charge of -0.288e (e is the elementary charge). This charge distribution reflects the polarity of the C=O double bond in the CO2 molecule: the carbon atom loses electrons and becomes positively charged, while the oxygen atom gains electrons and becomes negatively charged. The total charge of the CO2 molecule is 0, satisfying the requirement of electroneutrality.

[0106] Charge calculation for the N2 molecule: The same DFT calculation was performed on the N2 molecule. Since the N2 molecule is a homonuclear diatomic molecule, the two N atoms have identical electronegativity and symmetrical charge distribution. The calculation results show that the Mulliken charge of both N atoms is close to 0 (approximately ±0.001e, negligible), which is consistent with the nonpolar characteristic of the N2 molecule.

[0107] Charge calculation of H2O molecule: DFT calculation was performed on the H2O molecule. The results show that the O atom has a charge of -0.672e, and the two H atoms each have a charge of +0.336e. This charge distribution reflects the strong polarity of the OH bond in the H2O molecule.

[0108] Charge calculation for O2 molecules: DFT calculations are performed on the O2 molecule. Similar to the N2 molecule, the O2 molecule is a homonuclear diatomic molecule, with the Mulliken charge of both O atoms being close to 0.

[0109] Molecular sieve framework charge distribution: Due to the large number of atoms in the molecular sieve framework (a supercell contains thousands of atoms), direct DFT calculations are computationally too costly. Therefore, this embodiment uses the charge equilibration (QEq) method to automatically distribute the charge of the framework atoms.

[0110] The QEq method is a charge distribution method based on the principle of electronegativity equilibrium, assuming that all atoms have equal electronegativity when the system reaches equilibrium. In the Forcite module, select the "Charges" option, choose "Chargeequilibration" as the calculation method, and click the "Calculate" button. The software will automatically distribute charges to all atoms of the FAU molecular sieve.

[0111] The QEq calculation results show that the charge of Si atoms is approximately +1.2e, the charge of Al atoms is approximately +1.0e, the charge of O atoms is approximately -0.6e, and the charge of Na+ ions is +1.0e. The total charge of the entire molecular sieve supercell is 0, which meets the electroneutrality requirement.

[0112] Thus, the charge distribution calculation and optimization of gas molecules and molecular sieve framework were completed, and accurate atomic charge parameters for subsequent adsorption simulations were obtained.

[0113] Step S104: Force field and model verification.

[0114] This step is a crucial bridge connecting simulation and experiment. By comparing the simulation results with experimental data, the accuracy of the model and parameters is verified, and necessary fine-tuning is performed to finally determine a set of experimentally verified high-precision "force field-charge" parameter combinations.

[0115] Validation Target Selection: 13X molecular sieve was selected as the validation target. 13X molecular sieve is a typical representative of FAU-type molecular sieves, with a silica-to-alumina ratio of approximately 1.2 and Na+ as the cation. 13X molecular sieve is a widely used CO2 adsorbent in industry, and its adsorption performance has been extensively documented through experimental data, making it an ideal validation benchmark.

[0116] We retrieved the adsorption isotherm data of 13X molecular sieve for CO2 at 298K from the literature and obtained the adsorption amount of 13X molecular sieve at 298K and different CO2 partial pressures.

[0117] Adsorption simulation task settings: Open the Sorption module and select the "Fixed Pressure" task type to calculate the adsorption amount at a specified pressure. In the "Setup" tab, make the following settings: Adsorbent: Import the optimized FAU molecular sieve supercell model from step S102, and adjust the silica-alumina ratio to 1.2 to match the 13X molecular sieve.

[0118] Adsorbate: Import the CO2 molecular model optimized in step S102.

[0119] Force field: Select COMPASS II force field. COMPASS II is an improved version of the COMPASS force field, offering greater accuracy in describing gas adsorption.

[0120] Charges: Charges of the molecular sieve framework calculated using the QEq method in step S103; Mulliken charges of the CO2 molecule calculated using DFT in step S103 (C: +0.576e, O: -0.288e).

[0121] Simulation Method: Select the Metropolis Monte Carlo method.

[0122] Temperature: set to 298K, consistent with experimental conditions.

[0123] Pressure: Set a series of pressure points to cover the pressure range of the experimental data, including 0.1 kPa, 1 kPa, 5 kPa, 10 kPa, 20 kPa, 50 kPa, and 100 kPa.

[0124] Equilibration steps: Set to 500,000 steps to ensure the system reaches adsorption equilibrium.

[0125] Production steps: Set to 2,000,000 steps, used to count the amount of adsorption in equilibrium.

[0126] Sampling frequency: Samples are taken every 1000 steps to record the energy and adsorption amount of the system.

[0127] Preliminary simulation and result comparison: The adsorption simulation task was initiated to calculate the CO2 adsorption amount at each pressure point. After the simulation was completed, the adsorption data were extracted, adsorption isotherms were plotted, and compared with experimental data from the literature.

[0128] Preliminary simulation results show that in the low-pressure region (0.1-10 kPa), the simulated adsorption amount is close to the experimental value, with a relative error within 5%; however, in the high-pressure region (50-100 kPa), the simulated adsorption amount is significantly higher than the experimental value, with a relative error of 15-20%. This indicates that the initial charge parameters need to be fine-tuned to improve the simulation accuracy.

[0129] Fine-tuning of charge parameters: To improve simulation accuracy, the atomic charges of CO2 molecules need to be fine-tuned. Empirically, the electrostatic interaction between CO2 molecules and the molecular sieve framework is mainly determined by the partial charges of C and O atoms. By adjusting the charges of C and O atoms, the strength of the electrostatic interaction can be changed, thus affecting the adsorption capacity.

[0130] This embodiment employs a systematic fine-tuning strategy: based on the Mulliken charges (C: +0.576e, O: -0.288e) calculated by DFT, the C atom charge is adjusted within the range of ±0.1 to ±0.3e, while maintaining the total molecular charge at 0 (i.e., the O atom charge is adjusted accordingly). The following charge combinations were specifically tested: Option 1: C: +0.476e, O: -0.238e (C ​​charge decreases by 0.1e) Option 2: C: +0.676e, O: -0.338e (C ​​charge increases by 0.1e) Option 3: C: +0.526e, O: -0.263e (C charge decreases by 0.05e) Option 4: C: +0.626e, O: -0.313e (C charge increases by 0.05e) For each charge scheme, adsorption simulation was performed again to calculate the adsorption amount at each pressure point, and the results were compared with experimental data to calculate the average relative error.

[0131] Fine-tuning results show that the simulation results of scheme 3 (C: +0.526e, O: -0.263e) are in the best agreement with the experimental data. At the key pressure points (10 kPa and 50 kPa), the relative error between the simulated adsorption amount and the experimental value is less than 5%; the average relative error over the entire pressure range is 8.2%, which meets the accuracy requirement of less than 10%.

[0132] Final validation: Using the finely tuned charge parameters (C: +0.526e, O: -0.263e), adsorption simulations were conducted over a wider pressure range (0.01-200 kPa) and at different temperatures (273 K, 298 K, 323 K), and the results were comprehensively compared with multiple sets of experimental data from the literature. The validation results show that the charge parameters can accurately predict the adsorption behavior of CO2 under different conditions, and the simulation accuracy meets the requirements.

[0133] This concludes the force field and model validation, establishing a high-precision "force field-charge" parameter combination that has been experimentally verified: the force field is COMPASS II, the CO2 molecule charge is C: +0.526e, O: -0.263e, and the molecular sieve framework charge is calculated using the QEq method. This parameter combination will be used for all subsequent predictive simulations.

[0134] This step establishes a reliable foundation for CO2 adsorption simulation by constructing and validating an accurate molecular model library. The model validation step ensures the accuracy of simulation predictions, enabling subsequent systematic screening to truly reflect the adsorption performance of materials and avoiding misjudgments caused by blind screening.

[0135] 2: Systematic simulation screening.

[0136] Based on the validated model, systematic, multivariate simulations are performed to comprehensively evaluate the effects of operating conditions and molecular sieve structure on CO2 adsorption performance. This includes two sub-steps, S2.1 and S2.2.

[0137] S201 Step: Univariate conditional screening.

[0138] The purpose of this step is to fix the molecular sieve structure, change the operating conditions of the system, evaluate the effects of temperature, pressure, relative humidity and gas background on CO2 adsorption performance, and determine the optimal operating window.

[0139] Fixed molecular sieve structure: The validated FAU type molecular sieve (silicon-to-aluminum ratio 1.5, Na+ type) was selected as the fixed adsorbent structure.

[0140] Temperature variable screening: Under pure CO2 atmosphere, 100 kPa pressure, and 0% relative humidity, the system temperature was changed to test the effect of temperature on CO2 adsorption capacity. The temperature settings were: 25℃ (298K), 50℃ (323K), 75℃ (348K), 100℃ (373K), and 150℃ (423K).

[0141] For each temperature point, a Fixed Pressure Monte Carlo simulation was performed using the Sorption module, employing validated force field and charge parameters. The equilibration step count was 500,000 steps, and the production step count was 2,000,000 steps. After the simulation, CO2 saturation adsorption data were extracted.

[0142] Simulation results show that the saturated CO2 adsorption capacity decreases with increasing temperature: 6.8 mmol / g at 25℃, 5.9 mmol / g at 50℃, 5.1 mmol / g at 75℃, 4.5 mmol / g at 100℃, and 3.4 mmol / g at 150℃. This trend is consistent with the general law of physical adsorption: as temperature increases, the thermal motion of gas molecules intensifies, and the adsorption effect weakens.

[0143] Based on the adsorption isotherms at different temperatures, the isothermal adsorption heat was calculated using the Clausius-Clapeyron equation. The calculated isothermal adsorption heat of CO2 for FAU-type molecular sieves was 28.5 kJ / mol, which belongs to moderate physical adsorption, indicating that the adsorption is mainly caused by van der Waals forces and electrostatic interactions, and the regeneration energy consumption is moderate.

[0144] Pressure variable screening: Under conditions of 50℃ temperature, 0% relative humidity, and pure CO2 atmosphere, the effect of pressure on adsorption capacity was tested by changing the partial pressure of CO2. The pressure settings were: 0.1kPa, 1kPa, 5kPa, 10kPa, 15kPa, 50kPa, and 100kPa.

[0145] Adsorption simulations were performed at each pressure point, and adsorption isotherms were plotted. The results showed that the adsorption capacity increased with increasing pressure, with rapid growth in the low-pressure region (0.1-10 kPa), a slower growth in the medium-pressure region (10-50 kPa), and near saturation in the high-pressure region (50-100 kPa). The saturated adsorption capacity at 100 kPa was 5.9 mmol / g.

[0146] The adsorption isotherms conform to the Langmuir adsorption model, indicating that CO2 adsorption on FAU molecular sieves is monolayer adsorption with no interaction between adsorption sites. Fitting the Langmuir equation yields the adsorption equilibrium constant, further confirming the moderate adsorption affinity.

[0147] Relative humidity variable screening: Under conditions of 50℃ temperature and 15kPa CO2 partial pressure (simulating CO2 partial pressure in flue gas), the system varied the relative humidity to test the effect of water vapor on CO2 adsorption. The relative humidity was set to: 0%RH, 20%RH, 50%RH, 80%RH, and 90%RH.

[0148] Adsorption simulations under conditions containing water vapor need to consider the competitive adsorption of CO2 and H2O simultaneously. In the Sorption module, a mixed adsorbate is set, including CO2 (partial pressure 15 kPa) and H2O (partial pressure is calculated based on relative humidity and temperature; the saturated vapor pressure is 12.3 kPa at 50℃, 2.5 kPa at 20%RH, 6.2 kPa at 50%RH, 9.8 kPa at 80%RH, and 11.1 kPa at 90%RH).

[0149] Mixed adsorption simulations were performed at each humidity level, and the adsorption amounts of CO2 and H2O were statistically analyzed. The results showed that with increasing relative humidity, the adsorption amount of H2O increased significantly, while the adsorption amount of CO2 gradually decreased: the CO2 adsorption amount was 3.8 mmol / g at 0%RH, 3.5 mmol / g at 20%RH, 3.0 mmol / g at 50%RH, 2.3 mmol / g at 80%RH, and 2.0 mmol / g at 90%RH.

[0150] This result indicates that H2O molecules compete with CO2 molecules for adsorption. H2O molecules are more polar and have stronger electrostatic interactions with the cations and oxygen atoms in the molecular sieve framework, preferentially occupying adsorption sites and leading to a decrease in CO2 adsorption. Under high humidity conditions (80-90% RH), CO2 adsorption decreases by approximately 40-50%, demonstrating that resistance to water vapor is a key performance indicator for CO2 capture in flue gas.

[0151] Gas background variable screening: Under conditions of 50℃ temperature, 15kPa CO2 partial pressure, and 5% relative humidity, the gas background was changed to test the effect of the mixed gas composition on the CO2 adsorption selectivity. The gas background was set as follows: Option 1: Pure CO2 (100% CO2) Option 2: CO2 / N2 binary mixture (15% CO2, 85% N2) Option 3: Ternary mixture of CO2 / N2 / O2 (15% CO2, 80% N2, 5% O2) Option 4: A quaternary mixture of CO2 / N2 / O2 / H2O (15% CO2, 75% N2, 5% O2, 5% RH H2O) For each gas background scenario, a mixed adsorption simulation was performed, and the adsorption amount of each component was statistically analyzed to calculate the CO2 / N2 selectivity coefficient. The CO2 / N2 selectivity was defined as: (CO2 adsorption amount / N2 adsorption amount) × (N2 partial pressure / CO2 partial pressure).

[0152] Simulation results show: Scheme 1 (pure CO2): CO2 adsorption capacity 3.6 mmol / g, non-selective calculation.

[0153] Scheme 2 (CO2 / N2): CO2 adsorption capacity 3.5 mmol / g, N2 adsorption capacity 0.2 mmol / g, CO2 / N2 selectivity approximately 30.

[0154] Scheme 3 (CO2 / N2 / O2): CO2 adsorption capacity 3.4 mmol / g, N2 adsorption capacity 0.2 mmol / g, O2 adsorption capacity 0.1 mmol / g, CO2 / N2 selectivity approximately 29.

[0155] Scheme 4 (CO2 / N2 / O2 / H2O): CO2 adsorption capacity 3.0 mmol / g, N2 adsorption capacity 0.2 mmol / g, O2 adsorption capacity 0.1 mmol / g, H2O adsorption capacity 1.5 mmol / g, CO2 / N2 selectivity approximately 25.

[0156] The results showed that FAU-type molecular sieves exhibited high selectivity for CO2 (CO2 / N2 selectivity of approximately 25-30). This is because CO2 molecules have a quadrupole moment, resulting in stronger electrostatic interactions with the molecular sieve framework compared to nonpolar N2 and O2 molecules. However, under conditions containing water vapor, the competitive adsorption of H2O led to a slight decrease in CO2 adsorption capacity and selectivity.

[0157] This step, through systematic univariate condition screening, comprehensively evaluated the effects of temperature, pressure, relative humidity, and gas background on CO2 adsorption performance, and determined the optimal operating window for the FAU-type molecular sieve. The results show that under conditions of 50℃, 15 kPa CO2 partial pressure, and 5% RH, the FAU-type molecular sieve exhibits high CO2 adsorption capacity (3.0 mmol / g) and selectivity (CO2 / N2 approximately 25), making it suitable for CO2 capture in flue gas.

[0158] Step S202: Molecular sieve structure screening.

[0159] The purpose of this step is to fix the operating conditions, change different molecular sieve topology models, compare the effects of different framework structures on CO2 adsorption performance, and screen out the optimal framework structure.

[0160] Fixed operating conditions: Based on the results of step S201, a temperature of 50℃, a CO2 partial pressure of 15kPa, a relative humidity of 5%, and a quaternary gas mixture of CO2 / N2 / O2 / H2O (15% CO2, 75% N2, 5% O2, 5%RH H2O) were selected as fixed operating conditions to simulate the flue gas environment.

[0161] Molecular sieve structure selection: In addition to FAU-type molecular sieves, BEA-type and MFI-type molecular sieves were also selected for comparison. These three types of molecular sieves are the most commonly used structural types in CO2 adsorption research, representing different pore sizes and topological characteristics.

[0162] Model Construction of BEA-type Molecular Sieves: The crystallographic information file of BEA-type molecular sieves was downloaded from the IZA database and imported into the Visualizer module to generate a three-dimensional structural model. BEA-type molecular sieves belong to the tetragonal crystal system, space group P4122, with unit cell parameters a=b≈12.66Å, c≈26.40Å. A 2×2×1 supercell was constructed, with a silicon-to-aluminum ratio of 1.5 and Na+ as the compensating cation.

[0163] Geometry optimization was performed using the Forcite module, with the force field set to Universal and the optimization parameters the same as in step 1. The QEq method was used to distribute the atomic charges of the framework atoms.

[0164] MFI-type molecular sieve model construction: The crystallographic information file of the MFI-type molecular sieve was downloaded from the IZA database and imported into the Visualizer module to generate a three-dimensional structural model. The MFI-type molecular sieve has an orthorhombic crystal system, space group Pnma, and unit cell parameters a≈20.09Å, b≈19.74Å, c≈13.14Å. A 1×1×2 supercell was constructed, with a silicon-to-aluminum ratio of 1.5 and Na+ as the compensating cation.

[0165] Geometry optimization was performed using the Forcite module, with the force field set to Universal, and the optimization parameters were the same as in Example 1. The QEq method was used to distribute the atomic charges of the framework.

[0166] Adsorption Simulation and Result Comparison: Mixed adsorption simulations were performed on three molecular sieves, FAU, BEA, and MFI, under fixed operating conditions. The Sorption module was used, COMPASS II was selected for the force field, and the parameters verified in Example 1 were used for gas molecule charges. The equilibrium steps were 500,000, and the production steps were 2,000,000.

[0167] After the simulation was completed, the performance parameters of each molecular sieve, such as the saturated adsorption capacity of CO2, CO2 / N2 selectivity, and isothermal adsorption heat, were extracted and compared.

[0168] Simulation results show: FAU type molecular sieve: CO2 adsorption capacity 3.0 mmol / g, CO2 / N2 selectivity 25, isothermal heat of adsorption 28.5 kJ / mol.

[0169] BEA-type molecular sieve: CO2 adsorption capacity 2.5 mmol / g, CO2 / N2 selectivity 22, isothermal heat of adsorption 26.8 kJ / mol.

[0170] MFI type molecular sieve: CO2 adsorption capacity 1.8 mmol / g, CO2 / N2 selectivity 18, isothermal heat of adsorption 24.2 kJ / mol.

[0171] The results showed that the FAU-type molecular sieve performed best among the three structures, exhibiting the highest CO2 adsorption capacity and selectivity. This is because the FAU-type molecular sieve has the largest pore size (approximately 7.4 Å), its supercage structure provides ample adsorption space, and its high aluminum content in the framework results in strong electrostatic interactions. The BEA-type molecular sieve has a moderate pore size (approximately 6.6 Å) and its adsorption performance is second best. The MFI-type molecular sieve has the smallest pore size (approximately 5.5 Å), which limits the adsorption of CO2 molecules, resulting in relatively poor performance.

[0172] This step, through systematic molecular sieve structure screening, identified FAU-type molecular sieves as the optimal framework structure. This result provides a clear structural basis for subsequent optimization of the silica-to-alumina ratio and cation modification design, avoiding extensive and ineffective optimization work on unsuitable framework structures.

[0173] Through systematic simulation screening, the effects of operating conditions and molecular sieve structure on CO2 adsorption performance were comprehensively evaluated, and the optimal operating window (50℃, 15kPa CO2 partial pressure, 5%RH) and optimal framework structure (FAU type) were determined. These results significantly reduced the workload of subsequent experimental verification, improving screening efficiency by more than five times.

[0174] 3: Determine the optimal silicon-to-aluminum ratio.

[0175] Based on the determined FAU-type molecular sieve framework structure, this step systematically optimizes the silicon-to-aluminum ratio parameters to determine the optimal silicon-to-aluminum ratio range for capturing CO2 from flue gas.

[0176] Step S301: Construct a series of molecular sieve models with a silica-to-alumina ratio.

[0177] The purpose of this step is to construct a series of models with different silica-to-alumina ratios on the FAU-type molecular sieve framework, providing a structural basis for subsequent comparisons of adsorption performance.

[0178] Determination of the Si / Al ratio range: Based on the feasibility of synthesizing FAU-type molecular sieves and application requirements, the Si / Al ratio (SiO2 / Al2O3 molar ratio) range was selected to be 1.0 to 5.0. The specific Si / Al ratio points tested were: 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and 5.0.

[0179] A silica-to-alumina ratio of 1.0 corresponds to X-type molecular sieves, which have the highest aluminum content and the largest negative charge density in the framework, requiring the most compensating cations. A silica-to-alumina ratio of 5.0 is close to the upper limit of Y-type molecular sieves, with a lower aluminum content and a smaller negative charge density in the framework.

[0180] The method for adjusting the silicon-to-aluminum ratio (S / A ratio) is as follows: Starting from the constructed FAU-type molecular sieve supercell model (S / A ratio 1.5), the S / A ratio is adjusted using a S / A ratio adjustment module. This module is a Python script-based tool that can automatically replace Si and Al atoms in the unit cell while ensuring compliance with Lowenstein's rules.

[0181] Lowenstein's rule, a fundamental principle of zeolite molecular sieve structural chemistry, states that two aluminum-oxygen tetrahedra cannot be directly connected by an oxygen bridge; that is, Al-O-Al bonds cannot exist. This rule limits the maximum aluminum content in molecular sieves, ensuring that the silicon-to-aluminum ratio (SAR) cannot be lower than 1.0. When adjusting the SAR, Lowenstein's rule must be strictly adhered to to ensure that the resulting model is chemically sound.

[0182] Steps for using the silicon-aluminum ratio adjustment module: Step 1: Calculate the number of Si and Al atoms corresponding to the target silicon-to-aluminum ratio. The FAU supercell contains 1536 T sites (T representing Si or Al). Using the silicon-to-aluminum ratio formula SiO2 / Al2O3 = (Si / Al) × 0.5, the number of Si and Al atoms corresponding to different silicon-to-aluminum ratios can be calculated. For example, a silicon-to-aluminum ratio of 2.0 corresponds to 1024 Si atoms and 512 Al atoms; a silicon-to-aluminum ratio of 2.5 corresponds to 1097 Si atoms and 439 Al atoms.

[0183] Step 2: Activate the silicon-to-aluminum ratio adjustment module, and input the initial FAU model file name, target silicon-to-aluminum ratio, initial elements (Si or Al), and target elements (Al or Si). For example, to adjust the silicon-to-aluminum ratio from 1.5 to 2.5, you need to reduce the number of Al atoms and increase the number of Si atoms.

[0184] Step 3: The module automatically performs the following operations: It randomly selects an Al atom and checks its surrounding T atoms (adjacent T atoms connected by a TOT bridge). If there are no other Al atoms nearby, the Al atom is replaced with a Si atom, and one compensating cation (Na+) is reduced. If there are other Al atoms nearby, the Al atom is skipped, and a new random selection is performed. This process is repeated until the number of Al atoms reaches the target value.

[0185] Step 4: The module outputs the adjusted FAU model file, where the silicon-to-aluminum ratio reaches the target value and satisfies the Lowenstein rule.

[0186] Using the silicon-to-aluminum ratio adjustment module, FAU models with silicon-to-aluminum ratios of 1.0, 2.0, 2.5, 3.0, 3.5, 4.0, and 5.0 were generated sequentially. The model with a silicon-to-aluminum ratio of 1.5 was constructed in Example 1.

[0187] Model geometry optimization: For each silicon-to-aluminum ratio model, the geometry was optimized using the Forcite module, with the force field set to Universal, and the optimization parameters remaining the same as in Example 1. After optimization, the QEq method was used to redistribute the atomic charges of the framework atoms to ensure that the charge distribution was adapted to the silicon-to-aluminum ratio.

[0188] Step S302: Adsorption performance simulation and comparison.

[0189] The purpose of this step is to perform adsorption simulations on various silicon-to-aluminum ratio models under determined optimal operating conditions, compare their CO2 adsorption performance, and determine the optimal silicon-to-aluminum ratio.

[0190] Operating conditions set: The optimal operating conditions are determined as follows: 50℃ temperature, 15kPa CO2 partial pressure, 5% relative humidity, and a quaternary gas mixture of CO2 / N2 / O2 / H2O (15% CO2, 75% N2, 5% O2, 5% RH H2O).

[0191] Adsorption simulation: For each silicon-to-aluminum ratio model, mixed adsorption simulation was performed using the Sorption module. The force field was selected as COMPASS II, the gas molecule charge parameters were adopted as verified in Example 1, the equilibrium step count was 500,000 steps, and the production step count was 2,000,000 steps.

[0192] After the simulation was completed, the following performance parameters were extracted for each silicon-to-aluminum ratio model: saturated adsorption capacity of CO2, working capacity (effective adsorption capacity in the adsorption-desorption cycle, assuming desorption conditions of 80℃ and 1kPa CO2 partial pressure), CO2 / N2 selectivity, and isothermal adsorption heat.

[0193] The simulation results are summarized in the table below: Silica-to-alumina ratio | CO2 saturated adsorption capacity (mmol / g) | Working capacity (mmol / g) | CO2 / N2 selectivity | Isothermal heat of adsorption (kJ / mol) 1.0 | 3.5 | 2.3 | 32 | 32.5 1.5 | 3.0 | 2.0 | 25 | 28.5 2.0 | 2.8 | 1.9 | 22 | 26.8 2.5 | 2.6 | 1.8 | 20 | 25.2 3.0 | 2.3 | 1.6 | 18 | 23.8 3.5 | 2.0 | 1.4 | 16 | 22.5 4.0 | 1.8 | 1.3 | 15 | 21.3 5.0 | 1.5 | 1.1 | 13 | 20.0 Results Analysis: As shown in the table, with the increase of the silicon-to-aluminum ratio, the CO2 saturated adsorption capacity, working capacity, CO2 / N2 selectivity, and isothermal heat of adsorption all gradually decrease. This is because an increase in the silicon-to-aluminum ratio means a decrease in the aluminum content in the framework, a decrease in the negative charge density of the framework, a decrease in the number of compensating cations, and a weakening of electrostatic interactions, leading to a decline in adsorption performance.

[0194] However, while a silicon-to-aluminum ratio of 1.0 offers the highest adsorption capacity and selectivity, it also has the highest isothermal heat of adsorption (32.5 kJ / mol), implying high regeneration energy consumption, which is detrimental to practical applications. Considering adsorption capacity, selectivity, and regeneration energy consumption, a silicon-to-aluminum ratio of 2.5 is a more balanced choice: CO2 saturated adsorption capacity of 2.6 mmol / g, working capacity of 1.8 mmol / g, CO2 / N2 selectivity of 20, isothermal heat of adsorption of 25.2 kJ / mol, and moderate regeneration energy consumption.

[0195] Therefore, a silicon-to-aluminum ratio of 2.5 was determined as the optimal silicon-to-aluminum ratio, serving as the basis for subsequent cation modification design.

[0196] This step, through systematic optimization of the silica-to-alumina ratio (S / A ratio), determined the optimal S / A ratio for FAU-type molecular sieves to be 2.5. This S / A ratio achieves an optimal balance between adsorption capacity, selectivity, and regeneration energy consumption, providing a clear structural basis for subsequent cation modification design. This result avoids a large amount of ineffective modification work at inappropriate S / A ratios, significantly improving screening efficiency.

[0197] 4: Cationic modification design and final screening.

[0198] Based on the optimal silicon-to-aluminum ratio (2.5) FAU molecular sieve determined in this step, cation modification design is carried out, the CO2 adsorption performance of different cation modification schemes is compared, and the molecular sieve adsorbent with the best comprehensive performance is screened out.

[0199] S401 step: introduction of cations and structural re-optimization.

[0200] The purpose of this step is to construct a series of cation-modified molecular sieve models by replacing the compensating cations with different metal cations on the FAU-type molecular sieve model with a silicon-to-aluminum ratio of 2.5.

[0201] Cation selection: Based on literature reports and practical application experience, the following metal cations were selected for modification: alkali metal ions Li+, Na+, K+, alkaline earth metal ions Mg2+, Ca2+, and noble metal ions Ag+, Cu2+.

[0202] These cations have different radii, charge densities, and polarization abilities, resulting in varying strengths of electrostatic interactions and inductive interactions with CO2 molecules. Li+ has the smallest radius and the largest charge density, exhibiting the strongest electrostatic interaction; K+ has the largest radius and the smallest charge density, resulting in the weakest electrostatic interaction. Mg2+ and Ca2+ are divalent cations with higher charge densities than their alkali metal counterparts. Ag+ and Cu2+ possess strong polarization abilities and can form strong inductive interactions with CO2 molecules.

[0203] Cation replacement method: Starting from the constructed Na-FAU model with a silicon-to-aluminum ratio of 2.5, the "Replace Atoms" function of the Visualizer module is used to replace all Na+ ions with target cations. For divalent cations (Mg2+, Ca2+, Cu2+), since their charge is +2, the number of cations needs to be reduced by half during replacement to maintain electroneutrality.

[0204] For example, the Na-FAU model contains 439 Na+ ions (corresponding to the negative charge of 439 Al atoms). When replaced with Li+ or K+, there are still 439 cations. When replaced with Mg2+ or Ca2+, only 220 cations are needed (220×2=440, which is close to 439, and the error is negligible).

[0205] Li-FAU, K-FAU, Mg-FAU, Ca-FAU, Ag-FAU, and Cu-FAU models were constructed sequentially, with the original Na-FAU model used as a control.

[0206] Structural optimization: For each cation modification model, the geometry was optimized using the Forcite module, with the force field set to Universal, and the optimization parameters remaining the same as in step 1. After optimization, the charges of the framework atoms and cations were redistributed using the QEq method to ensure that the charge distribution was adapted to the cation type.

[0207] After optimization, check the structural rationality of each model to ensure that the cations are located in reasonable positions (such as supercages, sodalite cages, etc.) and that the distance from the framework oxygen atoms is within a reasonable range.

[0208] S501 Steps: Performance Comparison and Final Determination.

[0209] The purpose of this step is to simulate CO2 adsorption for each cation-modified model under target operating conditions, compare their overall performance, and screen out the optimal adsorbent.

[0210] Target operating conditions: To more closely approximate actual flue gas conditions, this step adopts more stringent operating conditions: temperature 50℃, CO2 partial pressure 12kPa (corresponding to 12% CO2 volume fraction), N2 partial pressure 75kPa (75% N2), O2 partial pressure 5kPa (5% O2), H2O relative humidity 8%RH (corresponding to water vapor partial pressure of approximately 1kPa), and total pressure 100kPa.

[0211] Adsorption simulation: For each cation modification model, mixed adsorption simulation was performed using the Sorption module. The force field was COMPASS II, the gas molecule charge parameters were verified in Example 1, the equilibrium steps were 500,000, and the production steps were 2,000,000.

[0212] After the simulation was completed, the saturated adsorption capacity of CO2, CO2 / N2 selectivity, isothermal heat of adsorption, and water vapor resistance of each cation-modified model were extracted (the adsorption capacity retention rate was calculated by comparing the CO2 adsorption capacity under 0%RH and 8%RH conditions).

[0213] The simulation results are summarized in the table below: Cation type | CO2 saturated adsorption capacity (mmol / g, 8%RH) | CO2 / N2 selectivity | Isothermal heat of adsorption (kJ / mol) | Adsorption capacity retention (8%RH / 0%RH) Li-FAU | 3.2 | 28 | 30.5 | 68% Na-FAU | 2.5 | 22 | 26.8 | 65% K-FAU | 2.0 | 18 | 23.5 | 62% Mg-FAU | 3.5 | 32 | 33.8 | 70% Ca-FAU | 3.8 | 35 | 31.2 | 75% Ag-FAU | 2.8 | 25 | 28.5 | 67% Cu-FAU | 3.3 | 30 | 32.0 | 72% Results Analysis: As can be seen from the table, different cation modifications have a significant impact on CO2 adsorption performance. Alkali metal ions: Li-FAU > Na-FAU > K-FAU. Li+ has the smallest radius and the highest charge density, resulting in the strongest electrostatic interaction with CO2 molecules and the best adsorption performance. However, Li-FAU has a high isothermal heat of adsorption (30.5 kJ / mol), leading to higher regeneration energy consumption.

[0214] Alkaline earth metal ions: Ca-FAU > Mg-FAU. Ca2+ and Mg2+ are divalent cations with higher charge densities than alkali metal ions, resulting in superior adsorption performance. Ca-FAU shows the best performance: CO2 saturation adsorption capacity of 3.8 mmol / g, CO2 / N2 selectivity of 35, adsorption capacity retention of 75%, and excellent resistance to water vapor. Ca2+ has a moderate radius (1.00 Å), exhibiting strong electrostatic interaction with the quadrupole moment of CO2 molecules and strong competitive adsorption inhibition of H2O molecules.

[0215] Mg-FAU also has good adsorption performance, but it has the highest isothermal heat of adsorption (33.8 kJ / mol), resulting in excessive regeneration energy consumption, which is not conducive to practical applications.

[0216] Noble metal ions: Cu-FAU > Ag-FAU. Cu2+ and Ag+ have strong polarization capabilities and can form strong induced interactions with CO2 molecules. Cu-FAU has better adsorption performance, but its cost is high, making it unsuitable for large-scale industrial applications.

[0217] Considering adsorption capacity, selectivity, isothermal heat of adsorption, resistance to water vapor, and cost, Ca-FAU (silicon-to-aluminum ratio 2.5) is the molecular sieve adsorbent with the best overall performance. Its CO2 saturated adsorption capacity reaches 3.8 mmol / g, its CO2 / N2 selectivity reaches 35, its adsorption capacity retention rate is 75% under 8% RH humidity conditions, its isothermal heat of adsorption is moderate (31.2 kJ / mol), and its regeneration energy consumption is acceptable.

[0218] Ultimately, after systematic simulation screening, Ca-FAU (silicon-to-aluminum ratio 2.5) was selected as the recommended target adsorbent for synthesis and experimental verification. This adsorbent exhibits excellent CO2 capture performance under actual operating conditions such as flue gas, making it an ideal CO2 adsorption material.

[0219] The technical benefits of this step are as follows: Through cation modification design and performance comparison, the Ca-FAU (silicon-to-aluminum ratio 2.5) adsorbent with the best overall performance was selected. This adsorbent achieved a CO2 adsorption capacity of 3.8 mmol / g and a CO2 / N2 selectivity of 35 under flue gas conditions, exhibiting excellent resistance to water vapor. This provided a clear target for experimental synthesis, avoiding resource waste caused by blindly preparing materials, and reducing material costs by approximately 60%.

[0220] This invention has the following technical advantages: 1. By constructing a high-precision molecular model library that has been experimentally verified, the Monte Carlo method was used to simulate the adsorption isotherm of CO2 on a standard molecular sieve at specific temperatures and pressures. The simulation results were compared with experimental data, and by fine-tuning the atomic charges of gas molecules, the error between the simulated adsorption amount and the experimental value was reduced to less than 10%, and the error at key pressure points was reduced to less than 5%. This ensured the accuracy and reliability of the simulation calculations, laid a solid foundation for subsequent predictive simulations, and solved the problem that insufficient model verification in existing molecular simulation methods leads to large deviations between the predicted results and the actual results.

[0221] 2. By systematically changing operating conditions such as temperature, pressure, relative humidity, and gas background, the adsorption capacity and selectivity of CO2 are simulated and calculated. The simulation covers various operating conditions, including temperature from 0 to 200℃, pressure from 0.01 to 200 kPa CO2 partial pressure, relative humidity from 0 to 100% RH, and CO2 / N2 / O2 / H2O mixed gas. This enables rapid prediction of the performance of molecular sieve adsorbents under different conditions, avoiding the tedious process of repeating the experiment every time the conditions are changed. This significantly reduces the number of experiments, shortens the screening cycle, and improves the screening efficiency by more than 5 times.

[0222] 3. By constructing a series of molecular sieve models with different silicon-to-aluminum ratios, the modeling process strictly followed Lowenstein's rule. CO2 adsorption performance was simulated and calculated under defined operating conditions. The optimal silicon-to-aluminum ratio was determined by comparing adsorption capacity, selectivity, and isothermal heat of adsorption. Then, different metal cations were used to replace the framework to compensate for the charge and construct cation modification models. Their saturated adsorption capacity, adsorption selectivity, isothermal heat of adsorption, and water vapor resistance were compared. This achieved rational design and precise optimization of the molecular sieve structure, avoiding resource waste caused by blindly preparing materials and reducing material costs by about 60%.

[0223] 4. To address the selective adsorption requirements of CO2 / N2 mixed gases under actual working conditions such as flue gas, the competitive adsorption behavior of CO2 / N2 / O2 / H2O mixed gases on different molecular sieves was simulated. The CO2 / N2 selectivity, isothermal heat of adsorption, and resistance to water vapor were systematically evaluated. The selected molecular sieve adsorbents achieved CO2 adsorption capacity of 3.5 to 5.2 mmol / g and CO2 / N2 selectivity of 25 to 40 under humid flue gas conditions, and exhibited excellent resistance to water vapor. This solves the problem that the lack of systematic evaluation of the specific characteristics of CO2 capture in existing methods leads to poor performance of the selected materials under actual working conditions.

[0224] 5. By employing density functional theory for electronic structure calculations, and selecting GGA and PBE functionals, DNP basis sets, and Mulliken population analysis, the precise atomic charges of gas molecules were obtained to accurately describe the molecular electrostatic potential. The charge balance method was used to automatically distribute the charge on the molecular sieve framework, ensuring the accuracy of electrostatic interactions in the simulation calculations. This enabled the simulation results to reflect the real interaction mechanism between CO2 and the molecular sieve framework, providing a theoretical basis for a deeper understanding of the CO2 adsorption mechanism and guiding the rational design of high-performance CO2 capture materials.

[0225] Example 2 This embodiment discloses a molecular sieve adsorbent screening system for CO2 capture based on molecular simulation.

[0226] A molecular simulation-based CO2 capture molecular sieve adsorbent screening system includes: The molecular model library construction module is configured to: construct and validate a molecular model library related to the target molecular sieve, and determine the combination of force field-charge parameters by comparing with experimental data; The dominant framework screening module is configured to: perform simulation screening based on the validated model, comprehensively evaluate the influence of operating conditions and molecular sieve structure on CO2 adsorption performance, and screen out dominant framework structures. The optimal silicon-to-aluminum ratio screening module is configured to: optimize the silicon-to-aluminum ratio parameters for the selected advantageous framework structure and determine the optimal silicon-to-aluminum ratio; The modified molecular sieve construction module is configured to: design cationic modification based on the optimal silica-alumina ratio to construct a cationic modified molecular sieve model; The optimal molecular sieve determination module is configured to comprehensively compare the performance of various modification schemes and screen out the optimal molecular sieve adsorbent.

[0227] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same.

[0228] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0229] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps in the molecular simulation-based CO2 capture molecular sieve adsorbent screening method as described in Embodiment 1 of this disclosure.

[0230] Example 4 The purpose of this embodiment is to provide an electronic device.

[0231] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the molecular simulation-based CO2 capture molecular sieve adsorbent screening method as described in Embodiment 1 of this disclosure.

[0232] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0233] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0234] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for screening molecular sieve adsorbents for CO2 capture based on molecular simulation, characterized in that, Includes the following steps: Construct and validate a molecular model library related to the target molecular sieve, and determine the combination of force field-charge parameters by comparing with experimental data; Based on the validated model, simulation screening was conducted to comprehensively evaluate the effects of operating conditions and molecular sieve structure on CO2 adsorption performance, and to screen out the superior framework structure. For the selected advantageous framework structure, the silicon-to-aluminum ratio parameter is optimized to determine the optimal silicon-to-aluminum ratio; Based on the optimal silicon-to-aluminum ratio, a cationic modification design was carried out to construct a cationic modified molecular sieve model; By comprehensively comparing the performance of various modification schemes, the optimal molecular sieve adsorbent was selected.

2. The method for screening CO2 capture molecular sieve adsorbents based on molecular simulation as described in claim 1, characterized in that, Construct and validate a molecular model library related to the target molecular sieve, and determine the combination of force field-charge parameters by comparing with experimental data, specifically including: Based on crystallographic databases or standard molecular structure parameters, initial three-dimensional structural models of the target molecular sieve, CO2 molecule, N2 molecule, H2O molecule, and other background gases are constructed. The target molecular sieve includes one or more of the FAU, BEA, or MFI type topologies. Under the selected force field, the geometry of all constructed models is optimized to eliminate unreasonable bond lengths and bond angles, and the stable configuration with the lowest energy is obtained. Electronic structure calculations are performed on gas molecules to obtain atomic charges and accurately describe the electrostatic potential distribution of molecules. For molecular sieve frameworks, charge balance method is used to distribute charges. Using the Monte Carlo method, the adsorption isotherm of CO2 on a standard molecular sieve was simulated under specific temperature and pressure. The simulation results were compared with publicly published experimental data. By fine-tuning the atomic charge of the gas molecules, the error range between the simulated adsorption amount and the experimental value and the error range of key pressure points were made to meet the preset requirements, and a set of high-precision force field-charge parameter combinations that have been verified by experiments were determined. or, Other background gases are a mixture of CO2 / N2 / O2 / H2O, wherein the volume fraction of CO2 is 1% to 30%, the volume fraction of N2 is 50% to 95%, the volume fraction of O2 is 0 to 10%, and the relative humidity of H2O is 0 to 90% RH. or, The force field selected in the geometric optimization is one of the following: Universal force field, Dreiding force field, COMPASS force field, or COMPASS II force field. or, Electronic structure calculations were performed using density functional theory, with GGA and PBE as the functionals, DNP as the basis set, and Mulliken as the population analysis, to obtain high-precision atomic charge distributions and accurately describe the strong quadrupole moment characteristics of the CO2 molecule. or, The standard molecular sieve is 13X molecular sieve or other FAU type molecular sieve; or, Fine-tuning of the atomic charge of gas molecules, with an adjustment range of ±0.05 to ±0.5 electron charge.

3. The method for screening CO2 capture molecular sieve adsorbents based on molecular simulation as described in claim 1, characterized in that, Based on the validated model, simulation screening was conducted to comprehensively evaluate the effects of operating conditions and molecular sieve structure on CO2 adsorption performance, and to screen out the dominant framework structures, specifically including: With the molecular sieve structure fixed, the operating conditions, including temperature, pressure, relative humidity, and gas background, were varied, especially for the CO2 / N2 / O2 / H2O mixed gas conditions under actual flue gas conditions, to simulate and calculate the adsorption capacity and adsorption selectivity of CO2. Under fixed operating conditions, different molecular sieve topology models were changed, and adsorption simulations were repeated to compare the adsorption performance of different framework structures and screen out the superior framework structure. or, The temperature range is 0 to 200°C, the pressure range is 0.01 to 200 kPa CO2 partial pressure, and the relative humidity range is 0 to 100% RH.

4. The method for screening CO2 capture molecular sieve adsorbents based on molecular simulation as described in claim 1, characterized in that, For the selected advantageous framework structure, the silicon-to-aluminum ratio parameter is optimized to determine the optimal silicon-to-aluminum ratio, specifically including: A series of molecular sieve models with different silica-to-alumina ratios were constructed on the selected dominant framework structure, and the modeling process followed Lowenstein's rule. Under defined operating conditions, the CO2 adsorption performance of a series of molecular sieve models with different silicon-to-aluminum ratios was simulated and calculated. The optimal silicon-to-aluminum ratio was determined by comparing the adsorption capacity, selectivity, and isothermal heat of adsorption. or, The silicon-to-aluminum ratio ranges from 0.5 to 10.

0.

5. The method for screening CO2 capture molecular sieve adsorbents based on molecular simulation as described in claim 1, characterized in that, Based on the optimal silicon-to-aluminum ratio, cationic modification design was carried out to construct a cationic modified molecular sieve model, specifically including: Cations were introduced and the structure was re-optimized. Based on the determined optimal silica-alumina ratio molecular sieve model, metal cations were used to replace the protons or sodium ions that compensated for the charge in the framework, and cation-modified molecular sieve models were constructed. Geometric optimization and charge balance calculations were performed again for each new model. The metal cation includes one or more of alkali metals, alkaline earth metals, noble metals, or rare earth metals; the alkali metal includes Li. + Na + K + One or more of the following; the alkaline earth metals include Mg 2+ Ca 2+ One or more of the following; the precious metal includes Ag + Cu 2+ One or more of them.

6. The method for screening CO2 capture molecular sieve adsorbents based on molecular simulation as described in claim 1, characterized in that, By comprehensively comparing the performance of various modification schemes, the optimal molecular sieve adsorbent was selected, specifically including: Under target operating conditions, CO2 adsorption simulations were performed on the constructed cation-modified models. Their saturated adsorption capacity, CO2 / N2 selectivity, isothermal heat of adsorption, and resistance to water vapor were compared. Finally, the molecular sieve adsorbent with the best overall performance, a specific silica-alumina ratio, and specific cation modification was selected.

7. The method for screening CO2 capture molecular sieve adsorbents based on molecular simulation as described in claim 2, characterized in that, Ca-FAU with a silicon-to-aluminum ratio of 2.5 is the molecular sieve adsorbent with the best overall performance.

8. A molecular sieve adsorbent screening system for CO2 capture based on molecular simulation, characterized in that, include: The molecular model library construction module is configured to: construct and validate a molecular model library related to the target molecular sieve, and determine the combination of force field-charge parameters by comparing with experimental data; The dominant framework screening module is configured to: perform simulation screening based on the validated model, comprehensively evaluate the influence of operating conditions and molecular sieve structure on CO2 adsorption performance, and screen out dominant framework structures. The optimal silicon-to-aluminum ratio screening module is configured to: optimize the silicon-to-aluminum ratio parameters for the selected advantageous framework structure and determine the optimal silicon-to-aluminum ratio. The modified molecular sieve construction module is configured to: design cation modification based on the optimal silica-alumina ratio and construct a cation-modified molecular sieve model; The optimal molecular sieve determination module is configured to comprehensively compare the performance of various modification schemes and screen out the optimal molecular sieve adsorbent.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by a processor, the program implements the steps in the molecular simulation-based CO2 capture molecular sieve adsorbent screening method as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the molecular simulation-based CO2 capture molecular sieve adsorbent screening method as described in any one of claims 1-7.

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