A screening method for hydration and desolvation pathways of aqueous sodium-ion battery electrolyte water cells

Abstract

The application discloses a kind of aqueous sodium-ion battery electrolyte hydration cell desolvation path screening method, comprising the following steps: step 1: constructing and optimizing the structure model of anion and cation of electrolyte dissolution in electrolyte and aqueous sodium-ion battery electrolyte system model: step 2: the ion hydration cell structure evolution in electrolyte is simulated;Step 3: extracting and optimizing steady-state ion hydration cell from the simulation results of step 2;Step 4: the desolvation path of steady-state ion hydration cell obtained in step 3 is analyzed, and the best path of steady-state ion hydration cell desolvation is obtained.The present application takes the ion hydration mechanism in aqueous sodium-ion battery electrolyte as the core, combines classical molecular dynamics, ab initio molecular dynamics, density functional theory, and studies the steady-state ion hydration cell configuration of electrolyte under different concentrations by multi-scale simulation and screens its desolvation path, to provide theoretical guidance for high-performance aqueous sodium-ion electrolyte design.

Description

Technical Field

[0001] This invention relates to the field of computational chemistry, specifically to a method for screening the desolvation pathway of hydrated cells in aqueous sodium-ion battery electrolytes. Background Technology

[0002] As a core component of aqueous sodium-ion batteries, the electrolyte imparts advantages to the battery system in terms of safety, environmental friendliness, renewability, and low cost. Ion hydration in the electrolyte has a decisive influence on the hydrogen bonding network and interfacial dynamics of the solution, thereby affecting the electrolyte's ion transport, solution viscosity, hydrogen evolution reaction, wide-temperature stability, and thermal stability. Ion transport behavior is directly affected by ion hydration and its desolvation behavior. Desolvation refers to the process of ions detaching from the solvation layer and is a prerequisite for ion transport; its efficiency directly affects the electrolyte's ionic conductivity and diffusion coefficient. High desolvation energy can lead to sluggish interfacial reaction kinetics, limiting rate performance, while low desolvation energy can significantly improve ion mobility and rate performance. Desolvation behavior is closely related to ion hydration. A dense ion-hydrated layer increases the viscous friction of ion migration, reducing the diffusion coefficient; while a loose ion-hydrated layer reduces the desolvation energy, increasing the diffusion coefficient. It is evident that the formation and structure of ion hydration cells in aqueous electrolytes, as well as the effective characterization of the desolvation process, are crucial for understanding ion hydration and desolvation, and thus guiding the rational design of electrolytes.

[0003] However, current modeling methods for ion hydrate cells in aqueous electrolytes are mainly based on initial models of independent cations, anions, and water molecules. After initial assembly, a single hydrate cell model is used for subsequent analysis of hydrate cell performance and electrolyte performance. This ion hydrate cell design method cannot accurately reproduce the types, configurations, hydration kinetics, hydrogen bond network evolution, interfacial kinetics, and electrolyte thermodynamic behavior in actual electrolytes. Therefore, it cannot accurately reflect the desolvation process of hydrate cells and cannot truly describe the ion transport behavior based on hydration and desolvation in electrolytes. For example, patent CN117437999A discloses a method for predicting the electrochemical performance of electrolytes. However, this patent uses an isolated, fixed coordination number single solvated complex model, which is based only on the primary assembly of independent anions, cations, and solvent molecules. This model is detached from the actual bulk environment of the electrolyte and cannot reconstruct the coexistence state of multiple hydrated cells and the dynamic evolution of hydrogen bond networks. At the same time, it is based only on static optimization configuration calculations and cannot dynamically and accurately characterize the ion hydration process. In addition, this patent does not analyze the desolvation process of ion hydrated cells, does not quantify the desolvation energy barrier, does not screen desolvation pathways, and cannot reveal the kinetic mechanisms of ion transport and interfacial reactions. Furthermore, relying solely on parameters such as binding energy, bond length, and frontier orbital energy levels, it cannot comprehensively predict key performance characteristics of electrolytes such as ionic conductivity and rate performance. The predicted results deviate significantly from reality and are difficult to effectively guide the directional design of electrolytes. Summary of the Invention

[0004] To address the shortcomings of existing ion hydrate cell design methods, which struggle to accurately reproduce the types, configurations, hydration kinetics, hydrogen bond network evolution, interfacial kinetics, and electrolyte thermodynamics in actual electrolytes, as well as their inability to precisely reflect the desolvation process of hydrate cells and accurately describe ion transport behavior based on hydration and desolvation in electrolytes, this invention provides a method for screening desolvation pathways of hydrate cells in aqueous sodium-ion battery electrolytes. Based on the ion hydration mechanism in aqueous sodium-ion battery electrolytes, and combining classical molecular dynamics, ab initio molecular dynamics, and density functional theory, this invention uses multi-scale simulations to study the steady-state ion hydrate cell configurations of sodium trifluoromethanesulfonate (NaOTF) and sodium perchlorate (NaClO4) at different concentrations and screen their dehydration pathways. This reveals the physical mechanism by which ion hydrate cells affect the macroscopic performance of the electrolyte, providing theoretical guidance for the design of high-performance aqueous sodium-ion electrolytes.

[0005] To achieve the above objectives, this invention provides a method for screening the desolvation pathway of hydrated cells in aqueous sodium-ion battery electrolytes, comprising the following steps:

[0006] Step 1: Construct and optimize the structural models of anions and cations in the electrolyte solution and the aqueous sodium ion electrolyte system model:

[0007] Step 2: Simulate the evolution of ion hydrate cell structure in the electrolyte;

[0008] Step 3: Extract and optimize steady-state ion hydrate cells from the simulation results of Step 2;

[0009] Step 4: Analyze the desolvation path of the steady-state ion hydrate obtained in Step 3 to obtain the optimal path for the desolvation of the steady-state ion hydrate.

[0010] Preferably, in step 1:

[0011] The first step is to construct anionic and cation structural models respectively: Na is formed by dissolving sodium salt NaX electrolyte in water. + X - The initial structural models of ions and water molecules were geometrically optimized using quantum chemical calculations to obtain the most stable structural model.

[0012] The second step is to construct an initial electrolyte model: based on the most stable structural model obtained in the first step, an initial electrolyte system is constructed.

[0013] Preferably, in the second step of constructing the initial electrolyte model, the ratio of NaX / H2O molecules is set according to the concentration used in the experiment to ensure that the ion concentration or electrolyte density is consistent with the experimental conditions; the electrolyte system model adopts periodic boundary conditions, and the initial electrolyte system model is structurally optimized using quantum chemical calculations to obtain the most stable electrolyte system model.

[0014] Preferably, in step 2:

[0015] The first step is to use the NPT ensemble to perform classical molecular dynamics simulations: balance the electrolyte density to the experimental range, ensure that the system reaches thermodynamic equilibrium, and enable the ion hydrate cell to reach a stable configuration;

[0016] The second step is to use the NVT ensemble to perform AIMD simulation: the electronic structure of the electrolyte system is obtained based on the DFT simulation calculation and the interaction forces between atoms are calculated in real time. Based on this, various transient behaviors that occur in the electrolyte are spontaneously captured, and the dynamic evolution process of ion hydrate cells in the aqueous sodium ion electrolyte system is simulated.

[0017] The third step is to analyze the coordination state of hydrated ions: calculate the radial distribution function of oxygen atoms of sodium ions and water molecules and oxygen atoms of anions, and integrate to obtain the coordination number of oxygen atoms and anions of sodium ions within the first solvation shell.

[0018] Preferably, in the first step of performing classical molecular dynamics simulation using the NPT ensemble, the equilibrium final state electrolyte system configuration and average cell parameters are extracted and used as initial input information for ab initio molecular dynamics simulation.

[0019] Preferably, in step 3:

[0020] The first step is to extract the sodium ion hydrate cell model: After the electrolyte reaches the final equilibrium state in step 2, a variety of basic sodium ion hydrate cell morphologies are initially formed in the electrolyte. In combination with the equilibrium trajectory reached by the dynamic evolution of the sodium ion hydrate cells simulated by AIMD in step 2, the visualization software VMD is used to extract the sodium ion hydrate cells.

[0021] The second step is to optimize the ion hydrated cell model: geometric optimization and frequency calculation are performed on the ion hydrated cell model extracted in the first step to obtain the optimized steady-state ion hydrated cell model.

[0022] The third step is steady-state ion hydrate cell energy analysis: Based on the electrostatic potential distribution, the polarity characteristics of the surface charge of molecules or ions are obtained. At the same time, the highest occupied molecular orbital and the lowest unoccupied molecular orbital in the hydrate cell are analyzed. The solvation energy is used to represent the strength of the interaction between the solute sodium ion and the solvent molecule, thereby describing the relevant energy changes in the solvation process.

[0023] Preferably, in step 4:

[0024] The first step is the desolvation process of the ion hydrate cell: the intermolecular forces within the ion hydrate cell are calculated, the weakest interacting solvent molecules are identified and removed, and then the geometry is optimized. The changes in the distance between the remaining atoms in the optimized ion hydrate cell are measured. Then, the solvent molecules are gradually removed through iterative processes until only sodium ions remain. At this point, complete desolvation can be considered achieved.

[0025] The second step is to screen the desolvation path of the ion hydrated cell: through the iterative process in the previous step, the sodium ions are simulated from having a complete hydration layer to complete dehydration. The energy difference at each step is recorded and the desolvation energy barrier curve is plotted. The lowest energy path of the desolvation process is screened, which is the optimal path for the desolvation of the steady-state ion hydrated cell.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The method described in this invention overcomes the limitations of traditional modeling, constructing a realistic electrolyte model that perfectly matches experimental conditions, providing pre-experimental and precise structural guidance. Existing technologies generally assemble single hydrated cell models by artificially placing anions, cations, and water molecules, which are completely disconnected from the concentration, environment, and dynamic behavior of actual electrolytes, and cannot reflect the diversity of solvation structures in real solutions. This invention sets the molecular ratio according to the type and concentration of sodium salts used in the experiment, matches the experimental density and thermodynamic equilibrium state through classical MD simulation of the NPT ensemble, and then captures the real ion coordination and solvent exchange dynamic behavior through AIMD simulation of the NVT ensemble. Finally, it extracts multiple steady-state hydrated cell models from the equilibrium trajectory, which can accurately identify different solvation modes such as SIP, CIP, and AGG and statistically analyze their proportions, completely restoring the evolution of coordination structures caused by changes in concentration and temperature in actual electrolytes. In battery research and development experiments, the method described in this invention can identify the dominant steady-state hydrated cell structure and solvation mode in the target electrolyte system before the experiment begins, clarify the influence of different sodium salt types and electrolyte concentrations on the hydrated structure, eliminate the need for extensive blind formulation screening experiments to explore the optimal concentration range and anion selection, significantly reduce the number of experimental trials, and significantly shorten the electrolyte formulation development cycle.

[0028] 2. The method described in this invention achieves dynamic quantification and precise selection of the optimal path for the entire desolvation process, directly guiding the targeted optimization of electrolyte rate performance and ion transport performance. Existing technologies can only calculate single parameters such as binding energy based on static configurations, failing to recreate the dynamic process of desolvation, let alone quantify the desolvation energy barrier, making it difficult to explain the core reasons for differences in electrolyte rate performance and ionic conductivity observed in experiments. This invention, through iterative removal of weakly interacting solvent molecules, fully simulates the entire process of sodium ions from a complete hydration layer to complete desolvation, recording the energy changes at each step and plotting energy barrier curves, precisely selecting the optimal desolvation path with the lowest energy barrier, and clarifying the rate-determining step and energy barrier bottleneck of the desolvation process. In practical applications, the method described in this invention can quantify the desolvation energy barrier of different electrolyte systems in advance and accurately predict the ion migration efficiency and rate performance of the electrolyte. When problems such as poor rate performance and sluggish interfacial kinetics occur in the experiment, it can quickly locate the energy barrier bottleneck in the desolvation process and provide targeted guidance to the experimenters to reduce the energy barrier of the optimal desolvation path by adjusting the type of anion, introducing functional additives, and optimizing the electrolyte concentration. This achieves a targeted improvement in the rate performance and ionic conductivity of the electrolyte, solving the technical problem of "no direction for performance optimization and only blind trial and error" in the existing battery research and development experiments.

[0029] 3. The method described in this invention establishes a multi-dimensional, full-chain analysis framework, providing comprehensive theoretical support for the design of electrolytes with wide temperature ranges and high stability through the correlation between microstructure and macroscopic performance. This invention constructs a multi-dimensional analysis system encompassing solvation thermodynamics, electronic state evolution, and dynamic desolvation. It can not only analyze the coordination structure and solvation energy of hydrated cells but also, by combining electronic structure information such as electrostatic potential and frontier orbital levels, correlate the hydrated cell structure with key performance parameters such as the evolution of the hydrogen bond network, thermal stability, and hydrogen evolution side reactions in the electrolyte, thus realizing the crucial link of "microscopic hydration structure - dynamic desolvation behavior - macroscopic electrolyte performance." This allows the method described in this invention to predict the wide-temperature-range stability, thermal stability, and electrochemical window of the electrolyte in advance during actual battery research and development tests, avoiding problems such as poor low-temperature performance, short cycle life, and severe hydrogen and oxygen evolution side reactions that may occur during experiments. At the same time, it can also clearly explain the microscopic mechanism of the performance differences observed in the experiment, such as clarifying that the low-temperature performance degradation of the electrolyte is due to excessively strong hydration cell binding and a significant increase in the solvation energy barrier at low temperatures. This guides researchers to directionally regulate the hydration cell structure and design high-performance electrolyte systems that meet the requirements of wide temperature range and long cycle life.

[0030] 4. The method described in this invention can significantly reduce R&D costs, promoting the transformation of electrolyte R&D from an "experience-based trial-and-error" approach to a "theory-guided" approach. The complete simulation system of this invention enables proactive prediction of electrolyte performance and precise identification of optimization directions, eliminating the need for extensive material synthesis, electrolyte preparation, and electrochemical testing experiments, thus significantly reducing the consumption of reagents, consumables, and equipment time. Furthermore, the standardized operating procedures of the method can be extended to the R&D of electrolytes with different sodium salt systems and additive systems, providing an efficient and reliable theoretical tool and design guidance for the industrial development of aqueous sodium-ion battery electrolytes. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the overall process of the screening method described in this invention.

[0032] Figure 2 The three concentrations (m is mol / kg) of the aqueous NaOTF electrolyte model in Example 1 are shown; where a is a concentration of 1 mol / kg, b is a concentration of 6 mol / kg, and c is a concentration of 10 mol / kg.

[0033] Figure 3 The radial distribution function and coordination number of various interatomic spacings in the NaOTF electrolyte in Example 1 are given; where a is the Na-O... W b is Na-O S c is HO W d is O W -O W .

[0034] Figure 4 The stable hydrated cell model obtained in Example 1; where a is SIP, b is CIP, and c and d are AGG.

[0035] Figure 5 This is a model of the desolvation process of different types of ion hydrates in the NaOTF electrolyte in Example 1; where a is [Na(H2O)6]. + b is [NaOTF(H2O)5], and c is [NaOTF2(H2O)4]. - d is [NaOTF3(H2O)3] 2- .

[0036] Figure 6 This refers to the energy required for the NaOTF electrolyte ion hydrate cells to remove water molecules in Example 1. Detailed Implementation

[0037] The technical solutions of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the present invention are within the scope of protection of the present invention.

[0038] Unless otherwise specified in the specific circumstances, the numerical ranges listed herein include upper and lower limits, as well as all integers and fractions within that range, but are not limited to the specific values ​​listed when the range is defined.

[0039] I. A method for screening the desolvation pathway of hydrated cells in aqueous sodium-ion battery electrolytes

[0040] In the development of aqueous sodium-ion battery electrolytes, existing theoretical simulation techniques cannot accurately reproduce the real electrolyte environment, are difficult to quantify the dynamic process of ion desolvation, and have a serious disconnect between microstructure and macroscopic performance. This results in large deviations between theoretical predictions and experimental results, and cannot effectively guide the directional design of electrolytes. The development process is highly dependent on experience-based trial and error, and is characterized by long cycles and high costs. This invention addresses these core problems. First, existing technologies generally suffer from the following problems: First, they construct single hydrated cell models by artificially assembling anions, cations, and water molecules. This design can only obtain a preset static stable configuration, which cannot reproduce the real state of multiple solvation modes such as SIP, CIP, and AGG coexisting due to changes in concentration and temperature in actual electrolytes, nor can it capture transient dynamic behaviors such as ion coordination and solvent molecule exchange. Second, they can only calculate single-point parameters such as binding energy based on static configurations, which cannot fully characterize the dynamic process of desolvation, make it difficult to quantify the desolvation energy barrier and optimal path, and cannot explain the microscopic mechanisms of differences in electrolyte rate performance and ion transport efficiency. Third, the simulation system is completely detached from real experimental conditions, and cannot provide practical experimental guidance for electrolyte formulation optimization and performance improvement, resulting in electrolyte development relying on long-term blind trial and error.

[0041] To address the aforementioned technical problems, this invention considers solutions from multiple perspectives: First, based on real experimental conditions, a periodic full electrolyte system model is constructed that perfectly matches the experimental concentration and environment, abandoning the traditional modeling method of isolated clusters. Second, with dynamic equilibrium as the core, multi-scale simulations are used to capture the real dynamic evolution behavior of the electrolyte system through classical MD in the NPT ensemble and ab initio AIMD calculation in the NVT ensemble, extracting real steady-state hydrated cells from the equilibrium trajectory. Third, with full-chain quantification as the goal, a simulation system for the entire desolvation process is built to accurately screen the optimal desolvation path with the lowest energy barrier, realizing the correlation between microstructure and macroscopic performance. Based on this, this invention forms a set of desolvation path screening methods for steady-state ion hydrated cells in aqueous sodium-ion battery electrolytes based on first principles and multi-scale molecular dynamics, constructing a complete analytical process of "full system model construction and thermodynamic equilibrium - dynamic evolution simulation of hydrated cells - accurate extraction and optimization of steady-state hydrated cells - quantitative screening of desolvation paths". The specific steps are as follows:

[0042] Step 1: Construct and optimize the structural models of anions and cations in the electrolyte solution and the aqueous sodium ion electrolyte system model:

[0043] Step 2: Simulate the evolution of ion hydrate cell structure in the electrolyte;

[0044] Step 3: Extract and optimize steady-state ion hydrate cells from the simulation results of Step 2;

[0045] Step 4: Analyze the desolvation path of the steady-state ion hydrate obtained in Step 3 to obtain the optimal path for the desolvation of the steady-state ion hydrate.

[0046] The method described in this invention has achieved unexpected technical effects in practical applications: it not only completely solves the technical problem of the disconnect between traditional models and real electrolyte environments, but also accurately identifies and statistically analyzes the proportion of different solvation modes, restores the dynamic evolution of hydrated cell structures, and significantly improves the reliability of theoretical predictions; it also realizes the dynamic quantification of the entire desolvation process and the precise locking of the optimal path, clarifies the energy barrier bottleneck of desolvation, and realizes the correlation chain between microscopic hydration behavior and macroscopic electrolyte performance; furthermore, it can provide preliminary formulation optimization directions and performance predictions for actual experiments, greatly reduce blind trial-and-error experiments, lower R&D costs, upgrade electrolyte R&D from experience-based trial-and-error to theory-guided, and provide an efficient and reliable standardized tool for the industrial development of high-performance aqueous sodium-ion battery electrolytes.

[0047] In some embodiments of this invention, existing technologies often directly use unoptimized initial structures for subsequent assembly and calculations when constructing ion-solvent models, without considering pre-optimization of the monomer structure. This leads to problems such as unreasonable geometric parameters like bond lengths and bond angles, and steric hindrance mismatch in the initial configuration. Consequently, the ion-solvent interactions and structural energies obtained from subsequent calculations contain significant errors and cannot accurately reflect the true interaction state between ions and solvent molecules. This invention pre-constructs Na... (The sentence is incomplete and requires further context to be fully translated). + X - The initial structural models of ions and water molecules were geometrically optimized using quantum chemical calculations to obtain the most stable structural model. By performing preliminary geometric optimization on the monomer structures of ions and water molecules, unreasonable structural deviations in the initial configuration were eliminated beforehand, resulting in monomer models with the lowest energy and most stable structures. This ensured the structural rationality and computational accuracy of the subsequent electrolyte system and hydrated cell model from the outset, avoiding distortion of subsequent simulation results due to monomer structural defects. This laid a solid foundation for accurately reproducing the true coordination interactions between ions and solvents, and between cations and anions. Therefore, in step 1:

[0048] The first step is to construct anionic and cation structural models respectively: a sodium salt NaX is pre-constructed (where X... - It is an anion, including but not limited to OTF. - ClO4 - The electrolyte commonly used in aqueous sodium-ion batteries is Na+ formed by dissolving the electrolyte in water. + X - The initial structural models of ions and water molecules were geometrically optimized using quantum chemical calculations to obtain the most stable structural model.

[0049] The second step is to construct an initial electrolyte model: Based on the most stable structural model obtained in the first step, an initial electrolyte system is constructed. Specifically, the NaX / H2O molecule ratio is set according to the concentration used in the experiment to ensure that the ion concentration or electrolyte density remains consistent with the experimental conditions. The electrolyte system model employs periodic boundary conditions, and quantum chemical calculations are used to optimize the structure of the initial electrolyte system model, thereby obtaining the most stable electrolyte system model.

[0050] In some embodiments of the present invention, in step 2:

[0051] The first step involved classical molecular dynamics (MD) simulations using the NPT ensemble: The electrolyte density was balanced to within the experimental range to ensure thermodynamic equilibrium and a stable ion hydrate cell configuration. The final equilibrium electrolyte system configuration and average cell parameters were extracted as initial input for ab initio molecular dynamics (AIMD) simulations. Through classical MD simulations using the NPT ensemble, under fixed particle number, pressure, and temperature conditions, full thermodynamic equilibrium of the electrolyte system was achieved. This ensured that macroscopic parameters such as density, temperature, and pressure were completely consistent with actual experiments, guaranteeing that the extracted hydrate cell configuration was a real, stable thermodynamic steady-state configuration existing in the actual electrolyte, rather than an artificially predetermined non-equilibrium structure. Simultaneously, this provided reasonable and reliable initial input for subsequent high-precision AIMD simulations, fundamentally avoiding distortion of subsequent simulation results caused by initial system imbalances.

[0052] The second step involves AIMD simulation using an NVT ensemble: Based on DFT simulation calculations, the electronic structure of the electrolyte system is obtained, and the interatomic interactions are calculated in real time. This allows for the spontaneous capture of various transient behaviors occurring in the electrolyte (e.g., anion coordination, solvent molecule exchange, ion diffusion, chemical bond breaking, electron transfer, etc.), simulating the dynamic evolution of ion hydrates in an aqueous sodium ion electrolyte system. This significantly improves the reliability of predicting electrolyte structure and performance. Employing DFT-based ab initio molecular dynamics simulations, the electronic structure and interatomic interactions of the system are calculated in real time without relying on empirical force fields. This spontaneously and accurately captures various short-term transient behaviors in the electrolyte, completely reconstructing the entire dynamic evolution of ion hydrates. This not only significantly improves the reliability of predicting electrolyte microstructure and macroscopic performance but also provides core and reliable dynamic data support for subsequent extraction of real steady-state hydrates and elucidation of the microscopic mechanism of ion solvation.

[0053] The third step is the analysis of the coordination state of hydrated ions: The radial distribution function of the oxygen atoms of sodium ions and water molecules, and the oxygen atoms of anions, is calculated and integrated to obtain the coordination numbers of sodium ions and anions within the first solvation shell. Specifically, the coordination number of sodium ions with water molecules ranges from 3 to 6, and the coordination number of anions ranges from 0 to 3. Through quantitative calculation and integration of the radial distribution function, the range of the first solvation shell of sodium ions is accurately defined, clarifying the actual coordination state and coordination number range of sodium ions. This avoids model bias caused by artificially preset coordination numbers, providing precise quantitative evidence for subsequent accurate extraction of steady-state hydrated cells and analysis of desolvation pathways. This ensures that all subsequent analyses are conducted around the actual hydration layer structure, fundamentally guaranteeing the accuracy and rationality of the analytical results.

[0054] In some embodiments of the present invention, in step 3:

[0055] The first step is to extract the sodium ion hydrate cell model: After the simulated electrolyte reaches its final equilibrium state in step 2, various basic sodium ion hydrate cell morphologies are initially formed in the electrolyte. Further, combining the equilibrium trajectory reached by the dynamic evolution of the ion hydrate cells simulated by AIMD in step 2, the visualization software VMD is used to extract the ion hydrate cells. This invention considers directly extracting spontaneously formed multiple sodium ion hydrate cell morphologies from the electrolyte phase system that has reached thermodynamic equilibrium under the same experimental conditions, based on the dynamic equilibrium trajectory. This ensures that the extracted hydrate cells are real and stable structures existing in the actual electrolyte, and at the same time, it can obtain multiple coexisting hydrate cell configurations in the electrolyte, solving the core shortcoming of traditional modeling that can only obtain a single preset configuration.

[0056] The second step is to optimize the ion hydrated cell model: The ion hydrated cell model extracted in the first step is geometrically optimized and its frequency calculated to obtain an optimized steady-state ion hydrated cell model. The steady-state ion hydrated cell construction scheme implemented in this invention significantly improves the effectiveness and information diversity of the hydrated cell structure compared to commonly used existing schemes. Existing technologies typically construct models of all components involved in the ion hydrated cell simultaneously, such as anions, cations, and water molecules, simply assembling them to form a single hydrated cell configuration. The lowest energy condition is then simulated and selected as the steady-state hydrated cell. This design of a steady-state hydrated cell cannot reflect the evolution of multi-ion solvation modes, hydrated cell geometry, and the types and numbers of coordinating elements caused by ion concentration, temperature, and other stimuli in the solution. This invention is based on the kinetic simulation of the electrolyte system reaching steady state, followed by extraction and further optimization of the ion hydrated cell model according to the AIMD trajectory. This allows for the identification of multiple solvation modes such as SIP, CIP, and AGG, and also enables dynamic evolution analysis of the ion hydrated cell structure, closely reflecting the ion hydration kinetics in real solution environments.

[0057] The third step is steady-state hydrated cell energy analysis: based on the electrostatic potential distribution, the polarity characteristics of the surface charge of molecules or ions are obtained. Simultaneously, the highest occupied molecular orbitals and the lowest unoccupied molecular orbitals in the hydrated cell are analyzed. The solvation energy represents the strength of the interaction between the solute sodium ions and solvent molecules, thereby describing the relevant energy changes in the solvation process. This invention constructs a multi-dimensional energy and electronic structure analysis system of "electrostatic potential-frontier orbitals-solvation energy," which can not only quantitatively characterize the interaction strength between sodium ions and solvent molecules through solvation energy, but also clarify the coordination sites and interaction nature through electrostatic potential analysis, and clarify the redox stability of the hydrated cell through frontier orbital analysis. It achieves a full-dimensional analysis from the electronic to the energy level, revealing the intrinsic mechanism of solvation. Furthermore, it can directly correlate the hydrated cell structure with the macroscopic electrochemical performance of the electrolyte, providing comprehensive theoretical support for subsequent explanations of the impact of desolvation behavior on electrolyte performance and guiding the targeted optimization of the electrolyte.

[0058] In some embodiments of the present invention, in step 4:

[0059] The first step, the desolvation process of the ion hydrate cell: Intermolecular forces within the ion hydrate cell are calculated, the weakest interacting solvent molecules are identified and removed, and then the geometry is optimized. The changes in the distance between the remaining atoms in this optimized ion hydrate cell are measured. Then, by iterating the above process, solvent molecules are gradually removed until only sodium ions remain, at which point complete desolvation can be considered achieved. This step enables a step-by-step, precise simulation of the entire desolvation process, clarifying the preferential removal order of solvent molecules and the evolution of coordination structures at each step. This overcomes the limitation of traditional methods that can only calculate the total desolvation energy. It can accurately identify the solvent molecules with the weakest interaction with sodium ions and the preferential desolvation sites in the hydrate cell, completely reconstructing the entire microscopic process of sodium ions from a complete hydration layer to complete desolvation, providing complete and continuous process data for subsequent screening of the optimal desolvation path and quantification of the desolvation energy barrier.

[0060] The second step is the screening of the desolvation path for ion hydrated cells: Simulating the process of sodium ions from having a complete hydration layer to complete dehydration through the iterative process in the previous step, recording the energy difference at each step and plotting the desolvation energy barrier curve, the lowest energy path of the desolvation process is screened, which is the optimal path for steady-state ion hydrated cell desolvation. This invention achieves precise screening of the optimal desolvation path and quantitative characterization of the energy barrier throughout the process, clarifying the rate-determining steps and energy barrier bottlenecks of the desolvation process, and thoroughly breaking down the intrinsic relationship between the microscopic behavior of desolvation and the macroscopic rate performance and ion transport efficiency of the electrolyte. It can provide precise and feasible optimization directions for actual experiments, guiding researchers to directionally regulate the hydrated cell structure by adjusting the type of anion, electrolyte concentration, and introducing additives, thereby reducing the energy barrier of the optimal desolvation path and achieving targeted improvement in electrolyte rate performance and ion transport performance. This fundamentally solves the technical problem of directionless performance optimization and reliance on blind trial and error in traditional electrolyte development.

[0061] II. Examples and Comparative Examples

[0062] Figure 1 This is a schematic diagram of the overall process of the present invention. The following embodiments will build a model and perform calculations according to this process.

[0063] Example 1: Screening of pathways for desolvation of steady-state ion hydrate cells in NaOTF electrolyte

[0064] (1) Construction and optimization of ion model

[0065] Establish the Na+ electrolyte sodium salt NaOTF aqueous solution dissolution process. + and OTF - The initial atomic coordinate model of the ion was established. First-principles calculations were used to geometrically optimize the model. The optimization calculations were performed using ORCA software with a B3LYP-D3(BJ) mixed functional and the def2-TZVP basis set. The strict convergence criterion of gradient <0.0003 Hartree / Bohr was used. Anion dispersion functionality was added. An SMD implicit solvent model was used to simulate the solution environment of the hydrated cell. Forced SCF convergence was implemented to avoid calculation failures due to oscillations. The optimized structure was verified as a potential energy surface minimum, thus obtaining the optimized ion model.

[0066] (2) Initial model construction and optimization of electrolyte

[0067] An initial electrolyte solution model was constructed using the molecular dynamics software Packmol, based on the optimized ionic model from the previous step. The molar ratio of sodium salt to solvent molecules was set, and a periodic three-dimensional box was used as the boundary condition. The initial density was set to be close to the experimental values ​​(1.1–1.4 g / cm³), thus obtaining the initial electrolyte model. Subsequently, CP2K software was used for optimization. The BLYP exchange correlation function was used, supplemented by Grimme DFT-D3 empirical dispersion correction, to describe quantum electron interactions. The Goedecker-Teter-Hutter (GTH) pseudopotential was used to treat the core electrons. Molott-DZVP-SR-GTH basis set optimization was applied to all atoms, with the Planewave cutoff set to 400 Ry and the relative cutoff set to 60 Ry. A 10... -6 The target accuracy is used for SCF calculation convergence, with the convergence settings being a maximum atomic force ≤ 0.00045 Hartree / Bohr and a maximum atomic displacement ≤ 0.003 Bohr. This ultimately yields an optimized electrolyte system model.

[0068] (3) Molecular dynamics (MD) simulation of electrolyte system

[0069] Molecular interactions were simulated using the NPT ensemble and characterized by the COMPASS III all-atom force field model. Self-consistent convergence of the system was achieved through an energy gradient optimization algorithm, with an accuracy requirement of 10-1. -6 Hartree; The total energy convergence threshold of the system is strictly controlled at 10. -5 Hartree values ​​were used to simultaneously satisfy the conditions of atomic force gradient ≤ 0.002 Hartree / Å and atomic displacement change ≤ 0.005 Å. The electrolyte density was balanced to within the experimental range to ensure the system reached thermodynamic equilibrium, thus achieving a stable model configuration. The final equilibrium configuration and average cell parameters were extracted as the initial input model and parameters for subsequent ab initio molecular dynamics (AIMD) simulations.

[0070] (4) Ab initio molecular dynamics (AIMD) simulation of electrolyte system

[0071] AIMD simulations were performed using the NVT ensemble and the QUICKSTEP module from the CP2K software package. The BLYP exchange correlation function and Grimme DFT-D3 empirical dispersion correction were used to describe quantum electron interactions. The core electrons were treated with a Goedecker-Teter-Hutter (GTH) pseudopotential. All atoms were optimized using the Molott-DZVP-SR-GTH basis set, with a Planewave cutoff of 750 Ry. The highest grid level of five Multigrids was applied, with a relative cutoff of 70 Ry. A 10-bit multigrid was used.-6 The target accuracy was used for SCF calculation convergence. The simulation temperature was 298.15 K, and the time step was 0.5 fs. In the first 4 ps stage, high-energy fluctuations in the initial configuration of the electrolyte system were eliminated to bring the electrolyte system to equilibrium; the following 30 ps was the data production stage, totaling 68,000 time steps, in which model information was saved in each simulation step.

[0072] (5) Extraction and optimization of ion hydrated cell model

[0073] First, based on the dynamic trajectory information of the steady-state electrolyte system obtained 30 ps after AIMD in the previous step, the type and basic morphology of sodium ion hydrate cells can be analyzed and determined, including the type and number of coordinating molecules, the interatomic spacing within the hydrate cell, and the geometric structure of the cell.

[0074] Secondly, based on the aforementioned AIMD equilibrium trajectory, hydrated cells were extracted using VMD visualization software to obtain initial models of various types of hydrated cells.

[0075] Subsequently, first-principles calculations were performed on the initial hydrated cell using ORCA software. A B3LYP-D3(BJ) mixed functional was selected, combined with the def2-TZVP basis set. The strict convergence criterion of gradient <0.0003 Hartree / Bohr was used by default. Anion dispersion was added. An SMD implicit solvent model was used to simulate the solution environment of the hydrated cell. SCF convergence was forced to avoid calculation failure due to oscillation. The optimized structure was verified to be the potential energy surface minimum point, and the optimized stable ion hydrated cell model was obtained.

[0076] (6) Screening of desolvation pathways for ion hydrated cells

[0077] The desolvation process of ion hydrates is one of the key steps in ion transport, and its efficiency directly affects the ionic conductivity and diffusion coefficient of the electrolyte. Identifying and screening the desolvation pathway of ion hydrates is crucial for electrolyte design and performance optimization.

[0078] Based on the results of the aforementioned steps, it was determined that the NaOTF electrolyte mainly contains [Na(H2O)6]. + , [NaOTF(H2O)5], [NaOTF2(H2O)4] - and [NaOTF3(H2O)3] 2- Four types of sodium ion hydrated cells. The changes in the solvation free energy of the four types of hydrated cells indicate that, with the increase of OTF... - The introduction of anions makes the solvation process more likely to occur spontaneously, and the stability of the solution is further improved. However, OTF - An increase in anions leads to a stronger solubilizing ability, making it easier for sodium ions to react with OTF. -Anions form stable ion pairs, reducing the concentration of free ions and thus limiting the conductivity of the electrolyte. Therefore, it is necessary to determine the desolvation ability of different sodium ion hydrates and screen for effective desolvation pathways.

[0079] By calculating the intermolecular forces within each ion hydrate cell to identify the weakest water molecule, removing this water molecule from the hydrate cell, and then performing geometric optimization, a new ion hydrate cell is obtained. This process is repeated until only sodium ions remain. This is the desolvation process. The energy difference at each step of removing the coordinating molecule from the hydrate cell is recorded, and a desolvation energy barrier curve is plotted. The lowest energy path for the desolvation process is selected, which is the optimal path for steady-state ion hydrate cell desolvation. Removing water molecules from the hydrate cell not only alters the original hydrate cell structure but also changes the interaction forces of the remaining coordinating molecules, affecting subsequent desolvation steps. OTF - Because of its relatively large size, the ion acts as a coordinating molecule in the sodium ion hydrate cell, compressing the space occupied by the remaining coordinating water molecules. This results in a more tightly bound hydrated water molecule structure, thus containing multiple OTFs. - It becomes increasingly difficult to remove water molecules from the hydrated cells of ions, which limits the desolvation of sodium ions and affects the ion transport capacity of the electrolyte.

[0080] Figure 2 Models of aqueous NaOTF electrolytes with three concentrations (m = mol / kg) are provided. The NaOTF electrolyte contains H₂O and Na₂O. + OTF - Three types of molecules. As the solution concentration increases, Na... + and OTF - An increase in the number of ions and their more dense distribution will affect the structure of sodium ion hydrates, as well as the types and number of coordinating molecules.

[0081] Figure 3 The figure shows the radial distribution function and coordination number of various interatomic spacings in the NaOTF electrolyte. In the figure, O... W and O S They represent water molecules and anions (OTF), respectively. - The results showed that at a low concentration of 1 m, the ion hydrated cell was mainly composed of a "water-encapsulated salt" structure, with sodium ions at the center and water molecules dominating the solvation layer. However, at a higher concentration of 10 m, the number of ions increased significantly, and the ratio of water molecules to ions decreased significantly. The ion hydrated cell gradually transitioned to a "salt-encapsulated water" structure. The anions and cations broke the hydrogen bond network formed by the water molecule-dominated topology in the low-concentration electrolyte and instead injected a large amount into the hydrogen bond network of the water body to jointly construct the solvation network. This can reduce the content of free water, thereby inhibiting water decomposition and broadening the electrochemical window.

[0082] Table 1 summarizes... Figure 3 The coordination configuration of various atoms in NaOTF electrolytes of different concentrations. At low electrolyte concentrations, the anions do not participate in the hydration of sodium ions, resulting in a Na-O configuration. S The spacing is much larger than the size of the hydrated cell (see Na-O). W (Interval), no coordinated O in sodium ion hydrates. S Atoms, i.e., OTF - Not in sodium ion hydrate cells. As electrolyte concentration increases, Na⁻¹O⁻… S The coordination number (denoted by CN) gradually increases to 1.76 (6 m) and even 2.33 (10 m). This indicates that in high-concentration electrolytes, [NaOTF(H2O)5] contact ion pairs will form, and more anions will participate in the formation of [NaOTF2(H2O)4] in the hydrated cells. - Even [NaOTF3(H2O)3] 2- Ion aggregates.

[0083] Table 1. Atomic coordination of NaOTF electrolytes at different concentrations

[0084]

[0085] Note: HO W The coordination case for O:H nonbonded structures.

[0086] Figure 4 This is the stable hydrated cell model obtained in Example 1. Through systematic analysis of AIMD trajectories, statistically representative hydrated cell models were extracted from aqueous sodium ion electrolytes of different concentrations. The typical solvation configurations (SIP / CIP / AGG) of Na+ at different concentrations and their distribution probabilities were quantitatively characterized. This hydrated cell model provides a precise structural basis for revealing solvation kinetics, the desolvation barrier of hydrated cells, and ion transport mechanisms, supporting the rational design of high-performance aqueous battery electrolytes.

[0087] Table 2 shows the water molecules and OTF. - The HOMO and LUMO values ​​of various types of hydrated cells were also observed. Compared with water molecules, sodium ion hydrated cells have a slightly smaller band gap, indicating that the energy required for electron excitation is lower. This reflects that water molecules are more likely to participate in interfacial charge transfer reactions after solvation, i.e., they are more prone to water splitting side reactions.

[0088] Table 2 Water molecules, OTF - and HOMO and LUMO of hydrated cells

[0089]

[0090] Table 3 lists the thermodynamic performance parameters of various types of hydrated cells. The solvation free energy ∆G(sol) of the electrolyte can represent the spontaneity of the solvation process. OTF - The introduction of [a specific substance] increases the absolute value of ∆G(sol), indicating that hydration is more likely to occur spontaneously. Simultaneously, the increase in entropy also indicates enhanced order in the hydration system, leading to the formation of stable ion pairs through strong coordination. OTF - The increase in ion concentration further improves the stability of the electrolyte, but the increase in ion concentration will reduce conductivity and ion mobility.

[0091] Table 3 Thermodynamic properties of hydrated cells

[0092]

[0093] Figure 5 The diagram illustrates the desolvation process model for different types of ion hydrates in NaOTF electrolyte. [Na(H₂O)₆] + For example, we calculate the intermolecular forces within the ion hydrate cell and identify and remove the weakest interacting solvent molecules. As water molecules are continuously removed from the ion hydrate cell, the coordination number of water molecules decreases, breaking the original octahedral symmetry and forming a low-symmetry coordination configuration. At the same time, the hydration layer becomes thinner and solvation weakens.

[0094] Figure 6 The energy required for four types of ion-dependent hydrated cells to remove water molecules was compared. OTF in hydrated cells. - Ions partially neutralize the positive charge of sodium ions, weakening the electrostatic attraction between water molecules and sodium ions in the hydrated cell. However, OTF... - Because ions are relatively large, occupying a hydration site in the hydration layer restricts the ideal geometric arrangement of remaining hydrated water molecules and reduces their coordination, resulting in a tighter bond between hydrated water molecules. Therefore, ions containing multiple OTFs... - Hydrated cells of ions [NaOTF2(H2O)4] - and [NaOTF3(H2O)3] 2- Removing water molecules from the medium is becoming increasingly difficult.

[0095] Meanwhile, experiments were used to verify the simulation results of Example 1: when the concentration of the aqueous NaOTF electrolyte increased from 1 mol / kg to 9 mol / kg, the Na in the solution... + The ionic conductivity initially increases with increasing concentration (1→6 mol / kg) (~53→98 mS cm⁻¹). -1 However, when the salt concentration increased from approximately 6 mol / kg to 9 mol / kg, the conductivity continued to decrease (98→82 mS / cm). -1This is mainly because high concentrations induce the formation of ion pairs (CIPs) and aggregates (AGGs), as well as an increase in solution viscosity. This is consistent with the results shown in Tables 1 and 3 provided after simulation using the path screening method described in Example 1. Figure 6 The results shown indicate that "the solution concentration increased, and the hydrated cells contained OTF." - As the number of ions increases, it becomes more difficult to remove water molecules from hydrated cells, which is consistent with the reduction of ion mobility and conductivity of the solution. This further proves the reliability of the path screening method described in this invention.

[0096] Example 2: Pathway screening for desolvation of steady-state ion hydrate cells in NaClO4 electrolyte

[0097] (1) Construction and optimization of ion model:

[0098] Establishment of electrolyte sodium salt NaClO4 aqueous solution dissolution of Na + and ClO4 - The initial atomic coordinate model of the ion was established. First-principles calculations were used to geometrically optimize the model. The optimization calculations were performed using ORCA software with a B3LYP-D3(BJ) mixed functional and the def2-TZVP basis set. The strict convergence criterion of gradient <0.0003 Hartree / Bohr was used. Anion dispersion functionality was added. An SMD implicit solvent model was used to simulate the solution environment of the hydrated cell. Forced SCF convergence was implemented to avoid calculation failures due to oscillations. The optimized structure was verified as a potential energy surface minimum, thus obtaining the optimized ion model.

[0099] (2) Initial electrolyte model construction and optimization:

[0100] An initial electrolyte solution model was constructed using the molecular dynamics software Packmol, based on the optimized ionic model from the previous step. The molar ratio of sodium salt to solvent molecules was set, and a periodic three-dimensional box was used as the boundary condition. The initial density was set to be close to the experimental values ​​(1.1–1.4 g / cm³), thus obtaining the initial electrolyte model. Subsequently, CP2K software was used for optimization. The BLYP exchange correlation function was used, supplemented by Grimme DFT-D3 empirical dispersion correction, to describe quantum electron interactions. The Goedecker-Teter-Hutter (GTH) pseudopotential was used to treat the core electrons. Molott-DZVP-SR-GTH basis set optimization was applied to all atoms, with the Planewave cutoff set to 400 Ry and the relative cutoff set to 60 Ry. A 10... -6 The target accuracy is used for SCF calculation convergence, with the convergence settings being a maximum atomic force ≤ 0.00045 Hartree / Bohr and a maximum atomic displacement ≤ 0.003 Bohr. This ultimately yields an optimized electrolyte system model.

[0101] (3) Classical molecular dynamics (MD) simulation of electrolyte system

[0102] Molecular interactions were simulated using the NPT ensemble and characterized by the COMPASS III all-atom force field model. Self-consistent convergence of the system was achieved through an energy gradient optimization algorithm, with an accuracy requirement of 10-1. -6 Hartree; The total energy convergence threshold of the system is strictly controlled at 10. -5 Hartree values ​​were used to simultaneously satisfy the conditions of atomic force gradient ≤ 0.002 Hartree / Å and atomic displacement change ≤ 0.005 Å. The electrolyte density was balanced to within the experimental range to ensure the system reached thermodynamic equilibrium, thus achieving a stable model configuration. The final equilibrium configuration and average cell parameters were extracted as the initial input model and parameters for subsequent ab initio molecular dynamics (AIMD) simulations.

[0103] (4) Ab initio molecular dynamics (AIMD) simulation of electrolyte system

[0104] AIMD simulations were performed using the NVT ensemble and the QUICKSTEP module from the CP2K software package. The BLYP exchange correlation function and Grimme DFT-D3 empirical dispersion correction were used to describe quantum electron interactions. The core electrons were treated with a Goedecker-Teter-Hutter (GTH) pseudopotential. All atoms were optimized using the Molott-DZVP-SR-GTH basis set, with a Planewave cutoff of 750 Ry. The highest grid level of five Multigrids was applied, with a relative cutoff of 70 Ry. A 10-bit multigrid was used. -6 The target accuracy was used for SCF calculation convergence. The simulation temperature was 298.15 K, and the time step was 0.5 fs. In the first 4 ps stage, high-energy fluctuations in the initial configuration of the electrolyte system were eliminated to bring the electrolyte system to equilibrium; the following 30 ps was the data production stage, totaling 68,000 time steps, in which model information was saved in each simulation step.

[0105] (5) Extraction and optimization of ion hydrated cell model

[0106] First, based on the dynamic trajectory information of the steady-state electrolyte system obtained 30 ps after AIMD in the previous step, the type and basic morphology of sodium ion hydrate cells can be analyzed and determined, including the type and number of coordinating molecules, the interatomic spacing within the hydrate cell, and the geometric structure of the cell.

[0107] Secondly, based on the aforementioned AIMD equilibrium trajectory, hydrated cells were extracted using VMD visualization software to obtain initial models of various types of hydrated cells.

[0108] Subsequently, first-principles calculations were performed on the initial hydrated cell using ORCA software. A B3LYP-D3(BJ) mixed functional was selected, combined with the def2-TZVP basis set. The strict convergence criterion of gradient <0.0003 Hartree / Bohr was used by default. Anion dispersion was added. An SMD implicit solvent model was used to simulate the solution environment of the hydrated cell. SCF convergence was forced to avoid calculation failure due to oscillation. The optimized structure was verified to be the potential energy surface minimum point, and the optimized stable ion hydrated cell model was obtained.

[0109] (6) Screening of desolvation pathways for ion hydrated cells

[0110] Based on the results of the aforementioned steps, it was determined that the NaClO4 electrolyte mainly contains [Na(H2O)6]. + , [NaClO4(H2O)5], [Na(ClO4)2(H2O)4] - and [Na(ClO4)3(H2O)3] 2- Four types of sodium ion hydrates were identified. The weakest water molecule was identified by calculating the intermolecular forces within each hydrate. After removing this water molecule, geometric optimization was performed to obtain a new hydrate. This process was repeated until only sodium ions remained. This is the desolvation process. The energy difference at each step of removing the coordinating molecule from the hydrate was recorded, and a desolvation energy barrier curve was plotted. The lowest energy path for desolvation was selected, representing the optimal path for steady-state hydrate desolvation. The energy required for desolvation of the four sodium ion hydrates did not change monotonically with the number of water molecules removed. At low coordination, the loose water molecule structure resulted in a lower desolvation energy barrier, while at high coordination, the strong electrostatic interaction and steric hindrance of ClO4⁻ caused the desolvation energy barrier to rise. Based on the desolvation path, it is optimal that the coordination number of ClO4⁻ in the hydrate does not exceed 2, which effectively broadens the electrochemical window and maintains good ion migration performance.

[0111] Comparative example:

[0112] When studying ion hydrate cells in NaOTF electrolyte using traditional techniques, the first step is usually to establish independent Na... + Ions, OTF - Ion and pure water molecule models were used, and then Na+ was individually inserted into the pure water model containing 64 water molecules (2×2×2 unit cells) based on different concentration requirements. + Or OTF - The number of ions, forming Na + or OTF - Solution from which Na is obtained + or OTF -Each ion hydrate cell is a SIP type. While this approach can quickly obtain a typical ion hydrate cell structure model and analyze local hydrogen bonding behavior in a small-scale model, the selection of the ion hydrate cell does not truly reflect the actual impact of changes in solution concentration. This is because as the solution concentration and the number of ions increase, ions may form in the solution due to changes in Na+. + OTF - Ion hydrate cells, including anion-anion, cation-cation, and anion-cation combinations, form CIP and AGG forms. This demonstrates that traditional ion hydrate cell characterization schemes cannot accurately describe complex ion hydrate cells. Furthermore, traditional techniques completely fail to identify Na+ when simulating ion hydrate cell behavior in NaOTF electrolytes of different concentrations. + - Na + Na + -OTF - OTF - -OTF - Even with various solvation modes, it is impossible to obtain a wealth of behavioral information about ion hydrates, such as the proportional evolution of each solvation mode with changes in solution concentration, hydrate geometry, hydrogen bond relaxation, and electron transfer.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Those skilled in the art should understand that any modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for screening desolvation pathways of hydrated cells in aqueous sodium-ion battery electrolytes, characterized in that, Includes the following steps: Step 1: Construct and optimize the structural models of anions and cations in the electrolyte solution and the aqueous sodium ion electrolyte system model: Step 2: Simulate the evolution of ion hydrate cell structure in the electrolyte; Step 3: Extract and optimize steady-state ion hydrate cells from the simulation results of Step 2; Step 4: Analyze the desolvation path of the steady-state ion hydrated cell obtained in Step 3 to obtain the optimal path for the desolvation of the steady-state ion hydrated cell.

2. The method for screening the desolvation pathway of aqueous sodium-ion battery electrolyte hydrates according to claim 1, characterized in that, In step 1: The first step is to construct anionic and cation structural models respectively: Na is formed by dissolving sodium salt NaX electrolyte in water. + X - The initial structural models of ions and water molecules were geometrically optimized using quantum chemical calculations to obtain the most stable structural model. The second step is to construct an initial electrolyte model: based on the most stable structural model obtained in the first step, an initial electrolyte system is constructed.

3. The method for screening the desolvation pathway of aqueous sodium-ion battery electrolyte hydrates according to claim 2, characterized in that, In the second step of constructing the initial electrolyte model, the ratio of NaX / H2O molecules is set according to the concentration used in the experiment to ensure that the ion concentration or electrolyte density is consistent with the experimental conditions. The electrolyte system model adopts periodic boundary conditions, and quantum chemical calculations are used to optimize the structure of the initial electrolyte system model to obtain the most stable electrolyte system model.

4. The method for screening the desolvation pathway of aqueous sodium-ion battery electrolyte hydrates according to claim 1, characterized in that, In step 2: The first step is to use the NPT ensemble to perform classical molecular dynamics simulations: balance the electrolyte density to the experimental range, ensure that the system reaches thermodynamic equilibrium, and enable the ion hydrate cell to reach a stable configuration; The second step is to use the NVT ensemble to perform AIMD simulation: the electronic structure of the electrolyte system is obtained based on the DFT simulation calculation and the interaction forces between atoms are calculated in real time. Based on this, various transient behaviors that occur in the electrolyte are spontaneously captured, and the dynamic evolution process of ion hydrate cells in the aqueous sodium ion electrolyte system is simulated. The third step is to analyze the coordination state of hydrated ions: calculate the radial distribution function of oxygen atoms of sodium ions and water molecules and oxygen atoms of anions, and integrate to obtain the coordination number of oxygen atoms and anions of sodium ions within the first solvation shell.

5. The method for screening the desolvation pathway of aqueous sodium-ion battery electrolyte hydrates according to claim 4, characterized in that, In the first step of classical molecular dynamics simulation using the NPT ensemble, the equilibrium final state electrolyte system configuration and average cell parameters are extracted to serve as the initial input information for ab initio molecular dynamics simulation.

6. The method for screening the desolvation pathway of aqueous sodium-ion battery electrolyte hydrates according to claim 1, characterized in that, In step 3: The first step is to extract the sodium ion hydrate cell model: After the electrolyte reaches the final equilibrium state in step 2, a variety of basic sodium ion hydrate cell morphologies are initially formed in the electrolyte. In combination with the equilibrium trajectory reached by the dynamic evolution of the sodium ion hydrate cells simulated by AIMD in step 2, the visualization software VMD is used to extract the sodium ion hydrate cells. The second step is to optimize the ion hydrated cell model: geometric optimization and frequency calculation are performed on the ion hydrated cell model extracted in the first step to obtain the optimized steady-state ion hydrated cell model. The third step is steady-state ion hydrate cell energy analysis: Based on the electrostatic potential distribution, the polarity characteristics of the surface charge of molecules or ions are obtained. At the same time, the highest occupied molecular orbital and the lowest unoccupied molecular orbital in the hydrate cell are analyzed. The solvation energy is used to represent the strength of the interaction between the solute sodium ion and the solvent molecule, thereby describing the relevant energy changes in the solvation process.

7. The method for screening the desolvation pathway of aqueous sodium-ion battery electrolyte hydrates according to claim 1, characterized in that, In step 4: The first step is the desolvation process of the ion hydrate cell: the intermolecular forces within the ion hydrate cell are calculated, the weakest interacting solvent molecules are identified and removed, the structure is then geometrically optimized, and the changes in the distance between the remaining atoms in the optimized ion hydrate cell are measured; then, the solvent molecules are gradually removed through iterative processes until only sodium ions remain, at which point complete desolvation can be considered achieved. The second step is to screen the desolvation path of the ion hydrated cell: simulate the process of sodium ions from having a complete hydration layer to complete desolvation through the iterative process in the previous step, record the energy difference at each step and draw the desolvation energy barrier curve, and screen the lowest energy path of the desolvation process, which is the optimal path for steady-state ion hydrated cell desolvation.

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