Method for simulating ionic conductivity of material according to composition of elements, solid electrolyte, and solid battery

Abstract

The present embodiments provide a simulation method for simulating the ionic conductivity of a material according to the composition of elements. This simulation method comprises the steps of: acquiring data for the structure of a material; generating training data including input data of the acquired structure of the material and output data of a set of energy and force of the material; training a neural network potential model by using the training data; and acquiring the energy and force of an arbitrary material for the structure of the arbitrary material by using the trained neural network potential model, and calculating the molecular dynamics and ionic conductivity of the arbitrary material.

Description

Simulation method for the ionic conductivity of materials according to the composition of elements, and solid electrolytes, solid batteries

[0001] The present disclosure relates to a method for simulating the ionic conductivity of a material according to the composition of elements, a solid electrolyte proposed by the simulation method, and an all-solid-state battery comprising the same.

[0002] Identifying specific factors that influence the particular properties of a specific substance remains a difficult task. Furthermore, due to the complex and diverse compositions and structures reported in experimental studies, it is difficult to establish consistent criteria for the properties of materials.

[0003] The introduction of Density Functional Theory (DFT) has improved research on material properties. From a theoretical perspective, DFT is a fundamental approach that provides reliable insights into material properties. However, DFT is facing limitations due to the very high computational costs and long simulation times required for large elemental cells.

[0004] Currently, there is a need to develop comprehensive theoretical and scalable methods to better understand and predict the complex properties of specific materials.

[0005] The embodiments provide a simulation method for more accurately predicting the ionic conductivity of a material according to the composition of elements.

[0006] The present embodiments provide a solid electrolyte proposed by the aforementioned simulation method and an all-solid-state battery including the same.

[0007] In one aspect, a simulation method for simulating the ionic conductivity of a material according to the composition of elements according to one embodiment comprises: a step of acquiring data on the structure of the material; a step of generating training data including input data on the acquired structure of the material and output data on the energy and force set of the material; a step of training a neural network potential model using the training data; and a step of acquiring the energy and force of any material for the structure of any material and calculating the molecular dynamics and ionic conductivity of any material using the trained neural network potential model.

[0008] In another aspect, the solid electrolyte according to another embodiment is a chlorine-doped Li, which is a lithium azyrodite-based material proposed in the aforementioned simulation method. 6-x PS 5-x Cl 1+x or Li doped with chlorine and bromine 6-x-y PS 5-x-y Cl 1+x Br y (x is 0 <x<0.8인 실수, y는 0<y<0.8인 실수)을 포함한다.

[0009] In another aspect, an all-solid-state battery according to another embodiment comprises a positive electrode comprising a positive electrode active material, a negative electrode comprising a negative electrode active material, and a solid electrolyte disposed between the positive electrode and the negative electrode.

[0010] The simulation method for the ionic conductivity of a material according to the composition of elements according to the embodiments can more accurately predict the ionic conductivity of a material according to the composition of elements.

[0011] In addition, the solid electrolyte according to the embodiments and the all-solid-state battery including the same can be proposed by the simulation method described above.

[0012] FIG. 1 is a flowchart of a method for simulating the ionic conductivity of a material according to the composition of elements in one embodiment.

[0013] FIGS. 2a to 2f illustrate examples of obtaining structural data of a material in the step of obtaining data on the structure of the material of FIG. 1.

[0014] Figure 3 specifically illustrates the steps for generating the training data of Figure 1.

[0015] Figure 4 is a conceptual diagram of a neural network potential model using the training data of Figure 1.

[0016] Figure 5 illustrates a specific example of the neural network potential model of Figure 4.

[0017] Figure 6 illustrates another specific example of the neural network potential model of Figure 4.

[0018] Figure 7 is the result of performing large-scale molecular dynamics calculations and ion conductivity calculations for various structures using the neural network potential model of Figure 4.

[0019] Figure 8 is a conceptual diagram of the first cage and second cage of a lithium azirodite-based material to which the simulation method of Figure 1 is applied.

[0020] Figure 9 illustrates the concepts of lithium ion pathways interpreted by applying the concepts of the first cage and the second cage to the crystal structure of the azirodite-based material of Figure 8.

[0021] Figure 10 illustrates the movement of lithium ions in the first cage of Figure 9.

[0022] Figure 11 illustrates that the disorder of lithium ions intensifies as the proportion of anti-site defects (Ras) increases in the crystal structure of the azirodite-based material of Figure 9.

[0023] Figure 12 illustrates that in the crystal structure of the azirodite-based material of Figure 8, if all sulfur and chloride ions are swapped positions (Ras=1), the order of lithium ions is restored.

[0024] Figure 13 illustrates the concept of performing DFT calculations to quantify changes in Coulomb interactions.

[0025] Figure 14 illustrates the Radial Distribution Function (RDF) results of lithium ions.

[0026] Figure 15 illustrates the process of doping chlorine into the 4d site, which is the center of the first cage.

[0027] Figure 16 illustrates the ion diffusivity of various chlorine doping configurations according to the chlorine ratio product in the crystal structures of quaternary and penta-elementary azyrodite materials.

[0028] Figure 17 illustrates the dominant effect of chlorine doping in the increase in ion diffusion due to lithium deficiency.

[0029] Figure 18 illustrates the change in diffusion rate according to the ratio of chlorine to bromine.

[0030] FIG. 19 is a flowchart of a method for manufacturing a solid electrolyte according to another embodiment.

[0031] Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the exemplary drawings. In assigning reference numerals to the components of each drawing, the same components may have the same reference numeral as much as possible, even if they are shown in different drawings. Furthermore, in describing the embodiments, if it is determined that a detailed description of related known components or functions may obscure the essence of the technical concept, such detailed description may be omitted. Where terms such as "comprising," "having," or "consisting of" are used in this specification, other parts may be added unless "only" is used. Where a component is expressed in the singular, it may include a plural unless otherwise specified.

[0032] Additionally, terms such as first, second, A, B, (a), (b), etc., may be used to describe the components of the present disclosure. These terms are used merely to distinguish the components from other components, and the nature, order, sequence, or number of the components are not limited by such terms.

[0033] In describing the positional relationship of components, where it is stated that two or more components are "connected," "combined," or "joined," it should be understood that while the two or more components may be directly "connected," "combined," or "joined," they may also be "connected," "combined," or "joined" with other components "intervened." Here, the other components may be included in one or more of the two or more components that are "connected," "combined," or "joined" with one another.

[0034] In describing the temporal flow relationship regarding components, methods of operation, or methods of production, for example, when the temporal or sequential relationship is described using "after," "following," "next," or "before," it may include cases where the relationship is not continuous unless "immediately" or "directly" is used.

[0035] Meanwhile, where numerical values ​​or corresponding information regarding a component (e.g., levels, etc.) are mentioned, even without separate explicit notation, the numerical values ​​or corresponding information may be interpreted as including a range of error that may occur due to various factors (e.g., process factors, internal or external shocks, noise, etc.).

[0036] A person skilled in the art will understand that the terms "learning" or "learning" appearing throughout the detailed description and claims of this disclosure refer to performing machine learning through procedural computing, and are not intended to refer to mental activities such as human educational activities.

[0037] The embodiments are described in detail below with reference to the drawings.

[0038] FIG. 1 is a flowchart of a method for simulating the ionic conductivity of a material according to the composition of elements in one embodiment.

[0039] Referring to FIG. 1, a simulation method (100) for simulating the ionic conductivity of a material according to the composition of elements according to one embodiment may include the steps of acquiring data on the structure of the material (S110), generating training data (S120), training a neural network potential model (S130), and calculating molecular dynamics and physical properties on the structure of the material (S140).

[0040] A simulation method (100) for simulating the ionic conductivity of a material according to the composition of elements according to one embodiment may further include, but is not limited to, a step (S150) of proposing the composition of elements for the material. In other words, this simulation method (100) may be used as a simulator for calculating molecular dynamics and physical properties of a material structure through a step (S140) of calculating molecular dynamics and physical properties of a material structure, and may further include a step (S150) of proposing the composition of elements for the material to propose or recommend the composition of elements for the material.

[0041] The aforementioned density functional theory (DFT) has enabled groundbreaking advancements in solid electrolytes, which are emerging as promising candidates for next-generation energy storage systems.

[0042] For example, identifying specific factors that influence the ionic conductivity of solid electrolyte materials remains a difficult task. From an experimental perspective, it is challenging to qualitatively or quantitatively distinguish the contributions of lithium ion conductivity between bulk and particle boundaries, as well as between solid electrolyte / electrode interfaces.

[0043] As mentioned above, from a theoretical perspective, Density Functional Theory (DFT) is a fundamental approach that provides reliable insights into material properties. However, the applicability of DFT is limited due to high computational costs associated with large elemental cells and long simulation times.

[0044] A method for simulating the ionic conductivity of a material according to the composition of elements (100) according to one embodiment can more accurately predict the ionic conductivity of a material according to the composition of elements. In addition, a method for simulating the ionic conductivity of a material according to the composition of elements (100) according to one embodiment can predict the composition of elements of a material with high ionic conductivity according to the composition of elements.

[0045] For example, a simulation method (100) for simulating the ionic conductivity of a material according to the composition of elements according to one embodiment provides a comprehensive theoretical and scalable method for better understanding and predicting the complex characteristics of a lithium azyrodite-based material that is or can be used as a solid electrolyte.

[0046] FIGS. 2a to 2f illustrate examples of obtaining structural data of a material in the step of obtaining data on the structure of the material of FIG. 1.

[0047] Referring to FIGS. 1 and FIGS. 2a to 2f, the step (S110) of obtaining data on the structure of a material can be performed by using computational simulation or chemical simulation to obtain data on the structure of the material to be analyzed.

[0048] Computational simulations or chemical simulations include, for example, molecular dynamics (MD) calculations that can predict the trajectories of atoms or molecules using density functional theory (DFT) calculations, first-principle calculations, and Newton's equations of motion, but are not limited to, DFT-DM calculations that combine density functional theory and molecular dynamics calculations.

[0049] Density Functional Theory (DFT) calculations aim to simulate atomic or molecular orbitals through quantum mechanical calculations that account for electrons. In this context, quantum mechanical calculations do not derive results for which a true value exists, but rather obtain convergent values ​​through iterative computation for values ​​whose existence is not clearly known.

[0050] First-principles calculations are used to compute the electronic structure describing the ions constituting the solid electrolyte and the interactions between them. This includes electronic states, electron distribution, and energy band structures. First-principles calculations are also used to calculate the energy levels and reaction entropy of the solid electrolyte to predict the thermodynamic properties of chemical reactions.

[0051] Molecular dynamics (MD) calculations are a technique that simulates atoms through the role of Newton. MD calculations utilize various results obtained from DFT calculations, such as stable molecular structures, van der Waals forces, bond angles, and bond lengths. In these calculations, molecules move as they collide with other molecules while possessing velocities, which allows for the identification of dynamic and structural properties.

[0052] Density Functional Theory (DFT) calculations assume that a chemical reaction occurs, whereas Molecular Dynamics (MD) calculations analyze the situation under the premise that a chemical reaction cannot or does not occur.

[0053] DFT-DM calculation involves performing Density Functional Theory (DFT) and Molecular Dynamics (MD) calculations simultaneously.

[0054] For example, as illustrated in FIGS. 2a to 2f, in the step (S110) of obtaining data on the structure of the material, data on the amorphous and crystalline elements of the quaternary (e.g., Li, P, S, Cl or Li, P, S, Br, etc.) or pentary (e.g., Li, P, S, Cl, Br, etc.) lithium azirodite-based material used in all-solid-state batteries can be obtained by using DFT-DM calculation.

[0055] As an actual experimental example, in the step (S110) of acquiring data on the structure of the material, DFT-DM calculations are used to obtain, for example, 1,674 amorphous structures of the quaternary lithium azirodite-based material Li6PS5Cl according to temperature change over time as shown in FIG. 2a; for example, 250 crystalline structures of Li6PS5Cl according to four temperature conditions over time as shown in FIG. 2b; for example, 210 structures of Li, P, S, and LiCl according to temperature change over time in the synthesis process described later as shown in FIG. 2c; and for example, 600 amorphous structures and 615 Li6PS5Cl0.5Br, which are quaternary and pentary lithium azirodite-based materials according to temperature change over time as shown in FIG. 2d. 0.5 Amorphous structure, as shown in FIG. 2e, for example, 250 crystalline structures of Li6PS5Br and 250 Li6PS5Cl0.5Br according to four temperature conditions over time. 0.5 As shown in Fig. 2f, for example, data on 210 LiBr structures with respect to temperature change over time could be obtained.

[0056] In the step (S110) of obtaining data on the structure of the material, using DFT-DM calculations, the number of obtained data for the amorphous and crystalline elements of the quaternary or pentary lithium azirodite-based material used in all-solid-state batteries is as shown in Table 1 below.

[0057] Type | Number of Data 1. Amorphous structure of Li6PS5Cl 16,740 2. Crystalline structure of Li6PS5Cl according to temperature conditions 4 (Number of temperature conditions) * 250 = 10,000 3. Structure of Li, P, S, and LiCl 4 (Number of elements) * 210 = 8,400 4. Amorphous structure of Li6PS5Br and Li6PS5Cl0.5Br 0.5250 crystalline structures of Li6PS5Br and 250 Li6PS5Cl0.5Br according to 600+615=121,554 amorphous structures and temperature conditions 0.5 Crystalline structure 4 (number of temperature conditions) * (250 + 250) = 20006 LiBr structure 210

[0058] Figure 3 specifically illustrates the steps for generating the training data of Figure 1.

[0059] Referring to FIGS. 1 and FIGS. 3, in the step (S120) of generating training data, training data is generated including input data of the structure of the material and output data of the energy and force set of the material.

[0060] In the step (S120) of generating training data, training data including input data of the structure of the material and output data of the energy and force set of the material can be generated through static calculation, which is a calculation method in which values ​​or variables used in calculation are maintained in a fixed state without changing over time or conditions, using data on the structure of the acquired material described with reference to FIGS. 2a to 2f.

[0061] Static calculations can be VDW calculations that take into account the van der Waals (VDW) interaction, which is a weak physical attraction between materials, by using a function to handle van der Waals interactions in density functional theory (DFT) calculations.

[0062] For example, the optB88-vdW functional illustrated in Fig. 3 is one of the functions developed in density functional theory (DFT) to accurately account for delocalized interactions, namely van der Waals (VdW) interactions. This function combines the optB88 exchange-correlation function with a potential that accounts for van der Waals interactions, and is used to accurately model physical systems where delocalized interactions are important.

[0063] It is highly useful in systems where van der Waals interactions are important, such as intermolecular forces, adsorption, and surface-molecular interactions. This function is widely used in the study of various solids and nanomaterials.

[0064] Because the DFT itself has weaknesses in accurately explaining van der Waals interactions, various functions incorporating van der Waals interaction potentials have been developed to compensate for this. Among them, optB88-vdW is evaluated as a relatively accurate and efficient function.

[0065] For example, as illustrated in FIG. 3, in the step (S120) of generating training data, training data including input data of the structure of the azirodite material and output data of the energy and force set of the azirodite material as shown in Table 2 can be generated from the acquired data of the amorphous and crystalline elements of the quaternary or pentary lithium azirodite material used in the all-solid-state battery of Table 1 through the optB88-vdW functional.

[0066] InputOutputStructure 1Energy 1Force set 1Structure 2Energy 2Force set 2Structure 3Energy 3Force set 3.......Structure nEnergy nForce set n

[0067] Figure 4 is a conceptual diagram of a neural network potential model using the training data of Figure 1.

[0068] Referring to FIGS. 1 and FIGS. 4, in the step of learning a neural network potential model (S130), a neural network potential model (NNP) is learned using the training data generated in the step of generating the training data (S120) described above.

[0069] Specifically, in the step of training the neural network potential model (S130), the neural network potential model can be trained using the training data of Table 2 generated.

[0070] This neural network potential model is a model that uses machine learning techniques to explain interactions between atoms and calculates the potential energy of the system accordingly. This neural network potential model utilizes the artificial neural network shown in Fig. 4 to learn from large-scale data and can quickly and accurately predict very complex interactions between atoms.

[0071] This neural network potential model can predict the energy and forces of any material based on the structure of any material.

[0072] For example, this neural network potential model can predict the energy and forces of azirodite materials for the structure of quaternary or pentary lithium azirodite materials.

[0073] Figure 5 illustrates a specific example of the neural network potential model of Figure 4. Figure 6 illustrates another specific example of the neural network potential model of Figure 4. Figure 7 is a flowchart used in another specific example of the neural network potential model of Figure 6.

[0074] Representative neural network potential models include the Behler-Parrinello Neural Network (BPNN) illustrated in FIG. 5, which predicts interactions between atoms by providing input to a neural network based on atomic-centered symmetry functions, and the Nequip model using a graph-based picture illustrated in FIG. 6. The Behler-Parrinello Neural Network (BPNN) is described in detail in T. Van der Heide et al., Comp. Phys. Commun. 284, 108580 (2023), and the Nequip model using a graph-based picture is described in detail in Nature Communications volume 13, Article number: 2453 (2022), and these constitute part of this specification.

[0075] For example, an Nequip model using a graph neural network-based picture, given a set of atoms (molecules or substances), atomic positions { } and chemical species {Z i} is the total potential energy E pot and the force acting on an atom { It is mapped to}. In this case, the total potential energy is calculated as the sum of the atomic potential energies. Then, the force is calculated as the slope of this predicted total potential energy.

[0076] [Mathematical Formula 1]

[0077]

[0078] N in mathematical formula 1 atoms represents the number of atoms, and E i,atomic represents the energy of each atom.

[0079] Other neural network potential models may include, but are not limited to, Deep Potential (DP) models that use deeper neural network structures to predict complex interactions in materials and are particularly efficient in solid-state materials, or Gaussian Approximation Potentials (GAP) models that complement the capabilities of neural network potentials by combining Gaussian processes and machine learning techniques.

[0080] Referring again to FIG. 1, the step (S140) of calculating molecular dynamics and ionic conductivity involves obtaining the energy and force of an arbitrary material for the structure of an arbitrary material using a learned neural network potential model and calculating the molecular dynamics and ionic conductivity for the arbitrary material.

[0081] Figure 7 is the result of performing large-scale molecular dynamics calculations and ion conductivity calculations for various structures using the neural network potential model of Figure 4.

[0082] As illustrated in Fig. 7, through the step (S140) of calculating molecular dynamics and ionic conductivity, it was possible to calculate the ionic conductivity for various ion distributions using more than 3,000 atoms in the lithium azylodite-based material in the aforementioned experiment.

[0083] Referring again to FIG. 1, the step (S150) of proposing the composition of elements for any material proposes the composition of elements for any material using molecular dynamics and ionic conductivity for any material.

[0084] In the step of proposing the composition of elements for an arbitrary material (S150), the movement paths of lithium obtained through large-scale calculations can be classified, and the energy required to pass through each path can be statistically analyzed.

[0085] In the step of proposing the composition of elements for an arbitrary substance (S150), the movement paths of specific elements or ions can be classified through molecular dynamics for the arbitrary substance, and the energy required to pass through each path can be statistically analyzed to propose a change in the composition of elements for the specific substance.

[0086] Figure 8 is a conceptual diagram of the first cage and second cage of a lithium azirodite-based material to which the simulation method of Figure 1 is applied.

[0087] Referring to FIG. 8, in the step (S150) of proposing the composition of elements for an arbitrary material, if the material is a lithium azyrodite-based material and a specific element is lithium, the two regions around an anion through which lithium can pass are the first cage (O h It is possible to define the second cage (Corridor-cage) and the second cage (Corridor-cage), statistically calculate the energy barrier required to escape each cage in various structures, identify the bottleneck as shown on the right side of Figure 8, and propose compositional changes to reduce the bottleneck.

[0088] The simulation method (100) for the ionic conductivity of a material according to the composition of elements according to the above-described embodiment can more accurately predict the ionic conductivity of the material according to the composition of elements. In addition, the simulation method according to the above-described embodiment can predict the composition of elements of a material with high ionic conductivity according to the composition of elements.

[0089] Hereinafter, a step (S150) of proposing the composition of elements for any material in an experimental example in which the material is a lithium azyrodite-based material and a specific element is lithium is described in detail with reference to FIGS. 9 to 19. Based on this, the present embodiments provide a solid electrolyte proposed by the simulation method described above and an all-solid-state battery including the same.

[0090] [Experimental Example]

[0091] In the step (S150) of proposing the composition of elements for an arbitrary substance, the present experimental example classifies the movement paths of specific elements or ions through molecular dynamics for the arbitrary substance and statistically analyzes the energy required to pass through each path to propose a change in the composition of elements for the specific substance.

[0092] As described above, in the experimental example, when the material is a lithium azylodite-based material and the specific element is lithium, the two regions around the anion through which the lithium can pass are defined as a first cage and a second cage, and the energy barrier required to escape each cage in various structures is statistically calculated and the bottleneck is identified, and a change in composition to reduce the bottleneck is proposed.

[0093] Specifically, this experimental example explores the influence of anions on lithium diffusion in lithium azyrodite-based materials (Li6PS5X, X = Cl and Br) by extending DFT capabilities through the introduction of simulations using a neural network potential model (NNP). This experimental example classifies the lithium framework into two different cages and demonstrates that sulfur ions at the center of these cages bind to surrounding lithium ions. From the results, this experimental example provides a strategy to improve lithium ion conductivity by minimizing the occupation of sulfur ions at the cage centers.

[0094] In this experimental example, a new theoretical perspective is proposed and verified through large-scale simulations combining artificial intelligence based on a neural network processor with molecular dynamics (MD). This experimental example classifies the lithium pathway of lithium azyrodite-based materials (Li6PS5X, X = Cl and Br) into two types of first and second cages (O cage and corridor cage) beyond conventional approaches. Through this new classification, this experimental example was able to analyze a wide number of lithium hopping events calculated by MD around these cages.

[0095] Through this experimental example, the effects of anion arrangement and distribution within the cage center can be evaluated, and a formula regarding the influence of anion composition on ion diffusivity can be established. This experimental example confirms that sulfur ions interact more strongly with lithium ions than halogen ions, thereby locally inhibiting lithium migration. This finding provides a deeper understanding of ion transport in lithium azyrodite and establishes a clear benchmark for evaluating and analyzing lithium ion conductivity in terms of anions at the cage center.

[0096] FIG. 9a illustrates the concepts of lithium ion pathways interpreted by applying the concepts of the first cage and the second cage in the crystal structure of the azirodite-based material of FIG. 8. FIG. 9b defines the lithium ion jumping pathways of the first cage and the second cage of FIG. 9a.

[0097] a in Fig. 9a is the crystal structure of Li6PS5Cl, and b in Fig. 9a shows the classification of lithium ion jumping in the crystal structure of Li6PS5Cl.

[0098] c in Fig. 9a is the first cage (O h This is a conceptual diagram of the second cage (Corridor-cage) and the second cage (Corridor-cage).

[0099] In this experimental example, two distinct cage types centered on anions were defined to clearly elucidate the effects of anions, thereby advancing the lithium diffusion mechanism in the azyrodite structure. This experimental example [addresses] the existing lithium cage as ' O h It was named the 'cage (octahedral cage or first cage),' where lithium ions form an octahedral structure centered around a 4d anion. h Recognizing that a cage alone cannot cover all possible lithium ion pathways, this experimental example also introduces a 'corridor cage (or second cage)' centered on a 4a anion.

[0100] This octahedron-shaped corridor cage O through the triangular facesh Interconnected with the cage O h It provides a comprehensive framework for analyzing the movement of lithium ions beyond the cage. Figure 9c illustrates this concept.

[0101] In Fig. 9a, the 4d site is sulfur; doping this site to replace it with chlorine (Cl) or bromine (Br) increases the movement of lithium ions (gray spheres) surrounding the cage, thereby improving ionic conductivity. Generally, after doping, the intra-cage jump (the red movement in Fig. 9c above), which is the movement of lithium ions inside the cage—that is, the first cage path (O)—which is the movement path within the cage h The movement of lithium ions along the cage path (yellow in Fig. 9b) can be mainly observed.

[0102] In this experimental example, when doped with chlorine or bromine, it was observed that not only lithium ions jump within the cage but also inter-cage jumps (blue movement in Fig. 9c), which are the movement of lithium ions between cages, occur frequently. Based on this observation, the inter-cage movement path where inter-cage jumps occur can be newly defined as the second cage path (light blue in Fig. 9b).

[0103] In summary, the red line within the Oh cage represents the path for well-known in-cage jumps, whereas the blue and red lines within the corridor cage contain the paths for in-cage and inter-cage jumps, respectively. The unique structure of the corridor cage allows not only within the same cage but also different O h It captures the complexity of lithium ion movement in azirodite-based materials by facilitating lithium ion movement throughout the cage. The morphology of the lithium ion cage plays a pivotal role in determining ionic conductivity, along with the spatial arrangement and disorder of lithium ions.

[0104] Figure 10 illustrates the movement of lithium ions in the first cage of Figure 9.

[0105] As mentioned above, the main aspect affecting lithium-ion arrangement is the distribution of anions. h In the cage, sulfide ions typically occupy the central position at the 4d site. They can exchange sites with chloride ions located at the 4a site, which can lead to anti-site defects.

[0106] Under these conditions, due to asymmetric Coulomb interactions, lithium ions align closer to sulfide ions than to chloride ions. This default setting is O h This triggers the movement of lithium ions from the 24g site to the 48h site in the cage (see Fig. 10).

[0107] Figure 11 illustrates that the disorder of lithium ions intensifies as the proportion of anti-site defects (Ras) increases in the crystal structure of the azirodite-based material of Figure 9.

[0108] These changes lead to significant disorder of cations between the 24g and 48h sites, affecting the overall ionic conductivity. As the proportion of anti-site defects (Ras) increases, the disorder of lithium ions intensifies, as illustrated in Fig. 11.

[0109] As illustrated in Fig. 11, the disorder of lithium ions intensifies as the proportion of anti-site defects (Ras) increases. Eventually, when all sulfur and chloride ions swap positions (Ras=1), the lithium ions experience uniform Coulomb interactions again.

[0110] Figure 12 illustrates that in the crystal structure of the azyrodite-based material of Figure 8, if all sulfur and chloride ions are swapped positions (Ras=1), the order of lithium ions is restored. Figure 13 illustrates the concept of performing DFT calculations to quantify changes in Coulomb interactions.

[0111] As shown in Fig. 12, this uniformity of electrostatic interaction restores the order of lithium ions.

[0112] As shown in Fig. 13, DFT calculations can be performed to quantify changes in these Coulomb interactions.

[0113] Figure 14 illustrates the Radial Distribution Function (RDF) results of lithium ions.

[0114] Referring to Fig. 14, according to the DFT calculations shown in Fig. 13, regardless of the anion type at the 4d or 4a site, lithium ions are preferentially distributed closer to sulfur ions, as can be seen from the fact that the bond length between lithium and sulfur is shorter than the bond length between lithium and chlorine. It can be seen that these results are consistent with the radial distribution function (RDF) analysis.

[0115] Figure 15 illustrates the process of chlorine doping into the 4d site, which is the center of the first cage. Figure 16 illustrates the ion diffusivity of various chlorine doping configurations according to the chlorine ratio product in the crystal structures of quaternary and penta-elementary azyrodite materials.

[0116] Referring to Fig. 15, in this experimental example, sulfur at the 4d site was replaced with chlorine to achieve various Ras compositions. Although it is also possible to replace sulfur at the 4a site, replacing the 4d site (Oh cage center) under high Ras conditions is effectively equivalent to chlorine doping at the 4a site.

[0117] In this experimental example, Equation 2 can be derived, which provides an ion diffusivity proportional to the product of Ras and 1 - Ras in the pattern of Fig. 16. This is a complementary perspective to the relationship of ion conductivity from the product term related to the vacancy concentration.

[0118] [Mathematical Formula 2]

[0119] D ∝ Ras × (1 - Ras).

[0120] Here, Ras is O h Cage (4d) center (R x,Oh The ratio of halogen ions is equal to that of ), and 1 - Ras is the center (R) of the corridor cage (4a). X,Corridor It is equal to the ratio of halogen ions. From this observation, it can be inferred that the lithium diffusivity is proportional to the product of the ratios of halogens at the two cage centers, as shown in Equation 3.

[0121] [Mathematical Formula 3]

[0122] D ∝ R X,Oh × R X,Corridor (x = Cl and Br)

[0123] Figure 17 illustrates the dominant effect of chlorine doping in the increase in ion diffusion due to lithium deficiency.

[0124] Based on these insights, Fig. 17 shows each cage center (R X,Oh × R X, Corridor The ion diffusivity of various chlorine-doped configurations according to the chlorine ratio product of ) shows a notable linear proportionality. Although chlorine doping can form lithium vacancies that affect ion diffusivity, the results of this experimental example suggest that the distribution and ratio of anions may play a more decisive role than lithium vacancies.

[0125] In Figure 17, when Ras=0, the diffusivity calculated from the MD based on NNP for a 2×2×2 supercell shows a greater increase in diffusivity due to chlorine doping than due to lithium deficiency (VLi).

[0126] This experimental example also demonstrated that this trend is consistent in the five-element system composed of Li, P, S, Cl, and Br. Similar to the results of the study on the quaternary system (Li, P, S, and Cl), R X,Oh × R X, Corridor We observed that as the (x = Cl and Br) values ​​increased, the ion diffusivity increased, showing a linear-like trend in terms of diffusivity.

[0127] The goal is to optimize the ionic conductivity according to x within the target composition formula, and I understand that the key to this is to effectively formulate an approximation equation that models the ionic conductivity of the composition formula.

[0128] Figure 18 illustrates the change in diffusion rate according to the ratio of chlorine to bromine.

[0129] Referring to FIG. 18, the present experimental example also has a similar R X,Oh × R X,Corridor It was found that even when using the values, the diffusivity can change depending on the chlorine / bromine ratio and exhibits relatively larger dispersion compared to the quaternary system. This experimental example confirmed that this dispersion occurs in the relative ratio of bromine and chlorine.

[0130] By integrating theoretical insights and experimental findings from the results, this experimental example not only advances fundamental knowledge regarding solid electrolytes but also presents innovative solutions for improving energy storage technology.

[0131] Above, the step (S150) of proposing the composition of elements for any material in an experimental example in which the material is a lithium azyrodite-based material and a specific element is lithium has been described in detail with reference to FIGS. 9 to 19. Below, a solid electrolyte proposed by the simulation method described above and an all-solid-state battery including the same are provided.

[0132] In another aspect, the solid electrolyte according to another embodiment is a chlorine-doped Li, which is a lithium azyrodite-based material proposed in the simulation method (100) described above. 6-x PS 5-x Cl 1+x or Li doped with chlorine and bromine 6-x-y PS 5-x-y Cl 1+x Br y (x is 0 <x<0.8인 실수, y는 0<y<0.8인 실수)을 포함한다.

[0133] In another aspect, an all-solid-state battery according to another embodiment comprises a positive electrode comprising a positive electrode active material, a negative electrode comprising a negative electrode active material, and a solid electrolyte disposed between the positive electrode and the negative electrode.

[0134] When synthesizing solid electrolyte Li6PS5Cl by adjusting the molar ratio without using an excess amount of chlorine, the following reaction scheme 1 is followed.

[0135] [Reaction Equation 1]

[0136] 0.5 P2S5+ LiCl + 2.5 Li2S -> Li6PS5Cl

[0137] The aforementioned Li uses an excess amount of chlorine to cause chloride ions to occupy the positions of sulfur ions and to create defects in lithium ions. 6-x PS 5-x Cl 1+x It can be synthesized by the following reaction scheme 2, but is not limited thereto.

[0138] [Reaction Equation 2]

[0139] 0.5 P2S5+ (1+x)LiCl + (2.5-x)Li2S -> Li 6-x PS 5-x Cl 1+x

[0140] Using chlorine and bromine, the aforementioned Li 6-x-y PS 5-x-y Cl 1+x Br y It can be synthesized by the following reaction scheme 3, but is not limited thereto.

[0141] [Reaction Equation 3]

[0142] 0.5 P2S5+ (1+x)LiCl + (y)LiCl + (2.5-xy)Li2S -> Li 6-x-y PS 5-x-y Cl 1+x Br y

[0143] Battery cells can be fabricated from the aforementioned all-solid-state battery. Subsequently, these battery cells can be bundled to produce modules, and these modules can be combined to ultimately produce a battery pack, or the cells can be directly integrated into a single large unit to produce a battery pack. Alternatively, the aforementioned all-solid-state battery can be packaged directly into a battery bag as soon as it is fabricated into battery cells.

[0144] FIG. 19 is a flowchart of a method for manufacturing a solid electrolyte according to another embodiment.

[0145] Referring to FIG. 19, a method (300) for manufacturing a solid electrolyte according to another embodiment comprises a chlorine-doped Li between a positive electrode containing a positive electrode active material and a negative electrode containing a negative electrode active material, according to reaction schemes 1 to 3. 6-x PS 5-x Cl 1+x or Li doped with chlorine and bromine 6-x-y PS 5-x-y Cl 1+x Br y It includes the step of preparing a mixture containing (S310) and the step of heat-treating the mixture (S320).

[0146] As explained with reference to FIG. 9a, chlorine-doped Li 6-x PS 5-x Cl 1+x or Li doped with chlorine and bromine 6-x-y PS 5-x-y Cl 1+x Br y It may include a crystal phase having an argyrodite-based crystal structure.

[0147] Chlorine and / or bromine can be doped into sulfur sites forming anti-site defects. The proportion of anti-sites doped by chlorine and / or bromine into sulfur sites can be 60–80%.

[0148] The all-solid-state battery and solid electrolyte according to the embodiments and the method for manufacturing the same can maximize ionic conductivity in the solid electrolyte while simultaneously maintaining stability.

[0149] Although the simulation method, all-solid-state battery, solid electrolyte, and method for manufacturing the same according to the embodiments have been described with reference to the drawings above, the present invention is not limited thereto.

[0150] A simulation method (100) for simulating the ionic conductivity of a material according to the composition of elements according to one embodiment can be used in a simulator that calculates molecular dynamics and physical properties of a material structure through a step (S140) of calculating molecular dynamics and physical properties of a material structure, or further includes a step (S150) of proposing a composition of elements for a material, and further includes a step (S150) of proposing a composition of elements for a material, and even proposes or recommends a composition of elements for a material.

[0151] The simulation method (100) or simulator according to the embodiments described above may be implemented through various means. For example, the simulation method (100) or simulator according to the embodiments may be implemented by hardware, firmware, software, or a combination thereof.

[0152] In the case of implementation by hardware, the simulation method (100) or simulator according to the embodiments may be implemented by one or more ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices), FPGAs (Field Programmable Gate Arrays), processors, controllers, microcontrollers, or microprocessors.

[0153] In the case of implementation by firmware or software, the simulation method (100) or simulator according to the embodiments may be implemented in the form of a device, procedure, or function that performs the functions or operations described above. The software code may be stored in a memory unit and executed by a processor. The memory unit may be located inside or outside the processor and may exchange data with the processor by various means already known.

[0154] Additionally, terms such as "system," "processor," "controller," "component," "module," "interface," "model," or "unit" described above may generally refer to computer-related entities, hardware, combinations of hardware and software, software, or running software. For example, the aforementioned components may be, but are not limited to, processes driven by a processor, processors, controllers, control processors, objects, execution threads, programs, and / or computers. For example, both the application running on the controller or processor and the controller or processor may be components. One or more components may reside within a process and / or execution thread, and the components may be located on a single device (e.g., a system, a computing device, etc.) or distributed across two or more devices.

[0155] Meanwhile, a simulation method (100) or simulator according to another embodiment provides a computer program stored on a computer recording medium. Additionally, another embodiment provides a computer-readable recording medium that records a program for realizing the aforementioned simulation method (100). The program recorded on the recording medium can be read, installed, and executed on a computer to perform the aforementioned steps.

[0156] In this way, in order for a computer to read a program recorded on a recording medium and execute functions implemented in the program, the aforementioned program may include code encoded in a computer language such as Python, C, C++, JAVA, or machine language, which can be read by the computer's processor (CPU) through the computer's device interface.

[0157] Such code may include functional code related to functions that define the aforementioned functions, and may also include control code related to execution procedures necessary for a computer processor to execute the aforementioned functions according to a predetermined procedure.

[0158] In addition, this code may further include memory reference-related code regarding where (address) in the computer's internal or external memory additional information or media required for the computer's processor to execute the aforementioned functions should be referenced.

[0159] In addition, if the computer processor needs to communicate with any other computer or server located remotely in order to execute the aforementioned functions, the code may further include communication-related code regarding how the computer processor should communicate with any other computer or server located remotely using the computer's communication module, and what information or media should be transmitted or received during communication.

[0160] A computer-readable recording medium that has recorded a program as described above includes, for example, ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical media storage device, etc., and may also include one implemented in the form of a carrier wave (for example, transmission via the Internet).

[0161] In addition, computer-readable recording media are distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner.

[0162] Furthermore, the functional program for implementing the present invention, and the related code and code segments, etc., may be easily inferred or modified by programmers skilled in the art to which the present invention belongs, taking into account the system environment of a computer that reads a recording medium and executes the program.

[0163] The aforementioned simulation method (100) may also be implemented in the form of a recording medium containing instructions executable by a computer, such as an application or program module executed by a computer. A computer-readable medium may be any available medium accessible by a computer and includes both volatile and non-volatile media, as well as removable and inseparable media. Additionally, a computer-readable medium may include all computer storage media. A computer storage medium includes both volatile and non-volatile, removable and inseparable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data.

[0164] The aforementioned simulation method (100) and simulator may be executed by an application basically installed on the terminal (which may include a program included in a platform or operating system, etc., installed basically on the terminal), or by an application (i.e., a program) directly installed by the user on the master terminal through an application providing server, such as an application store server, an application, or a web server related to the service. In this sense, the method of providing information related to the aforementioned user behavior may be implemented by an application (i.e., a program) that is basically installed on the terminal or directly installed by the user, and may be recorded on a computer-readable recording medium such as the terminal.

[0165] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0166] The scope of the present invention is defined by the claims set forth below rather than by the detailed description above, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present invention.

[0167] The foregoing description is merely an illustrative explanation of the technical concept of the present disclosure, and those skilled in the art to which the present disclosure pertains may make various modifications and variations within the scope of the essential characteristics of the technical concept. Furthermore, since these embodiments are intended to explain, not limit, the scope of the technical concept is not limited by these embodiments. The scope of protection of the present disclosure shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of the present disclosure.

[0168] CROSS-REFERENCE TO RELATED APPLICATION

[0169] This patent application claims priority pursuant to Section 119(a) of the U.S. Patent Act (35 USC § 119(a)) to Korean Patent Application No. 10-2024-0162314 filed on November 14, 2024, all of which are incorporated by reference into this patent application. Furthermore, this patent application claims priority in countries other than the United States for the same reasons as above, all of which are incorporated by reference into this patent application.

Claims

1. A simulation method for simulating the ionic conductivity of a material according to the composition of elements, A step of acquiring data on the structure of the material; A step of generating training data that includes data on the structure of an acquired material as input data and a set of energy and force of the material as output data; A step of training a neural network potential model using the above training data; and A simulation method comprising the step of obtaining the energy and force of an arbitrary material for the structure of an arbitrary material using the learned neural network potential model and calculating the molecular dynamics and ionic conductivity for the arbitrary material.

2. In Paragraph 1, A simulation method further comprising the step of proposing the composition of elements for the arbitrary material using molecular dynamics and ionic conductivity for the arbitrary material.

3. In Paragraph 1, The step of acquiring data on the structure of the above-mentioned substance is a simulation method for acquiring data on the structure of the substance to be analyzed using computational simulation or chemical simulation.

4. In Paragraph 1, A simulation method for generating the training data through static calculation using acquired data on the structure of the material in the step of generating the training data.

5. In Paragraph 1, The above static calculation is a simulation method that calculates the Van der Waals (VDW) interaction, which is a weak physical attraction between materials, by using a function to handle the Van der Waals interaction in density functional theory (DFT) calculations.

6. In Paragraph 1, The above neural network potential model is a simulation method that is a Behler-Parrinello Neural Network Potential model or an Nequip model using a graph-based picture.

7. In Paragraph 2, A simulation method that, in the step of proposing the composition of elements for the above arbitrary substance, classifies the movement paths of specific elements or ions through molecular dynamics of the above arbitrary substance and statistically analyzes the energy required to pass through each path to propose a change in the composition of elements for the specific substance.

8. In Paragraph 7, In the step of proposing the composition of elements for the above arbitrary material, A simulation method in which, when the above material is a lithium azyrodite-based material and the above specific element is lithium, two regions around anion through which the lithium can pass are defined as a first cage and a second cage, and the energy barrier required to escape each cage in various structures is statistically calculated and the bottleneck is identified and a compositional change is proposed to reduce the bottleneck.

9. In Paragraph 8, The above lithium argyrodite-based material is a chlorine-doped Li having an argyrodite-based crystal structure. 6-x PS 5-x Cl 1+x or Li doped with chlorine and bromine 6-x-y PS 5-x-y Cl 1+x Br y (x is 0 <x<0.8인 실수, y는 0<y<0.8인 실수)인 시뮬레이션 방법.

10. In Paragraph 9, The doping ratio (R) at the center of the first cage mentioned above x, oh ) x ratio doped in the center of the second cage (R x, Corridor ) A simulation method having the relationship between the ionic conductivity (D) of the above lithium azyrodite-based material and the following mathematical formula: D ∝ R X,Oh × R X,Corridor = Race (1 - Race) Here, Ras is equal to the ratio of halogen ions at the center of the first cage, and 1 Ras is equal to the ratio of halogen ions at the center of the second cage.

11. Chlorine-doped Li, which is the lithium azyrodite-based material proposed in the simulation method of claim 8 6-x PS 5-x Cl 1+x or Li doped with chlorine and bromine 6-x-y PS 5-x-y Cl 1+x Br y (x is 0 <x<0.8인 실수, y는 0<y<0.8인 실수)을 포함하는 고체 전해질.

12. In Paragraph 11, The above Li 6-x PS 5-x Cl 1+x is synthesized by the following reaction equation, and 0.5P2S5+ (1+x)LiCl + (2.5-x)Li2S -> Li 6-x PS 5-x Cl 1+x The above Li 6-x-y PS 5-x-y Cl 1+x Br y is a solid electrolyte synthesized by the following reaction equation. 0.5 P2S5+ (1+x)LiCl + (y)LiBr + (2.5-xy)Li2S -> Li 6-x-y PS 5-x-y Cl 1+x Br y 13. Anode comprising a positive electrode active material; A cathode comprising a cathode active material; and An all-solid-state battery comprising the solid electrolyte of claim 11 disposed between the anode and the cathode.

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