Chloride-based solid electrolyte and all-solid-state lithium ion battery

Abstract

To provide a chloride-based solid electrolyte with low activation energy, and all-solid-state lithium ion battery using the same.SOLUTION: A chloride-based solid electrolyte expressed by the composition formula: LiaMbOcCld (M being one or two selected from V, Ta, Fe, Sb, Ga, W, and Zr, with 1.0≤a≤3.0, 1.0≤b≤4.0, 0≤c≤1.0, and 3.0≤d≤19.0), and having an enthalpy at absolute zero in the range of -1.8132 to -0.7429 eV / atom.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a chloride-based solid electrolyte and an all-solid-state lithium-ion battery. [Background technology]

[0002] With the recent rapid spread of information-related devices and communication devices such as personal computers, video cameras, and mobile phones, the development of batteries to be used as their power sources has become increasingly important. Among these batteries, lithium-ion batteries have attracted attention due to their high energy density. Furthermore, high energy density and improved battery characteristics are also required for lithium secondary batteries for large-scale applications such as vehicle-mounted power sources and load leveling.

[0003] However, in the case of lithium-ion batteries, the electrolyte is mostly organic, and even if a flame-retardant compound is used, the risk of fire cannot be completely eliminated. All-solid-state lithium-ion batteries, which use a solid electrolyte, have been attracting attention in recent years as an alternative to liquid-based lithium-ion batteries. Among these, all-solid-state lithium-ion batteries with lithium halide added as the solid electrolyte are becoming mainstream.

[0004] Among halogen-based solid electrolytes, chloride-based solid electrolytes in particular have been actively developed and researched. 3-2X M X In 1-Y M´ Y L 6-Z L´ Z (wherein M and M' are metal elements, L and L' are halogen elements, and X, Y and Z independently satisfy 0≦X<1.5, 0≦Y<1, 0≦Z≦6), and it is stated that the use of such a solid electrolyte can provide an all-solid-state lithium secondary battery with good charge-discharge characteristics. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-244734 Summary of the Invention [Problem to be solved by the invention]

[0006] However, due to the recent demand for all-solid-state lithium-ion batteries, there is a demand for further research and development of chloride-based solid electrolyte compounds having novel compositions that provide good battery characteristics.

[0007] A good solid electrolyte is expected to be a material with a low energy barrier (activation energy) required for lithium ions to migrate through the crystal lattice. A low activation energy contributes to high lithium ion conduction. Therefore, from the viewpoint of improving battery characteristics, a solid electrolyte with a low activation energy is preferable. The present invention has been made to solve the above-mentioned problems, and aims to provide a chloride-based solid electrolyte with a low activation energy and an all-solid-state lithium ion battery using the same. [Means for solving the problem]

[0008] The present invention, which was completed based on the above findings, is defined below. (1) Composition formula: Li a M b O c Cl d (In the formula, M is one or two of V, Ta, Fe, Sb, Ga, W, and Zr, and 1.0≦a≦3.0, 1.0≦b≦4.0, 0≦c≦1.0, and 3.0≦d≦19.0.) A chloride-based solid electrolyte with an enthalpy of -1.8132 to -0.7429 eV / atom at absolute zero. (2) The chloride-based solid electrolyte according to (1) above, wherein d is 3.0≦d≦4.0 in the formula. (3) An all-solid-state lithium ion battery including a solid electrolyte layer containing the chloride-based solid electrolyte according to (1) or (2), a positive electrode layer, and a negative electrode layer. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide a chloride-based solid electrolyte having a low activation energy and an all-solid-state lithium-ion battery using the same. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram of an all-solid-state lithium-ion battery according to an embodiment of the present invention. [Figure 2] 1 is a flowchart showing a procedure for determining the composition of a chloride-based solid electrolyte according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0011] Next, embodiments of the present invention will be described in detail with reference to the drawings. It should be understood that the present invention is not limited to the following embodiments, and that appropriate design changes and improvements may be made based on the ordinary knowledge of those skilled in the art without departing from the spirit of the present invention.

[0012] (chloride-based solid electrolyte) The chloride-based solid electrolyte according to an embodiment of the present invention has the composition formula: Li a M b O c Cl d(wherein M is one or two of V, Ta, Fe, Sb, Ga, W, and Zr, and 1.0≦a≦3.0, 1.0≦b≦4.0, 0≦c≦1.0, and 3.0≦d≦19.0.) In the chloride-based solid electrolyte according to the embodiment of the present invention, the presence of M, which is one or two of V, Ta, Fe, Bi, Ga, W, and Zr and has a large ionic radius in the solid electrolyte, expands the crystal lattice and improves ionic conductivity. Furthermore, the presence of Cl, a halogen with a large ionic radius, increases the stability of the crystal structure and suppresses the generation of impurity phases. With this configuration, substances that inhibit ionic conduction are reduced, and therefore a chloride-based solid electrolyte with good ionic conductivity can be obtained.

[0013] The chloride-based solid electrolyte according to the embodiment of the present invention satisfies the following formula in the above formula: 1.0≦a≦3.0. If a is less than 1.0, the carrier concentration may be low, resulting in a problem of reduced ionic conductivity. If a is more than 3.0, it may be difficult to synthesize a single phase.

[0014] The chloride-based solid electrolyte according to an embodiment of the present invention satisfies the condition 1.0≦b≦4.0 in the above composition formula. If b is less than 1.0, the amount of substitution is small, resulting in a weak crystal lattice expansion effect, and there is a risk that a sufficient improvement in ionic conductivity cannot be achieved. Furthermore, if b is more than 4.0, there is a risk that a phase with low ionic conductivity will be formed.

[0015] The chloride-based solid electrolyte according to the embodiment of the present invention satisfies 0≦c≦1.0 in the above composition formula. In this way, the chloride-based solid electrolyte according to the embodiment of the present invention may or may not contain O in the composition formula. In this way, the chloride-based solid electrolyte according to the embodiment of the present invention may be a so-called oxychloride-based solid electrolyte.

[0016] The chloride-based solid electrolyte according to an embodiment of the present invention satisfies 3.0≦d≦19.0 in the composition formula above. The composition d relating to Cl may be 3.0≦d≦4.0.

[0017] Chloride-based solid electrolytes according to an embodiment of the present invention include LiVCl5, LiVCl6, Li2VCl5, Li2VCl6, LiFeCl4, LiGaCl4, LiWCl6, LiSb2Cl7, Li3V2Cl 12 , or Li3Zr4Cl 19 The chloride-based solid electrolyte according to the embodiment of the present invention is preferably LiVOCl3, LiVOCl4, Li6VOCl2, or LiVOCl3. 11 , Li2V2OCl6, Li2VOCl4, Li2VOCl5, or Li3VOCl5.

[0018] The average particle size D50 (50% cumulative volume particle size D50) of the chloride-based solid electrolyte according to an embodiment of the present invention is not particularly limited, but may be 0.01 to 100 μm, 0.1 to 100 μm, or 0.1 to 50 μm.

[0019] The chloride-based solid electrolyte according to the embodiment of the present invention has an enthalpy at absolute zero (0 K) of -1.8132 to -0.7429 eV / atom. This configuration increases the stability of the crystal structure and suppresses the generation of impurity phases. As a result, a chloride-based solid electrolyte with low activation energy is obtained.

[0020] In recent years, advances in computers and computational techniques have enabled highly accurate calculations of H (enthalpy) using first-principles calculations based on quantum mechanics. First-principles calculations are a computational method for determining the electronic state of a material system without reference to experimental results. Based on quantum mechanics, they use atomic number and system structure as input parameters to ab initio elucidate physical mechanisms and predict physical properties. First-principles calculations use crystal structure information as initial information and generalized gradient approximation to obtain enthalpy information. Since G (free energy) = H (enthalpy) - TS (temperature × entropy), free energy and enthalpy are equal at absolute zero (0 K). While it is difficult to experimentally measure thermodynamic parameters such as enthalpy at absolute zero, it is known that experimental and calculated values coincide near room temperature in solids. The enthalpy at absolute zero for chloride-based solid electrolytes according to embodiments of the present invention can also be calculated using first-principles calculations. Software used for first-principles calculations includes Vienna Ab initio Simulation Package (VASP), ABINIT, and QUANTUM ESPRESSO.

[0021] Furthermore, the enthalpy at absolute zero of the chloride-based solid electrolyte according to the embodiment of the present invention may be calculated using a machine learning potential (neural network potential). The machine learning potential is an AI model trained using the results of large-scale quantum chemical calculations performed by a supercomputer as training data, and has the advantage of being able to perform calculations with similar accuracy at speeds nearly several thousand times faster than the above-mentioned first-principles calculations. Examples of software used for the machine learning potential include Matlantis (registered trademark). A method for calculating the enthalpy at absolute zero of a chloride-based solid electrolyte using Matlantis will be described in detail below in the section on the method for producing a chloride-based solid electrolyte.

[0022] The activation energy of the chloride-based solid electrolyte according to the embodiment of the present invention is preferably 28.0 kJ / mol or less. When the activation energy is 28.0 kJ / mol or less, the energy barrier required for lithium ions to migrate through the crystal lattice is low, thereby improving ionic conductivity and battery characteristics. The activation energy of the chloride-based solid electrolyte according to the embodiment of the present invention is more preferably 20.0 kJ / mol or less, and even more preferably 15.0 kJ / mol or less.

[0023] The ionic conductivity of the chloride-based solid electrolyte according to the embodiment of the present invention at 30°C is preferably 0.5 mS / cm or more, more preferably 8.0 mS / cm or more, and even more preferably 1.0 mS / cm or more. The ionic conductivity of the chloride-based solid electrolyte may be evaluated by a simulation, which will be described in detail later in the description of the method for producing the chloride-based solid electrolyte, or may be evaluated from actual chloride-based solid electrolyte powder as follows. First, 0.2 g of the chloride-based solid electrolyte powder was pressed at 370 MPa to form a plate. Then, 0.1 g of gold powder was spread on each side and pressed at 555 MPa to produce a pellet with a 10 mm diameter gold electrode. Using this pellet, AC impedance measurements were performed at 30°C from 20 Hz to 100 MHz at an applied voltage of 100 mV using a Toyo Corporation E4990A with open-short correction. The Li-ion migration resistance was determined by analyzing the arcs observed in the Cole-Cole plot obtained from the AC impedance measurement above 10 kHz. Next, ionic conductivity was calculated from the Li-ion migration resistance and the thickness and area of the solid electrolyte portion of the pellet used for measurement, using the following formula: Ion conductivity (mS / cm) = Thickness of the solid electrolyte part of the pellet (cm) × 1000 / [(Li ion migration resistance (Ω)) × (area of the solid electrolyte part of the pellet (cm) 2 ))]

[0024] (Method of manufacturing chloride-based solid electrolyte) The method for producing a chloride-based solid electrolyte according to an embodiment of the present invention is a method for producing a chloride-based solid electrolyte comprising the steps of: a M b O c Cl d (wherein M is one or two of V, Ta, Fe, Sb, Ga, W, and Zr, and 1.0≦a≦3.0, 1.0≦b≦4.0, 0≦c≦1.0, 3.0≦d≦19.0) and has an enthalpy at absolute zero of −1.8132 to −0.7429 eV / atom, and the composition of the chloride-based solid electrolyte having this enthalpy can be determined as shown in the flowchart in FIG. 2. Hereinafter, the procedure for determining the composition of the chloride-based solid electrolyte will be described in accordance with this flowchart.

[0025] <Step 1> The physical properties and crystal structure data of compositions containing Li and Cl (or Li, O, Cl) listed in the Materials Project are extracted using an API (Application Programming Interface). The Materials Project is a database of first-principles calculation results, and in addition to crystal structures, it also contains various data on materials, such as band structures, thermodynamic quantities, phase diagrams, and magnetic moments. Examples of compositions containing Li and Cl include LiGaCl4 and LiFeCl4. Examples of compositions containing Li, O, and Cl include LiAlS3(Cl2O3)2 and LiFeMoClO4.

[0026] <Step 2> Next, similarly, the physical properties and crystal structure data of compositions containing Li and halogen species: M (or Li, O, M) listed in the Materials Project are extracted, and all M is replaced with Cl and added to the data in Step 1. M is a halogen species other than Cl (F, Br, I), and examples of compositions containing Li and M include Li2VF6, K2LiSbBr6, and Cs2LiYI6. Examples of compositions containing Li, O, and M include LiVOF3, Li4Al3Ge3BrO 12 , Li4Ga3Si3IO 12By substituting halogen species other than Cl (F, Br, I) for Cl, Li2VCl6, K2LiSbCl6, Cs2LiYCl6, LiVOCl3, Li4Al3Ge3ClO 12 , Li4Ga3Si3ClO 12 Compositions obtained by element substitution in this way include compositions containing Li and Cl (or Li, O, and Cl), which are not listed in the Materials Project, and therefore candidates for chloride-based solid electrolytes according to embodiments of the present invention can be selected from a wide range of compositions other than those listed in the Materials Project.

[0027] <Step 3> Next, compositions containing protons, rare gases, and actinide elements are excluded from the data obtained in step 2. Specifically, compositions containing H, He, Ne, Ar, Kr, Xe, Ac, Th, Pa, U, Np, Pu, etc. are excluded. Examples of compositions that are excluded include LiThF5, LiHF2, LiH2ClO5, and Li6Zr6HCl. 18 , Li4H3ClO3, Li2UC l6 etc. Next, from the remaining compositions, materials that satisfy the following formula 1 are further excluded. E_above_hull > 0.05 ··· (Formula 1) Here, "E_above_hull" is a thermodynamic index that indicates how energetically unfavorable it is from the thermodynamic convex hull, and is data listed in the Materials Project. Stable phases indicate 0. A small "E_above_hull" indicates that the phase is easy to produce experimentally (stable). Therefore, excluding compositions that satisfy Equation 1 means that only compositions with an "E_above_hull" of 0.05 or less remain, indicating that the extracted data will only include very stable compositions.

[0028] <Step 4> Next, for each crystal structure of the composition obtained in step 3, the abc axis of the crystal lattice is made into a supercell at the same magnification so that the total number of atoms is more than 60. The crystal structure is data listed in the Materials Project. Supercelling is one of the simulation calculation methods, and is a method in which the cell (periodically repeated unit) when simulating with periodic boundary conditions is made into a supercell larger than the theoretically smallest unit cell (unit lattice). Under periodic boundary conditions, the macroscopic system (10 23 Microscopic systems (10 order) that can be handled by simulation 9 Boundaries are set so that the total number of atoms can be expressed in units of order (order of magnitude). However, if the cell size for MD calculations is small due to the boundary conditions, the target atom will be affected by overlapping contributions from each atom, making it impossible to obtain the correct interactions (energy and force). For this reason, the cell size is typically set to at least twice the potential cutoff distance (in Matlantis®, described below, this is up to r = 6 Å between neighboring atoms). The above issue can be resolved by using the Supercell method to expand the cell in each abc axis direction (expanding by a factor of α as shown in the formula below). The above "total number of atoms exceeding 60" is an arbitrary threshold; the unit cells of each composition material extracted using API are transformed to the same size using the formula below so that the number of atoms exceeds 60. α (integer) = Function that returns the smallest integer greater than or equal to the given number ((60 ÷ number of atoms in the unit cell) (1 / 3) )

[0029] <Step 5> Next, based on the crystal lattice supercelled in step 4, a structural relaxation (structural optimization) calculation is performed using a machine learning potential. More specifically, the structural relaxation calculation is performed using Matlantis (registered trademark), a neural network potential. The structural relaxation calculation calculates the locally stable coordinates of the system and their energy, and optimizes the force applied to each atom. The structural relaxation calculation also optimizes the model to perform the NVT-MD calculation described below with the optimal lattice length. In the structural relaxation, the initial coordinates of the system are determined, and a locally stable point is found from the initial coordinates to find the stable structure. The structural relaxation algorithm uses the Matlantis Atomic Simulation Environment (ASE), a local structural relaxation algorithm. The structural relaxation calculation is performed using the optimization algorithm implemented in the ASE. Specifically, the local structural relaxation algorithm, ASE, uses the BFGS method, which approximates the Hessian from the optimization trajectory and executes the Newton method algorithm using the approximate Hessian. Calculate the enthalpy at absolute zero using the data optimized by the BFGS method (iterator: iterator = 10, maximum argument value: fmax = 5.0 × 10 -3 ) will be implemented.

[0030] Specifically, the enthalpy calculation is carried out in the following procedure. (1) Calculate the energy of a single atom (E_atom) for each element and compile it in a CSV table (a data sheet for enthalpy calculations). (2) Next, for the stable structure (energy E0) that was created as a Supercell in step 5 above and subjected to structural relaxation calculations, use the data sheet in (1) to calculate the enthalpy (H_0) at absolute zero using the following formula: H_0 = E0-Σ(E_atom) Here, E0 is the ground state energy, and Σ(E_atom) is the sum of the energies of each element in the composition.

[0031] <Step 6> Next, Matlantis performs NVT-MD (100 ps) calculations at a specific temperature (800 K) on the most stable structure (ground state) at absolute zero obtained in the structural relaxation calculation in Step 5. MD simulation is a computer simulation of the physical motion of atoms and molecules. It solves Newton's equations of motion in classical mechanics for each atom that makes up a substance, tracking the time evolution of atomic positions and energy to predict the physical properties of the system. NVT-MD is a canonical ensemble (canonical state distribution) MD simulation, which assumes constant temperature and volume and can be used when volume and temperature changes are to be ignored. The NVT-MD (100 ps) calculation predicts the diffusion coefficient D of Li. When measuring the diffusivity (e.g., diffusion coefficient) of Li at room temperature, the time scale is on the order of several hours. However, such time scales cannot be reproduced in atomic simulations, so the simulation is performed on an extremely short calculation scale of 100 ps. The jump frequency of Li self-diffusion is very small at room temperature, so it is unrealistic to observe Li hopping to neighboring sites due to vibrations around a fixed point at around 100 ps. For this reason, we performed simulations at a high temperature of 800 K to observe Li hopping due to thermal vibrations and evaluate the diffusivity of Li in the material.

[0032] <Step 7> Next, based on the diffusion coefficient D of Li obtained in step 6, the MSD (mean square displacement) at a specific temperature (800K) is calculated. MSD is a statistical processing index that indicates the magnitude of movement, and is the average value of the square of the distance between the start point and end point of movement within a certain time span. The formula for calculating MSD is shown in Equation (2) below.

[0033] [ka]

[0034] In equation (2), N is the number of atoms in the grain boundary, r α (t+t0) is the position of the first atom at time t[s], rα (t0) indicates the position of the first atom in the initial configuration.

[0035] Next, the diffusion coefficient of Li (D) [m 2 / s] is calculated. MSD = 2dDt (Equation 3) In equation (3), d represents the dimension of diffusion, and t represents time [s]. The diffusion coefficient D [m 2 / s] is calculated from the slope of the MSD, where the horizontal axis is time [ps] and the vertical axis is MSD [Å], and the units are converted as follows: ps, Å system → m 2 / sec conversion D = (slope of MSD) × 1e -16 (Å 2 )×1e 12 (ps) / 6

[0036] <Step 8> Next, add the diffusion coefficients obtained in step 7 to the table listing the compositions (compositions obtained in step 3) listed in the Materials Project. This will create a list that combines the physical properties of each composition with its diffusion coefficient.

[0037] <Step 9> Next, the compositions listed in the table obtained in step 8 are ranked by lithium diffusion coefficient based on materials reported in publicly known experimental papers (previously reported materials).

[0038] <Step 10> Next, from the compositions ranked in Step 9, those with Li diffusion coefficients above a predetermined value are selected and labeled with their structural features. Structural features include whether the compound is a layered compound or not, and whether the cation polyhedra in the crystal are joined by point contacts. Layered compounds are acceptable, but because the Li diffusion path in layered compounds is a layered structure (2D plane), they may have inferior diffusivity compared to materials with 3D Li diffusion. Furthermore, structures in which the cation polyhedra in the crystal are joined by point contacts are expected to be particularly good ionic conductors. The large number of interstitial spaces is expected to form paths for Li atoms, increasing the 3D diffusivity of Li within the material. For example, in the crystal structure of LiFeCl4, LiCl tetrahedra and FeCl tetrahedra are joined by point contacts. In this way, compositions having a Li diffusion coefficient equal to or greater than a predetermined value and having favorable structural characteristics are selected as promising candidate compositions for chloride-based solid electrolytes according to embodiments of the present invention. 12 , or Li3Zr4Cl 19 , LiVOCl3, LiVOCl4, Li6V2OCl 11 , Li2V2OCl6, Li2VOCl4, Li2VOCl5, Li3VOCl5, etc. are selected as the composition of the chloride-based solid electrolyte according to an embodiment of the present invention.

[0039] In the embodiment of the present invention, the diffusion coefficient (D) [m 2 / s] to the activation energy (E A ) [J / mol] can be calculated based on the Arrhenius equation shown in the following formula (4).

[0040] [ka]

[0041] In equation (4), D0 is the frequency factor, R is the gas constant [J / mol·K], and T is the absolute temperature [K].

[0042] Furthermore, the obtained diffusion coefficient of Li (D) [m 2 / s] to (D) [cm 2 / s], the ionic conductivity (σ) [S / cm] can be calculated based on the Nernst-Einstein equation shown in the following formula (5).

[0043] [ka]

[0044] In equation (5), z is the valence of the ion, F is the Faraday constant [C / mol], and c is the concentration of the ion [mol / cm 3 ], R is the gas constant [J / mol K], and T is the absolute temperature [K].

[0045] The composition of the chloride-based solid electrolyte according to the embodiment of the present invention may be determined from the promising candidate compositions obtained as described above. Specifically, the activation energy (E A ) is 28.0 [kJ / mol] or less can be considered as promising candidate compositions. After the composition is determined, the chloride-based solid electrolyte according to the embodiment of the present invention is actually produced as follows. First, raw materials are weighed to obtain a predetermined composition in a glove box filled with an inert gas atmosphere such as argon gas or nitrogen gas. Examples of the raw materials used here include LiCl, LiVO, LiTaO, LiFe, LiBi, LiGa, and LiZr. Other known compounds of Li with V, Ta, Fe, Bi, Ga, or Zr may also be used as raw materials.

[0046] Next, the materials are mixed for 5 to 30 minutes using a ball mill or the like to produce a mixed powder. At this time, it is preferable to mix for a time such that the average particle size of the mixed powder becomes 5 to 40 μm. By mixing the raw materials in a ball mill or the like in this way, mechanochemical synthesis (synthesis that uses mechanical energy to promote chemical reactions) is performed, making it possible to produce a chloride-based solid electrolyte with reduced activation energy.

[0047] Next, the mixed powder is pelletized, vacuum sealed in a quartz ampoule, and fired together with the quartz ampoule at 400 to 800°C for 1 to 20 hours to obtain a compound having the composition formula: Li a M b O c Cl d (wherein M is one or two of V, Ta, Fe, Sb, Ga, W, and Zr, and 1.0≦a≦3.0, 1.0≦b≦4.0, 0≦c≦1.0, and 3.0≦d≦19.0), a chloride-based solid electrolyte according to an embodiment of the present invention can be produced.

[0048] The composition of the prepared chloride-based solid electrolyte can be determined by, for example, weighing out 0.5 g of a sample of chloride-based solid electrolyte particles, dissolving it in various acids, and then analyzing it using an inductively coupled plasma optical emission spectrometer (ICP-OES) "PS7800" manufactured by Hitachi High-Tech Corporation.

[0049] (All-solid-state lithium-ion battery) A solid electrolyte layer can be formed using the chloride-based solid electrolyte according to the embodiment of the present invention, and an all-solid-state lithium-ion battery can be fabricated that includes the solid electrolyte layer, a positive electrode layer, and a negative electrode layer. The positive electrode layer and the negative electrode layer that constitute the all-solid-state lithium-ion battery according to the embodiment of the present invention are not particularly limited and can be formed of known materials and can have a known configuration as shown in FIG.

[0050] The positive electrode layer of the lithium ion battery is formed into a layer of a positive electrode mixture obtained by mixing a known positive electrode active material for lithium ion batteries with the chloride-based solid electrolyte according to the embodiment of the present invention or another chloride-based solid electrolyte.

[0051] The positive electrode mixture may further contain a conductive additive. Examples of the conductive additive include carbon materials, metal materials, and mixtures thereof. The conductive additive may include at least one element selected from the group consisting of carbon, nickel, copper, aluminum, indium, silver, cobalt, magnesium, lithium, chromium, gold, ruthenium, platinum, beryllium, iridium, molybdenum, niobium, osmium, rhodium, tungsten, and zinc. The conductive additive is preferably a highly conductive carbon element, or a metal element, mixture, or compound containing carbon, nickel, copper, silver, cobalt, magnesium, lithium, ruthenium, gold, platinum, niobium, osmium, or rhodium. Examples of the carbon material include carbon black, such as ketjen black, acetylene black, denka black, thermal black, and channel black; graphite; carbon fiber; and activated carbon.

[0052] The average thickness of the positive electrode layer of the lithium ion battery is not particularly limited and can be appropriately designed depending on the purpose. The average thickness of the positive electrode layer of the lithium ion battery may be, for example, 1 μm to 100 μm, or 1 μm to 10 μm.

[0053] The method for forming the positive electrode layer of the lithium ion battery is not particularly limited and can be appropriately selected depending on the purpose. Examples of the method for forming the positive electrode layer of the lithium ion battery include sputtering using a target material of the positive electrode active material and compression molding of the positive electrode active material.

[0054] The negative electrode layer of the lithium ion battery may be a layer of a known negative electrode active material for lithium ion batteries, or may be a layer of a negative electrode mixture obtained by mixing a known negative electrode active material for lithium ion batteries with the chloride-based solid electrolyte according to the embodiment of the present invention or another chloride-based solid electrolyte.

[0055] The negative electrode layer, like the positive electrode layer, may contain a conductive additive. The conductive additive may be the same material as described for the positive electrode layer. Examples of the negative electrode active material include carbon materials, specifically, artificial graphite, graphite carbon fiber, resin-baked carbon, pyrolytic vapor-grown carbon, coke, mesocarbon microbeads (MCMB), furfuryl alcohol resin-baked carbon, polyacene, pitch-based carbon fiber, vapor-grown carbon fiber, natural graphite, and non-graphitizable carbon, or mixtures thereof. Examples of the negative electrode material include metals such as lithium metal, indium metal, aluminum metal, and silicon metal, as well as alloys of these metals combined with other elements or compounds.

[0056] The average thickness of the negative electrode layer of the lithium ion battery is not particularly limited and can be appropriately selected depending on the purpose. The average thickness of the negative electrode layer of the lithium ion battery may be, for example, 1 μm to 100 μm, or 1 μm to 10 μm.

[0057] The method for forming the negative electrode layer of the lithium ion battery is not particularly limited and can be appropriately selected depending on the purpose. Examples of the method for forming the negative electrode layer of the lithium ion battery include sputtering using a target material of the negative electrode active material, compression molding of the negative electrode active material, and vapor deposition of the negative electrode active material.

[0058] The average thickness of the solid electrolyte layer of the lithium ion battery formed using the chloride-based solid electrolyte according to the embodiment of the present invention is not particularly limited and can be appropriately designed depending on the purpose. The average thickness of the solid electrolyte layer of the lithium ion battery may be, for example, 50 μm to 500 μm, or 50 μm to 100 μm.

[0059] The method for forming the solid electrolyte layer of the lithium ion battery is not particularly limited and can be appropriately selected depending on the purpose. Examples of the method for forming the solid electrolyte layer of the lithium ion battery include sputtering using a target material for the solid electrolyte and compression molding of the solid electrolyte.

[0060] Other components constituting the lithium ion battery are not particularly limited and can be appropriately selected depending on the purpose. Examples include a positive electrode current collector, a negative electrode current collector, and a battery case.

[0061] The size and structure of the positive electrode current collector are not particularly limited and can be appropriately selected depending on the purpose. Examples of materials for the positive electrode current collector include die steel, stainless steel, aluminum, aluminum alloys, titanium alloys, copper, gold, and nickel. The positive electrode current collector may be in the form of, for example, a foil, a plate, or a mesh. The average thickness of the positive electrode current collector may be, for example, 10 μm to 500 μm, or 50 μm to 100 μm.

[0062] The size and structure of the negative electrode current collector are not particularly limited and can be appropriately selected depending on the purpose. Examples of materials for the negative electrode current collector include die steel, gold, indium, nickel, copper, and stainless steel. The negative electrode current collector may be in the form of, for example, a foil, a plate, or a mesh. The average thickness of the negative electrode current collector may be, for example, 10 μm to 500 μm, or 50 μm to 100 μm.

[0063] The battery case is not particularly limited and can be appropriately selected depending on the purpose, and examples thereof include known laminate films that can be used in conventional all-solid-state batteries, such as resin laminate films and films in which metal is vapor-deposited on resin laminate films. The shape of the battery is not particularly limited and can be appropriately selected depending on the purpose. Examples include cylindrical, square, button, coin, and flat types. [Example]

[0064] The following examples are provided to provide a better understanding of the present invention and its advantages, but the present invention is not limited to these examples.

[0065] Example 1 In Example 1, the composition of the chloride-based solid electrolyte was determined as follows, based on the flow chart shown in FIG.

[0066] <Step 1> First, the physical properties of compositions containing Li and Cl, and the physical properties of compositions containing Li, O, and Cl, as well as the respective crystal structure data, were extracted using an API (Application Programming Interface).

[0067] <Step 2> Next, similarly, the physical properties of compositions containing Li and halogen species: M, as well as the physical properties of compositions containing Li, O, and M, as well as the respective crystal structure data, were extracted, and all M was replaced with Cl and added to the data in Step 1. M was set to halogen species other than Cl (F, Br, I).

[0068] <Step 3> Next, compositions containing protons, rare gases, and actinide elements were excluded from the data obtained in Step 2. Specifically, compositions containing H, He, Ne, Ar, Kr, Xe, Ac, Th, Pa, U, Np, Pu, etc. were excluded. Next, from the remaining compositions, materials that satisfy the following formula 1 were further excluded. E_above_hull > 0.05 ··· (Formula 1) This resulted in extractable data limited to very stable compositions.

[0069] <Step 4> Next, for each crystal structure of the composition obtained in Step 3, the abc axis of the crystal lattice was magnified to create a supercell so that the total number of atoms was greater than 60. The crystal structure data was listed in the Materials Project. The "total number of atoms greater than 60" was determined arbitrarily using a single threshold, and the unit cell of each composition material extracted using API was transformed to the same size using the following formula so that the number of atoms was greater than 60. α (integer) = Function that returns the smallest integer greater than or equal to the given number ((60 ÷ number of atoms in the unit cell) (1 / 3) )

[0070] <Step 5> Next, based on the crystal lattice supercelled in step 4, structural relaxation (structural optimization) calculations were performed using a machine learning potential. More specifically, structural relaxation calculations were performed using Matlantis (registered trademark), a neural network potential. The structural relaxation algorithm used was the ASE (Atomic Simulation Environment) of Matlantis, a local structural relaxation algorithm, and structural relaxation calculations were performed using the optimization algorithm implemented in the ASE. Specifically, in the ASE, a local structural relaxation algorithm, the BFGS method was used, which approximates the Hessian from the optimization trajectory and executes the Newton method algorithm using the approximate Hessian. The data optimized by the BFGS method was used to calculate the enthalpy at absolute zero (iterator: iter = 10, maximum argument value: fmax = 5.0 × 10 -3 ) was carried out.

[0071] The enthalpy calculation was carried out specifically according to the following procedure. (1) The energy of a single atom (E_atom) for each element was calculated and compiled in a CSV table (a data sheet for enthalpy calculations). (2) Next, for the stable structure (energy E0) that was created as a Supercell in step 5 above and subjected to structural relaxation calculations, the enthalpy (H_0) at absolute zero was calculated using the data sheet in (1) using the following formula: H_0 = E0-Σ(E_atom) Here, E0 is the ground state energy, and Σ(E_atom) is the sum of the energies of each element in the composition.

[0072] <Step 6> Next, NVT-MD (100 ps) calculations were performed at a specific temperature (800 K) using Matlantis for the most stable structure (ground state) at absolute zero obtained in the structural relaxation calculation in Step 5. The diffusion coefficient D of Li was predicted using this NVT-MD (100 ps) calculation.

[0073] <Step 7> Next, the mean square displacement (MSD) at a specific temperature (800 K) was calculated based on the diffusion coefficient D of Li obtained in step 6. The formula for calculating the MSD is shown in the following formula (2).

[0074] [ka]

[0075] In equation (2), N is the number of atoms in the grain boundary, r α (t+t0) is the position of the first atom at time t[s], r α (t0) indicates the position of the first atom in the initial configuration.

[0076] Next, the diffusion coefficient of Li (D) [m 2 / s] was calculated. MSD = 2dDt (Equation 3) In equation (3), d represents the dimension of diffusion, and t represents time [s]. The diffusion coefficient D [m 2 / s] is calculated from the slope of the MSD, where the horizontal axis is time [ps] and the vertical axis is MSD [Å], and the units are converted as follows: ps, Å system → m 2 / sec conversion D = (slope of MSD) × 1e -16 (Å 2 )×1e 12 (ps) / 6

[0077] <Step 8> Next, the diffusion coefficients obtained in Step 7 were added to the table listing the compositions (compositions obtained in Step 3) listed in the Materials Project. This resulted in a list combining the physical properties of each composition with its diffusion coefficient.

[0078] <Step 9> Next, the compositions listed in the table obtained in step 8 were ranked by lithium diffusion coefficient based on materials reported in publicly known experimental papers (previously reported materials).

[0079] <Step 10> Next, from the compositions ranked in step 9, we selected those with a Li diffusion coefficient of 7.3428E-06 [cm / s] or higher for chloride-based solid electrolytes and 4.98053E-06 [cm / s] or higher for oxychloride-based solid electrolytes, and labeled their structural features. The structural feature was whether the cation polyhedra in the crystal were joined by point contact (corner sharing) or line contact (corner + edge sharing). In this way, compositions with a diffusion coefficient equal to or greater than a predetermined value and favorable structural characteristics were selected as promising candidate compositions for chloride-based solid electrolytes. 12 , or Li3Zr4Cl 19 , LiVOCl3, LiVOCl4, Li6V2OCl 11, Li2V2OCl6, Li2VOCl4, Li2VOCl5, and Li3VOCl5 were selected as the composition of the chloride-based solid electrolyte according to Example 1.

[0080] In addition, the diffusion coefficient of Li (D) [m 2 / s] to the activation energy (E A ) [J / mol] was calculated based on the Arrhenius equation shown in the following formula (4).

[0081] [ka]

[0082] In equation (4), D0 is the frequency factor, R is the gas constant [J / mol·K], and T is the absolute temperature [K].

[0083] From the above simulation, LiVCl5, LiVCl6, Li2VCl5, Li2VCl6, LiFeCl4, LiGaCl4, LiWCl6, LiSb2Cl7, Li3V2Cl were selected as the composition of the chloride-based solid electrolyte according to Example 1. 12 , or Li3Zr4Cl 19 , LiVOCl3, LiVOCl4, Li6V2OCl 11 , Li2V2OCl6, Li2VOCl4, Li2VOCl5, Li3VOCl5, the diffusion coefficient of Li (D) [m 2 / s], enthalpy at absolute zero (0 K) [eV / atom], activation energy (E A ) [J / mol] and structural characteristics are shown in Table 1 below. In Table 1, there are several examples where the evaluation results differ even for the same composition, but this is because, as mentioned above, halide materials are substituted with chlorine and added as chloride data. Also, because the crystal symmetry of the original structure (e.g., orthorhombic, cubic, tetragonal) is maintained, the diffusion coefficient values may differ even for the same composition. As a reference example, the diffusion coefficient of Li (D) [m 2 / s], enthalpy at absolute zero (0 K) [eV / atom], activation energy (E A ) [J / mol] and structural characteristics are shown in Table 1 below.

[0084] [Table 1]

[0085] (Consideration) According to the simulation described in Example 1, the composition formula: Li a M b O c Cl d (wherein M is one or two of V, Ta, Fe, Sb, Ga, W, and Zr, and 1.0≦a≦3.0, 1.0≦b≦4.0, 0≦c≦1.0, 3.0≦d≦19.0) Several chloride-based solid electrolytes were obtained with absolute zero enthalpies of -1.8132 to -0.7429 eV / atom and low activation energies of 28.0 kJ / mol or less. All of these were predicted to have good ionic conductivity when used in all-solid-state lithium-ion batteries. In addition, the chloride-based solid electrolyte LiAlCl4 is a garnet-type LiAlCl4, which is attractive for application to sintered bodies for metallic lithium electrodes. 6.6 La3Zr 1.6 Ta 0.4 O 12 It is known to have good ionic conductivity, with an ionic conductivity one order of magnitude higher than that of LLZT (see the paper below, "Metastable Chloride Solid Electrolyte with High Formability for Rechargeable All-Solid-State Lithium Metal Batteries"), which is thought to be due to the fact that it is a material with a low energy barrier (activation energy) required for lithium ions to move through the crystal lattice. Naoto Tanibata, Shuta Takimoto et al., “Metastable Chloride Solid Electrolyte with High Formability for Rechargeable All-Solid-State Lithium Metal Batteries” (web site: https: / / pubs.acs.org / doi / abs / 10.1021 / acsmaterialslett.0c00127) In the simulation results in Table 1 above, LiAlCl4 is determined to be a chloride-based solid electrolyte with a low activation energy. Therefore, it can be seen that the simulation results above actually indicate a material with a low energy barrier (activation energy).

Claims

1. Composition formula: Li a M b O c Cl d (In the formula, M represents one or two of V, Ta, Fe, Sb, Ga, W, and Zr, and 1.0≦a≦3.0, 1.0≦b≦4.0, 0≦c≦1.0, and 3.0≦d≦19.0.) and the enthalpy at absolute zero is -1.8132 to -0.7429 eV / atom.

2. 2. The chloride-based solid electrolyte according to claim 1, wherein d satisfies the formula 3.0≦d≦4.

0.

3. An all-solid-state lithium ion battery comprising a solid electrolyte layer containing the chloride-based solid electrolyte according to claim 1 or 2, a positive electrode layer, and a negative electrode layer.

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