Apparatus for deriving design plan and method thereof

Abstract

The present document relates to an apparatus for deriving a design plan, the apparatus comprising: a memory configured to store at least one instruction; and a processor configured to execute the at least one instruction, wherein the processor is configured to acquire target material data, derive first data including raw material data from the target material data, derive second data including reaction equation data from the first data, derive third data by selecting the second data according to a supply-and-demand criterion, and generate a design plan on the basis of the third data.

Description

Device and method for deriving design proposals

[0001] Cross-citation with related applications

[0002] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2024-0183528 filed on December 11, 2024, and includes all contents disclosed in the document of said Korean patent application as part of this specification.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to an apparatus and a method for deriving a design proposal.

[0005] Rechargeable battery technology has made rapid progress in recent years. Rechargeable batteries are rechargeable batteries and can be interpreted to include not only conventional nickel-cadmium batteries, nickel-metal hybrid batteries, and lead-acid batteries, but also lithium-ion batteries. In particular, lithium-ion batteries play an essential role in electric vehicles (EVs), energy storage systems (ESS), and portable electronic devices.

[0006] Rechargeable batteries consist of electrodes and electrolytes, and the electrodes include a positive electrode and a negative electrode. In the case of lithium-ion batteries, the positive and negative electrodes perform the function of inserting or releasing lithium ions, and in this process, electrical energy can be stored or released as chemical energy. Lithium cobalt oxide (LiCoO2), lithium nickel manganese cobalt oxide (LiNiMnCoO2, NCM), and lithium nickel cobalt aluminum oxide (LiNiCoAlO2, NCA) are mainly used as positive electrode active materials, while graphite is primarily used as the negative electrode active material. However, research on next-generation negative electrode materials such as silicon (Si) and lithium metal (Li metal) is currently being conducted.

[0007] The safety and performance of secondary batteries can vary depending on the materials of their respective components. Furthermore, the safety and performance of secondary batteries can be enhanced by coating the positive and negative active materials. Accordingly, research is being conducted on novel materials applicable as components or coating materials, as well as methods for synthesizing them, to improve the performance and safety of secondary batteries.

[0008] According to the embodiments disclosed in this document, we intend to provide an apparatus and a method for deriving a design that generates a design capable of synthesizing target material data from target material data.

[0009] According to the embodiments disclosed in this document, we intend to provide an apparatus and a method for generating a design proposal based on raw material data and reaction equation data derived from target material data.

[0010] According to the embodiments disclosed in this document, we intend to provide an apparatus and a method for deriving a design that generates an effective design from target material data in terms of reaction energy and supply costs.

[0011] The technical problems of this document are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the descriptions below.

[0012] A design proposal derivation device according to one embodiment of the present document is a design proposal derivation device comprising a memory configured to store at least one instruction and a processor configured to execute said at least one instruction, wherein the processor may be configured to acquire target material data and derive first data including raw material data from said target material data, derive second data including reaction formula data from said first data, select said second data according to supply and demand criteria to derive third data, and generate a design proposal based on said third data.

[0013] According to one embodiment, the processor derives filtered data selected based on whether the target material data corresponds to a negative factor, and derives the first data based on the filtered data.

[0014] According to one embodiment, the negative factor may be pre-set based on toxicity and legal compliance.

[0015] According to one embodiment, the processor can identify a cation included in the target material data and combine an anion capable of binding to the cation with the cation to derive the raw material data.

[0016] According to one embodiment, the processor can derive the first data by filtering the raw material data based on its actual existence and availability.

[0017] According to one embodiment, the processor may derive the reaction equation data by combining the raw material data, and derive the second data based on the reaction equation data, reaction energy, and side reaction characteristic information.

[0018] According to one embodiment, the processor can derive the second data among the reaction equation data in which the reaction energy is less than or equal to a reference energy and the side reaction characteristic information is less than or equal to a reference reaction value.

[0019] According to one embodiment, the supply and demand criteria include a supply cost and a supply and demand quantity, and the processor can derive data from the second data in which the supply cost is less than or equal to a reference cost and the supply and demand quantity is greater than or equal to a reference supply quantity as the third data.

[0020] According to one embodiment, the processor may organize the third data into a data frame according to priority and generate the data frame as the design plan.

[0021] A method for deriving a design proposal according to one embodiment of the present document may include the steps of: acquiring target material data; deriving first data including raw material data from the target material data; deriving second data including reaction formula data from the first data; deriving third data by selecting the second data according to supply and demand criteria; and generating a design proposal based on the third data.

[0022] According to one embodiment, the method may include the step of deriving filtered data selected based on whether the target substance data corresponds to a negative factor, and the step of deriving the first data based on the filtered data.

[0023] According to one embodiment, the negative factor may be pre-set based on toxicity and legal compliance.

[0024] According to one embodiment, the step of deriving the first data may include the step of identifying a cation included in the target material data, and the step of deriving the raw material data by combining an anion capable of binding to the cation with the cation.

[0025] According to one embodiment, the step of deriving the first data may include the step of filtering the raw material data based on its actual existence and availability.

[0026] According to one embodiment, the step of deriving the second data may include the step of deriving the reaction formula data by combining the raw material data, and the step of deriving the second data based on the reaction formula data, reaction energy, and side reaction characteristic information.

[0027] According to one embodiment, the step of deriving the second data may be characterized by deriving the second data as the data in which the reaction energy among the reaction equation data is less than or equal to the reference energy and the side reaction characteristic information is less than or equal to the reference reaction value.

[0028] According to one embodiment, the supply and demand criteria include a supply cost and a supply and demand quantity, and the step of deriving the third data may be characterized by deriving the data in the second data where the supply cost is less than or equal to a standard cost and the supply and demand quantity is greater than or equal to a standard supply quantity as the third data.

[0029] According to one embodiment, the step of generating the design proposal may be characterized by configuring the third data into a data frame according to priority and generating the data frame as the design proposal.

[0030] A computer-readable medium according to one embodiment of the present document may record a program for executing the design proposal derivation method on a computer.

[0031] The present technology can provide a design proposal derivation device and a method for generating a design proposal capable of synthesizing target material data from target material data.

[0032] In addition, the present technology can provide a design proposal derivation device and a method for generating a design proposal based on raw material data and reaction equation data derived from target material data.

[0033] In addition, the present technology can provide a design derivation device and a method for generating a design that is advantageous in terms of reaction energy and supply costs from target material data.

[0034] In addition, various effects that can be identified directly or indirectly through this document may be provided.

[0035] FIG. 1 is a block diagram showing the structure of a design proposal derivation system according to one embodiment of the present document.

[0036] FIG. 2 is a block diagram showing the configuration of a design proposal derivation device according to one embodiment of the present document.

[0037] FIG. 3a is a drawing for explaining the derivation of raw material data according to one embodiment of the present document.

[0038] FIGS. 3b and FIGS. 3c are drawings for illustrating a data frame according to one embodiment of the present document.

[0039] FIG. 3d is a drawing for explaining a design plan according to one embodiment of the present document.

[0040] FIG. 4 is a flowchart illustrating a method for deriving a design proposal according to one embodiment of the present document.

[0041] FIG. 5 is a block diagram showing the hardware configuration of a computing system for performing a method for deriving a design proposal according to one embodiment of the present document.

[0042] Hereinafter, various embodiments of this document will be described in detail with reference to the attached drawings. To clarify this document, parts unrelated to the explanation have been omitted, and throughout this document, the same reference numerals are used for identical or similar components, and redundant descriptions are omitted.

[0043] Furthermore, the various embodiments of this document may be implemented in various forms and should not be interpreted as being limited to the embodiments described in this document. The embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives.

[0044] To describe the components of the embodiments of this document, terms such as first, second, A, B, (a), (b), etc., may be used. These terms are intended merely to distinguish the components from other components and do not limit the nature or order of the components. Furthermore, unless otherwise defined, all terms used in this document, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document belong. Terms or words used in this document should not be interpreted as being limited to their ordinary or dictionary meanings, but must be interpreted in a meaning and concept consistent with the technical spirit of this document.

[0045] According to various embodiments of this document, each component may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the aforementioned components or steps may be omitted, or one or more other components or operations may be added. Additionally, according to various embodiments, multiple components may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as they were performed by the corresponding component among the multiple components prior to integration. According to various embodiments of this document, operations performed by a module, program, or other component may be executed sequentially, in parallel, or repeatedly, one or more of the operations may be executed in a different order, or one or more other operations may be added.

[0046] The embodiments of the present document will be described in detail below with reference to FIGS. 1 to 5.

[0047] FIG. 1 is a block diagram showing the structure of a design proposal derivation system according to one embodiment of the present document.

[0048] Referring to FIG. 1, a design proposal derivation system (10) according to one embodiment of the present document may include a design proposal derivation device (100) and a server (200).

[0049] According to one embodiment, the design proposal derivation device (100) may be a computing device that performs a design proposal derivation method according to one embodiment of the present document. The design proposal derivation device (100) may be an electronic device equipped with a computer program for performing a design proposal derivation method according to one embodiment of the present document.

[0050] According to one embodiment, a design proposal derivation device (100) can acquire target material data and generate a design proposal for synthesizing the target material data. Here, the target material data is information of the target material to be the subject of the design proposal, and may include the name or chemical formula of the target material.

[0051] According to one embodiment, the design proposal derivation device (100) can acquire target material data input by a user. Additionally, the design proposal derivation device (100) can acquire target material data received from a user terminal (not shown) from the user terminal (not shown).

[0052] According to one embodiment, the design proposal derivation device (100) can derive first data including raw material data from target material data. Specifically, the design proposal derivation device (100) can identify a cation from the target material data, derive an anion capable of combining with the cation, and derive raw material data by combining the cation and the anion.

[0053] According to one embodiment, a design proposal derivation device (100) can derive second data including reaction formula data from first data including raw material data. Specifically, the design proposal derivation device (100) can derive reaction formula data by combining raw material data and derive second data based on reaction energy and side reaction information of the reaction formula data. In addition, the design proposal derivation device (100) can derive second data from reaction formula data by further considering specific reaction conditions.

[0054] According to one embodiment, the design proposal derivation device (100) can derive third data by selecting second data according to supply and demand criteria. Specifically, the design proposal derivation device (100) can derive third data from the second data in which the supply cost is less than or equal to the standard cost and the supplyable quantity is greater than or equal to the standard supply quantity.

[0055] According to one embodiment, the design proposal derivation device (100) can generate a design proposal based on third data. Specifically, the design proposal derivation device (100) can organize the third data into a data frame according to priority and generate the data frame as a design proposal.

[0056] According to one embodiment, a design proposal derivation device (100) may communicate with a server (200) to request necessary data from the server (200) and obtain necessary data from the server (200). For example, the design proposal derivation device (100) may request toxicity information or legal compliance information of target material data from the server (200) and obtain toxicity information or legal compliance information of target material data from the server (200). Additionally, the design proposal derivation device (100) may request existence information related to the actual existence of raw material data or supply information related to supply availability from the server (200) and obtain existence information or supply information of raw material data from the server (200).

[0057] For example, the design proposal derivation device (100) may request standard enthalpy information of reactants and products from the server (200) to calculate the reaction energy of the reaction equation data, and obtain standard enthalpy information of reactants and products from the server (200). In addition, the design proposal derivation device (100) may request information on the supply cost and available supply amount of each raw material data from the server (200), and obtain information on the supply cost and available supply amount of raw material data from the server (200).

[0058] According to one embodiment, the server (200) may be a computing device that manages a database. For example, the server (200) may be a computing device that manages a database of toxicity information and legal conformity information of compounds. Additionally, the server (200) may be a computing device that manages a database of compound supply information. The server (200) may be a computing device that manages a database of standard enthalpy information of compounds.

[0059] According to one embodiment, the server (200) may be configured as a computing device that manages a plurality of databases. According to another embodiment, the server (200) may be configured as a separate computing device for each database managed. Although the server (200) is depicted as a single configuration in FIG. 1, this is merely an example and the server (200) may include a plurality of computing devices.

[0060] According to one embodiment, the server (200) may communicate with the design proposal derivation device (100) and transmit data included in a database to the design proposal derivation device (100) in response to a request from the design proposal derivation device (100). For example, when the server (200) receives a request from the design proposal derivation device (100) for toxicity information or legal compliance information of target substance data, the server (200) may search for the corresponding target substance data in a compound toxicity database or legal compliance database managed by the server (200). Accordingly, the server (200) may transmit the toxicity information or legal compliance information of target substance data to the design proposal derivation device (100).

[0061] For example, when the server (200) receives a request from the design proposal derivation device (100) for existence information related to the actual existence of raw material data or supply information related to the availability of supply, the server (200) can search for raw material data in the database managed by the server (200). Accordingly, the server (200) can transmit the existence information or supply information of raw material data to the design proposal derivation device (100).

[0062] For example, when the server (200) receives a request for standard enthalpy of formation information of reactants and products from the design proposal derivation device (100), the server (200) can search for reactants and products in the standard enthalpy of formation database of compounds managed by the server (200). Accordingly, the server (200) can transmit the standard enthalpy of formation information of reactants and products to the design proposal derivation device (100).

[0063] As another example, the server (200) can receive reaction formula data from the design proposal derivation device (100), search for reactants and products in the standard enthalpy of formation database of compounds, and calculate the reaction energy of the reaction formula data. Accordingly, the server (200) can transmit the reaction energy of the reaction formula data to the design proposal derivation device (100).

[0064] Hereinafter, a design proposal derivation device according to one embodiment of the present document will be described with reference to FIG. 2 and FIG. 3a to 3d.

[0065] FIG. 2 is a block diagram showing the configuration of a design proposal derivation device according to one embodiment of the present document. FIG. 3a is a diagram explaining the derivation of raw material data according to one embodiment of the present document. FIG. 3b and FIG. 3c are diagrams explaining a data frame according to one embodiment of the present document. FIG. 3d is a diagram explaining a design proposal according to one embodiment of the present document.

[0066] Referring to FIG. 2, a design proposal derivation device (100) according to one embodiment of the present document may include an interface (110) and a processor (120). Additionally, the design proposal derivation device (100) may further include a memory (130).

[0067] According to one embodiment, the interface (110) may receive data from a user or output the result of an operation of the processor (120). For example, the interface (110) may receive and obtain target material data from a user. Additionally, the interface (110) may output a design generated by the processor (120).

[0068] According to one embodiment, the interface (110) can transmit and receive data by communicating with an external device, including a server (200), via wired or wireless communication. For example, the interface (110) can request toxicity information or legal compliance information of target substance data from the server (200) and can receive toxicity information or legal compliance information of target substance data from the server (200).

[0069] For example, the interface (110) may request from the server (200) existence information related to whether raw material data actually exists or supply information related to availability, and may receive the existence information or supply information of raw material data from the server (200).

[0070] For example, the interface (110) may request standard enthalpy information of the reactants and products from the server (200) and receive standard enthalpy information of the reactants and products from the server (200). As another example, the interface (110) may request the calculation of reaction energy by transmitting reaction equation data to the server (200) and receive the result of the reaction energy calculation of the reaction equation data from the server (200).

[0071] For example, the interface (110) may request the supply cost and available supply amount of each raw material data from the server (200) and receive the supply cost and available supply amount of each raw material data from the server (200). As another example, the interface (110) may request the calculation of the supply cost and available supply amount by transmitting reaction formula data to the server (200) and receive the result of the calculation of the supply cost and available supply amount of the reaction formula data from the server (200).

[0072] According to one embodiment, the processor (120) may execute at least one instruction for performing a method for generating a design proposal according to one embodiment of the present document. For example, the processor (120) may include at least one of a CPU (Central Processing Unit), an MCU (Microcontroller Unit), a DSP (Digital Signal Processor), a FPGA (Field-Programmable Gate Array), and an ASIC (Application-Specific Integrated Circuit). Additionally, the processor (120) may be composed of one or more units. For example, the processor (120) may be composed of a single-core processor, a multi-core processor, or a multi-processor system.

[0073] According to one embodiment, the processor (120) derives filtered data selected based on whether the target material data corresponds to a negative factor, and can derive first data based on the filtered data.

[0074] For example, the processor (120) can select cases where the target material data does not correspond to a negative factor and derive it as filtered data. Additionally, the processor (120) can exclude the target material data from the design proposal derivation target if the target material data corresponds to a negative factor. Accordingly, the processor (120) can output a notification that the target material data corresponds to a negative factor through the interface (110).

[0075] According to one embodiment, the processor (120) can pre-set negative factors based on toxicity and legal compliance. Accordingly, the processor (120) can derive filtered data selected based on whether target substance data corresponds to negative factors.

[0076] According to one embodiment, negative factors may be pre-set based on toxicity criteria. The toxicity criteria may include at least one of the chemical toxicity and biocompatibility of the material. In particular, the toxicity criteria for the target material data as a battery material may include at least one of electrochemical stability and toxicity of the manufacturing and disposal processes.

[0077] For example, when a processor (120) requests toxicity information of target substance data from a server (200) through an interface (110), the server (200) can search for toxicity information of target substance data in a database stored in the server (200) and transmit it to the interface (110). Accordingly, the processor (120) can analyze the toxicity information of target substance data received from the server (200) to identify whether it satisfies toxicity standards. If the toxicity information of target substance data does not satisfy toxicity standards, the processor (120) can set it as a negative factor.

[0078] According to one embodiment, negative factors may be pre-set based on legal conformity criteria. Legal conformity criteria may include the patent infringement potential and regulatory status of the target substance data. Additionally, legal conformity criteria may be set based on the regulatory status of the country where the target substance data is to be synthesized and distributed.

[0079] For example, when a processor (120) requests legal conformity information of target substance data from a server (200) through an interface (110), the server (200) can search for legal conformity information of target substance data in a database stored in the server (200) and transmit it to the interface (110). The processor (120) can analyze the legal conformity information of target substance data received from the server (200) to identify whether it satisfies the legal conformity criteria. If the legal conformity information of target substance data does not satisfy the legal conformity criteria, the processor (120) can set it as a negative factor.

[0080] According to one embodiment, the processor (120) can determine whether the target substance data corresponds to a negative factor based on a negative factor pre-set by the server (200) based on toxicity and legal compliance.

[0081] For example, when the processor (120) transmits target substance data to the server (200) through the interface (110), the server (200) can compare the target substance data with the data in the negative factor database stored in the server (200). If the server (200) finds data in the negative factor database that matches the target substance data, it can transmit a comparison result to the interface (110) indicating that the target substance data corresponds to a negative factor. Additionally, if the server (200) finds no data in the negative factor database that matches the target substance data, it can transmit a comparison result to the interface (110) indicating that the target substance data does not correspond to a negative factor. Accordingly, the processor (120) can derive filtered data that selects target substance data that does not correspond to a negative factor, and derive first data based on the filtered data.

[0082] According to one embodiment, the processor (120) can derive first data including raw material data from target material data. Raw material data refers to a material required to synthesize the target material data. For example, raw material data may be a combination of compounds each containing elements included in the target material data.

[0083] Referring to FIG. 3a, the case where LiAlSO4 is the target substance is illustrated. An interface (110) of a design proposal derivation device (100) according to one embodiment of the present document can obtain target substance data related to the name or chemical formula of the target substance (e.g., LiAlSO4). In step (a), a processor (120) can identify cations Li, Al, and Si from the target substance data.

[0084] In step (b), the processor (120) can derive all anions capable of binding to the cation identified from the target material data. Additionally, the processor (230) can derive anions containing at least one element among H (hydrogen), N (nitrogen), O (oxygen), C (carbon), F (fluorine), P (phosphorus), and S (sulfur). However, since this is merely an example, the processor (120) according to one embodiment of the present document can derive anions containing elements other than those disclosed.

[0085] For example, the processor (120) has O as an anion capable of binding to the cation Li. x , (OH) x , (NO3)3, N x It can be derived. Here, x can be an integer greater than or equal to 0. Also, although not shown in FIG. 3a, the processor (120) can likewise derive all anions that can be bonded to each other cations Al and Si.

[0086] In step (c), the processor (120) can derive raw material data by combining a cation identified from the target material data and an anion capable of binding to the cation. For example, the processor (120) can derive raw material data including LiOH, Li2CO3, LiSO4, and LiC by combining an anion capable of binding to the cation Li. Additionally, the processor (120) can derive raw material data including Al(NO3)3, Al2O3, Al4C3, and AlN by combining an anion capable of binding to the cation Al. Additionally, the processor (120) can derive raw material data including Si3N4, SiO2, SiO, and SiC by combining an anion capable of binding to the cation Si.

[0087] According to one embodiment, the processor (120) can derive first data by filtering raw material data based on actual existence and availability of supply. Specifically, the processor (120) can derive first data by filtering based on whether the compound included in the raw material data is a theoretically existing compound, whether there is a supplier, and the cost of supply.

[0088] For example, when the processor (120) requests existence information related to the actual existence of raw material data from the server (200) through the interface (110), the server (200) can search for compounds included in the raw material data in the database stored in the server (200) and transmit existence information related to the actual existence to the interface (110). The processor (120) can derive filtered first data based on the existence information of the raw material data received from the server (200).

[0089] For example, when a processor (120) requests supply information related to the availability of raw material data from a server (200) through an interface (110), the server (200) can search for supply information including whether compounds included in the raw material data are sold, costs, etc., in a database stored in the server (200) and transmit it to the interface (110). The processor (120) can derive filtered first data based on the supply information of the raw material data received from the server (200).

[0090] Referring to FIG. 2, a processor (120) according to one embodiment can derive reaction equation data by combining raw material data. For example, the processor (120) can derive LiNO3 and NH4SCN as raw material data for the target material Li2SO4, and derive reaction equation data of 2LiNO3 + NH4SCN → Li2SO4 + CO2 + 2N2 + H2 by combining the raw material data.

[0091] According to one embodiment, the processor (120) can derive second data from reaction formula data by further considering specific reaction conditions. For example, the processor (120) can derive second data from reaction formula data that can react at pre-set reaction conditions of 600°C and air.

[0092] According to one embodiment, the processor (120) can calculate the reaction energy of the reaction equation data. Reaction energy may refer to the energy required for a chemical reaction to occur or the energy released during the reaction process. The processor (120) can calculate the reaction energy based on the value obtained by subtracting the standard enthalpy of formation of the reactants from the standard enthalpy of formation of the products included in the reaction equation data. Here, the reactants may include raw material data, and the products may include target material data and by-products.

[0093] For example, when the processor (120) requests standard enthalpy of formation information for reactants and products from the server (200) via the interface (110), the server (200) can search for the reactants and products in the database stored in the server (200) and transmit the respective standard enthalpy of formation information. The processor (120) can obtain the standard enthalpy of formation information for each reactant and product from the server (200) and calculate the reaction energy of the reaction equation data based on the value obtained by subtracting the standard enthalpy of formation of the reactant from the standard enthalpy of formation of the product.

[0094] For example, if the reaction formula data for the target substance Li2SO4 is 2LiNO3 + NH4SCN → Li2SO4 + CO2 + 2N2 + 2H2, the processor (120) can request standard enthalpy information for the reactants LiNO3 and NH4SCN and the products Li2SO4, CO2, N2, and H2 from the server (200) via the interface (110). The server (200) can search for the reactants and products in a database and transmit the respective standard enthalpy information for formation to the processor (120) via the interface (110).

[0095]

[0096] [Table 1] shows an example of standard enthalpy of formation information for reactants and products. Referring to Table 1, the processor (120) can calculate the reaction energy of Li2SO4 -0.458 eV / atom based on the value obtained by subtracting the standard enthalpy of formation of the reactants from the standard enthalpy of formation of the products.

[0097] According to one embodiment, the processor (120) can identify side reaction characteristic information of the reaction formula data. The side reaction characteristic information is information encompassing characteristics related to the side reaction of the byproduct of the reaction formula data, and may include at least one of the generation rate of the byproduct, the physical state of the byproduct (e.g., solid, liquid, gas, etc.), the generation energy of the byproduct, and the reaction conditions of the byproduct.

[0098] According to one embodiment, the processor (120) can derive second data based on reaction equation data, reaction energy, and side reaction characteristic information. The processor (120) can derive second data from the reaction equation data in which the reaction energy is less than or equal to a reference energy and the side reaction characteristic information is less than or equal to a reference reaction value.

[0099] According to one embodiment, the processor (120) can select reaction equation data in which the reaction energy is less than or equal to the reference energy among the reaction equation data and derive it as second data. The processor (120) can set the reference energy to a specific numerical value of 0 or less. For example, if the processor (120) sets the reference energy to -0.1 eV / atom, the processor (120) can select reaction equation data in which the reaction energy is less than or equal to the reference energy -0.1 eV / atom and derive it as second data. However, since this is merely an example, the processor (120) can set a value different from -0.1 eV / atom as the reference energy within the reaction energy range of a spontaneous reaction.

[0100] According to one embodiment, the processor (120) can select reaction equation data in which the byproduct characteristic information is less than or equal to a reference reaction value and derive it as second data. The reference reaction value may be set in correspondence with byproduct characteristic information including at least one of a byproduct generation ratio, a physical state of a byproduct (e.g., solid, liquid, gas, etc.), energy of byproduct generation, and reaction conditions of a byproduct, and may include at least one of a reference ratio, a reference state, a reference energy, and a reference reaction condition.

[0101] For example, the processor (120) can select reaction equation data in which the generation ratio of byproducts is below a reference ratio and derive it as second data. Additionally, if the reference state is set to gas, the processor (120) can select reaction equation data in which the physical state of the byproduct is gas and derive it as second data. Additionally, the processor (120) can select reaction equation data in which the generation energy of the byproduct is above a reference energy and set it as second data. Additionally, the processor (120) can select reaction equation data in which the reaction conditions of the byproduct differ from the reference reaction conditions and derive it as second data.

[0102] According to one embodiment, the processor (120) may derive third data by selecting second data according to supply and demand criteria. The supply and demand criteria may include supply costs and available supply quantities. The processor (120) may derive third data from the second data in which the supply cost is less than or equal to a reference cost and the available supply quantity is greater than or equal to a reference supply quantity.

[0103] For example, the processor (120) may request the supply cost and available supply amount of each raw material data from the server (200) via the interface (110) based on the reaction equation data included in the second data. The server (200) may search for and transmit the supply cost and available supply amount of the raw material data from the database. Based on the supply cost and available supply amount of the raw material data obtained from the server (200), the processor (120) may select the second data in which the supply cost of the raw material data is less than or equal to the standard cost and the available supply amount is greater than or equal to the standard supply amount, and derive it as the third data.

[0104] According to one embodiment, the processor (120) may organize the third data into a data frame according to priority and generate the data frame as a design. The processor (120) may pre-set the priority based on the absolute value of the reaction energy or the supply cost.

[0105] For example, the processor (120) can organize third data into data frames in order of increasing absolute value of reaction energy. Additionally, the processor (120) can select third data with the largest absolute value of reaction energy for each target material data and organize them into data frames. That is, the processor (120) can organize a single or multiple third data into data frames based on the absolute value of reaction energy.

[0106] As another example, the processor (120) can organize third data into data frames in order of lowest supply cost. Additionally, the processor (120) can select the third data with the lowest supply cost for each target material data and organize it into data frames. That is, the processor (120) can organize one or more third data into data frames based on supply costs.

[0107] Referring to FIG. 3b, the processor (120) can construct a data frame containing target material data, reaction energy, and reaction equation data. For example, if the target material is Li2SO4, the processor (120) can construct a data frame containing reaction equation data 2LiNO3+ NH4SCN → Li2SO4+ CO2+ 2N2+ 2H2 derived from the target material data and reaction energy -0.458 eV / atom.

[0108] For example, if the target substance is Li2B4O7, the processor (120) can construct a data frame containing reaction equation data 2B2O3 + 2LiOH → Li2B4O7 + H2O and reaction energy -0.189 eV / atom derived from the target substance data.

[0109] For example, if the target material is Li2MoO4, the processor (120) can construct a data frame containing reaction equation data MoO3 + 2LiOH → Li2MoO4 + H2O and reaction energy -0.296 eV / atom derived from the target material data.

[0110] For example, if the target material is Li2WO4, the processor (120) can construct a data frame containing reaction equation data WO3 + 2LiOH → Li2WO4 + H2O and reaction energy -0.226 eV / atom derived from the target material data.

[0111] For example, if the target substance is Li2B4H4O9, the processor (120) can construct a data frame containing reaction equation data derived from the target substance data 12LiNO3+ 20B5H4NO8+ 38LiOH → 25Li2B4H4O9+ 16N2+ 9H2O and reaction energy -0.149 eV / atom.

[0112] For example, if the target material is Li3Al(BO3)2, the processor (120) can construct a data frame containing reaction equation data 6LiNO3+ B4C + 2AlN → 2Li3Al(BO3)2+ 3O2+ CO2+ 4N2 derived from the target material data and reaction energy -0.830 eV / atom.

[0113] For example, if the target substance is LiB3PO8, the processor (120) can construct a data frame containing reaction equation data derived from the target substance data 8LiNO3+ 4H3PO4+ 3B4C → 4Li2B3PO8+ 3CO2+ 4N2+ 4H2+ 2H2O and reaction energy -0.852 eV / atom.

[0114] For example, if the target material is Li4ZrF8, the processor (120) can construct a data frame containing reaction equation data 4LiNO3+ 8NH4F + ZrO2→ Li4ZrF8+ 6N2+ 2H2+ 14H2O derived from the target material data and reaction energy -0.479 eV / atom.

[0115] For example, if the target material is LiAlSiO4, the processor (120) can construct a data frame containing reaction equation data derived from the target material data 4SiC + 4Al(NO3)3 + 4LiOH → 4LiAlSiO4 + 7O2 + 4CO2 + 6N2 + 2H2O and reaction energy -0.631 eV / atom.

[0116] Referring to FIG. 3c, the processor (120) can derive a synthesis path including reactants (a), a synthesis ratio of reactants (b), and products (c) based on reaction formula data, and can construct a data frame including a synthesis path and reaction energy for each target material data.

[0117] For example, if the target substance is Li2SO4, the processor (120) can derive a synthesis route including reactants (LiNO3 and NH4SCN), a synthesis ratio of reactants (2.0:1.0), and products (Li2SO4, CO2, N2 and H2) based on reaction equation data 2LiNO3+ NH4SCN → Li2SO4+ CO2+ 2N2+ 2H2 derived from the target substance data.

[0118] For example, if the target material is Li2B4O7, the processor (120) can derive a synthesis route including reactants (B2O3 and LiOH), a synthesis ratio of reactants (2.0 : 2.0), and products (Li2B4O7 and H2O) based on reaction equation data 2B2O3 + 2LiOH → Li2B4O7 + H2O derived from the target material data.

[0119] For example, if the target material is Li2MoO4, the processor (120) can derive a synthesis route including reactants (MoO3 and LiOH), a synthesis ratio of reactants (1.0 : 2.0), and products (Li2MoO4 and H2O) based on reaction equation data MoO3 + 2LiOH → Li2MoO4 + H2O derived from the target material data.

[0120] For example, if the target material is Li2WO4, the processor (120) can derive a synthesis route including reactants (WO3 and LiOH), a synthesis ratio of reactants (1.0 : 2.0), and products (Li2WO4 and H2O) based on reaction equation data WO3 + 2LiOH → Li2WO4 + H2O derived from the target material data.

[0121] For example, if the target material is Li2B4H4O9, the processor (120) can derive a synthesis route including reactants (LiNO3, B5H4NO8, and LiOH), a synthesis ratio of reactants (0.48 : 0.8 : 1.52), and products (Li2B4H4O9, N2, and H2O) based on reaction formula data 12LiNO3 + 20B5H4NO8 + 38LiOH → 25Li2B4H4O9 + 16N2 + 9H2O derived from the target material data.

[0122] For example, if the target material is Li3Al(BO3)2, the processor (120) can derive a synthesis route including reactants (LiNO3, B4C and AlN), a synthesis ratio of reactants (3.0 : 0.5 : 1.0), and products (Li3Al(BO3)2, O2, CO2, and N2) based on reaction equation data 6LiNO3 + B4C + 2AlN → 2Li3Al(BO3)2 + 3O2 + CO2 + 4N2 derived from the target material data.

[0123] For example, if the target material is LiB3PO8, the processor (120) can derive a synthesis route including reactants (LiNO3, H3PO4 and B4C), a synthesis ratio of reactants (2.0 : 1.0 : 0.75) and products (Li2B3PO8, CO2, N2, H2 and H2O) based on reaction formula data 8LiNO3 + 4H3PO4 + 3B4C → 4Li2B3PO8 + 3CO2 + 4N2 + 4H2 + 2H2O derived from the target material data.

[0124] For example, if the target material is Li4ZrF8, the processor (120) can derive a synthesis route including reactants (LiNO3, NH4F and ZrO2), a synthesis ratio of reactants (4.0 : 8.0 : 1.0), and products (Li4ZrF8, N2, H2 and H2O) based on reaction formula data 4LiNO3 + 8NH4F + ZrO2 → Li4ZrF8 + 6N2 + 2H2 + 14H2O derived from the target material data.

[0125] For example, if the target material is LiAlSiO4, the processor (120) can derive a synthesis route including reactants (SiC, Al(NO3)3 and LiOH), a synthesis ratio of reactants (1.0 : 1.0 : 1.0) and products (LiAlSiO4, O2, CO2, N2 and H2O) based on reaction equation data 4SiC + 4Al(NO3)3 + 4LiOH → 4LiAlSiO4 + 7O2 + 4CO2 + 6N2 + 2H2O derived from the target material data.

[0126] However, since this is merely an example, the processor (120) may configure the data frame in a different way than as illustrated in FIG. 3b and FIG. 3c. For example, the processor (120) may configure the data frame by arranging the top 10 reaction equation data with the largest absolute values ​​of reaction energy for each target substance data in order of reaction energy size.

[0127] According to one embodiment, the processor (120) can generate a design proposal based on third data. For example, the processor (120) can generate a design proposal by configuring a table or graph based on third data.

[0128] Referring to FIG. 3d, a processor (120) according to one embodiment can generate a design proposal by configuring the reaction energy for each target material data into a graph. For example, the processor (120) can select the reaction equation data with the largest absolute value of reaction energy for each target material data and configure the reaction energy of the selected reaction equation data for each target material data into a graph. The processor (120) can configure the graph with the horizontal axis representing the target material data and the vertical axis representing the reaction energy. Additionally, the processor (120) can configure the graph by plotting the reaction energy and reference energy (Ref) together for each target material data. Through this, the user can identify the target material and its reaction equation in which the reaction energy is less than or equal to the reference energy (Ref).

[0129] According to one embodiment, the processor (120) can generate a design proposal by configuring a graph of the supply costs for each target material data. For example, the processor (120) can select the reaction formula data with the lowest supply cost for each target material data and configure a graph of the supply costs of the selected reaction formula data for each target material data. The processor (120) can configure a graph with the horizontal axis representing the target material data and the vertical axis representing the supply costs. Additionally, the processor (120) can configure a graph that shows both the supply costs and the reference costs for each target material data. Through this, the user can identify target materials and their reaction formulas for which the supply cost is less than or equal to the reference cost.

[0130] According to one embodiment, the processor (120) can generate a design proposal by configuring reaction energy and supply costs for each target material data together in a graph. For example, the processor (120) can configure reaction energy and supply costs for each target material data together in a graph with the left vertical axis representing reaction energy and the right vertical axis representing supply costs. Additionally, the processor (120) can configure a graph that shows reference energy (Ref) and reference costs together.

[0131] Accordingly, users can select desired target material data and synthesis routes based on reaction energy and supply costs for each target material data. In other words, this allows users to select the optimal target material and its synthesis route in terms of reaction energy and supply costs without having to try all possible synthesis routes for the target material data.

[0132] Referring again to FIG. 2, a memory (130) according to one embodiment may store at least one instruction executed by a processor (120). Specifically, the memory (130) may store at least one instruction for performing a method for deriving a design plan according to an embodiment of the present document. Additionally, the memory (130) may temporarily and / or permanently store data acquired by the interface (110), data input to the processor (120), and / or the operation results of the processor (120).

[0133] According to various embodiments, the memory (130) may include volatile memory and / or non-volatile memory. For example, the volatile memory may include at least one of SRAM (Static RAM), DRAM (Dynamic RAM), SDRAM (Synchronous DRAM), and DDR SDRAM (Double Date Rate SDRAM). Additionally, the non-volatile memory may include at least one of ROM, PROM (Programmable ROM), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, magnetic disk, and optical disk.

[0134] According to one embodiment, the memory (130) may temporarily or permanently store target material data acquired by the interface (110). Additionally, the memory (130) may temporarily or permanently store at least one of the first data, second data, and third data derived by the processor (120). Additionally, the memory (130) may temporarily or permanently store data frames and design plans configured by the processor (120).

[0135] FIG. 4 is a flowchart illustrating a method for deriving a design proposal according to one embodiment of the present document.

[0136] In the following, it is assumed that the design proposal derivation device (100) of FIG. 2 performs each step of FIG. 4. Each step of FIG. 4 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each step may be changed, and multiple steps may be performed in parallel.

[0137] Referring to FIG. 4, in step S410, the design proposal derivation device (100) can perform the step of acquiring target material data. For example, the design proposal derivation device (100) can receive target material data directly from a user. As another example, the design proposal derivation device (100) can communicate with a separate user terminal to acquire target material data received by the user terminal from the user.

[0138] For example, the design proposal derivation device (100) can derive filtering data selected based on whether the target material data corresponds to a negative factor, and derive first data including raw material data based on the filtering data.

[0139] For example, the design proposal derivation device (100) can pre-set negative factors based on toxicity and legal compliance. The design proposal derivation device (100) can identify whether the target substance data corresponds to a negative factor based on the toxicity and legal compliance of the target substance data. Accordingly, the design proposal derivation device (100) can derive filtered data by selecting if the target substance data does not correspond to a negative factor.

[0140] In step S420, the design proposal derivation device (100) can derive first data including raw material data from target material data. For example, the design proposal derivation device (100) can derive raw material data by identifying a cation included in the target material data and combining an anion capable of binding to the cation with the cation.

[0141] For example, the design proposal derivation device (100) can derive first data by filtering raw material data based on whether it actually exists and whether it is available for supply. The design proposal derivation device (100) can derive first data by filtering raw material data that actually exists and is available for supply.

[0142] In step S430, the design proposal derivation device (100) can derive second data including reaction formula data from first data. For example, the design proposal derivation device (100) can derive reaction formula data by combining raw material data included in the first data.

[0143] For example, the design proposal derivation device (100) can derive second data based on the reaction energy and side reaction characteristic information of the reaction formula data. The design proposal derivation device (100) can derive second data from the reaction formula data in which the reaction energy is less than or equal to the reference energy and the side reaction characteristic information is less than or equal to the reference reaction value.

[0144] In step S440, the design proposal derivation device (100) can derive third data selected from second data based on supply and demand criteria. The supply and demand criteria may include supply costs and available supply quantities.

[0145] For example, the design proposal derivation device (100) can derive data from the second data in which the supply cost is less than or equal to the standard cost and the supply quantity is greater than or equal to the standard supply quantity as the third data.

[0146] In step S450, the design proposal derivation device (100) can generate a design proposal based on third data. For example, the design proposal derivation device (100) can organize the third data into data frames according to priority and generate the data frames as a design proposal.

[0147] For example, the design proposal derivation device (100) may organize the third data into a data frame in order of the largest absolute value of the reaction energy and generate the data frame as a design proposal. Additionally, the design proposal derivation device (100) may organize the third data into a data frame in order of the smallest supply cost of the raw material data and generate the data frame as a design proposal.

[0148] According to an embodiment, the method for deriving a design proposal can be implemented in the form of a computer program stored on a computer-readable storage medium. That is, the computer program may include instructions for implementing the method for deriving a design proposal, and the instructions of the program may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0149] According to an embodiment, at least one program and / or instruction for executing a method for deriving a design proposal on a computer may be recorded or stored on a computer-readable medium. According to an embodiment, the computer-readable medium may include a non-transitory medium. The computer-readable medium may be implemented in the form of a recording medium and / or a storage medium. According to an embodiment, the method for deriving a design proposal may be provided in the form of a computer program product. The computer program product may be distributed in the form of a storage medium and / or a recording medium, or distributed in the form of a mobile application, a computer program, etc., through the web, markets, stores, etc.

[0150] According to an embodiment, a computer-readable medium may include magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a CD-ROM and a DVD, magneto-optical media such as a floptical disk, and hardware devices specifically configured to store and execute computer program instructions such as ROM, RAM, flash memory, and server devices. Computer program instructions may include machine code generated by a compiler and high-level language code that can be executed by a computer using an interpreter, etc.

[0151] FIG. 5 is a block diagram showing the hardware configuration of a computing system for performing a method for deriving a design proposal according to one embodiment of the present document.

[0152] Referring to FIG. 5, a computing system (500) according to one embodiment disclosed in this document may include an MCU (510), memory (520), an input / output I / F (530), and a communication I / F (540).

[0153] The MCU (510) may be a processor (120) that executes various programs stored in memory (520) (e.g., data collection program, graph generation program, data analysis program, data decomposition algorithm, normalization program and design proposal generation program, etc.), processes various information including target material data, raw material data, standard enthalpy of formation, side reaction characteristic information, etc. through these programs, and performs the functions of the design proposal generation device (100) shown in FIGS. 1 to 4.

[0154] The memory (520) can store various programs such as a data collection program, a graph generation program, a data analysis program, a data decomposition algorithm, a normalization program, and a design proposal generation program.

[0155] These memories (520) may be provided in multiple quantities as needed. The memories (520) may be volatile memories or non-volatile memories. As volatile memories, the memory (520) may use RAM, DRAM, SRAM, etc. As non-volatile memories, the memory (520) may use ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the listed memories (520) are merely examples and are not limited to these examples.

[0156] The input / output I / F (530) can provide an interface that enables data transmission and reception between an input device (not shown), such as a keyboard, mouse, or touch panel, and an output device (not shown), such as a display, and the MCU (510).

[0157] The communication I / F (540) is configured to transmit and receive various data to and from a server and may be various devices capable of supporting wired or wireless communication.

[0158] In this way, a computer program according to one embodiment disclosed in this document may be implemented as a module that performs each of the functions illustrated in FIG. 2, for example, by being written to memory (520) and processed by an MCU (510).

[0159] In the foregoing, although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purposes of the embodiments disclosed in this document, all components may be selectively combined in one or more ways to operate.

[0160] Furthermore, terms such as "include," "compose," or "have" as described above, unless specifically stated otherwise, mean that the relevant component may be inherent; thus, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their meaning in the context of the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

[0161] The foregoing disclosure outlines the features of several embodiments to enable those skilled in the art to better understand aspects of the present disclosure. Those skilled in the art will understand that the present disclosure can be readily used as a basis for designing or modifying other structures to perform the same purpose or achieve the same advantages as the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent configurations do not depart from the scope of the present disclosure and that various changes, substitutions, and modifications may be made in the present disclosure without departing from the scope of the present disclosure.

[0162] Although the present document has been described above with reference to limited embodiments and drawings, the present document is not limited thereto, and various implementations are possible within the scope of the technical concept of the present document and the equivalent scope of the claims set forth below by those skilled in the art to which the present document belongs.

Claims

1. Memory configured to store at least one instruction; and A design proposal derivation device comprising a processor configured to execute at least one of the above instructions, The above processor is, Acquire target substance data, and Deriving first data including raw material data from the above target material data, and Deriving second data including reaction equation data from the first data above, and The above second data is selected according to supply and demand criteria to derive the third data, and A design proposal derivation device configured to generate a design proposal based on the above third data.

2. In Paragraph 1, The above processor is a design proposal derivation device that derives filtered data selected based on whether the target material data corresponds to a negative factor, and derives the first data based on the filtered data.

3. In Paragraph 2, The above-mentioned negative factor is a design proposal derivation device that is pre-set based on toxicity and legal compliance.

4. In Paragraph 1, The above processor is a design derivation device that identifies a cation included in the target material data and combines an anion capable of binding to the cation with the cation to derive the raw material data.

5. In Paragraph 1, The above processor is a design proposal derivation device that derives the first data by filtering the raw material data based on its actual existence and availability.

6. In Paragraph 1, The above processor is a design proposal derivation device that derives the reaction equation data by combining the above raw material data and derives the above second data based on the reaction equation data, reaction energy, and side reaction characteristic information.

7. In Paragraph 6, The above processor is a design proposal derivation device that derives the second data among the reaction equation data in which the reaction energy is less than or equal to the reference energy and the side reaction characteristic information is less than or equal to the reference reaction value.

8. In Paragraph 1, The above supply and demand criteria include supply costs and available quantities, and The above processor is a design proposal derivation device that derives data from the above second data in which the supply cost is less than or equal to the reference cost and the supplyable amount is greater than or equal to the reference supply amount as the above third data.

9. In Paragraph 1, The above processor is a design proposal derivation device that organizes the above third data into a data frame according to priority and generates the above data frame into the above design proposal.

10. Step of acquiring target substance data; A step of deriving first data including raw material data from the above target material data; A step of deriving second data including reaction equation data from the first data above; A step of deriving third data by selecting the above second data according to supply and demand criteria; and A method for deriving a design proposal, comprising the step of generating a design proposal based on the third data above.

11. In Paragraph 10, A step of deriving filtered data selected based on whether the above target substance data corresponds to a negative factor; and A method for deriving a design proposal, further comprising the step of deriving the first data based on the filtering data above.

12. In Paragraph 11, The above-mentioned negative factors are a method for deriving a design proposal that is pre-established based on toxicity and legal compliance.

13. In Paragraph 10, The step of deriving the above first data is, A step of identifying cations included in the above target substance data; and A method for deriving a design proposal comprising the step of deriving raw material data by combining an anion capable of binding to the above cation with the above cation.

14. In Paragraph 10, The step of deriving the above first data is, A method for deriving a design proposal comprising the step of filtering the above raw material data based on actual existence and supply availability.

15. In Paragraph 10, The step of deriving the above second data is, A step of deriving the reaction formula data by combining the above raw material data; and A method for deriving a design proposal comprising the step of deriving the second data based on the reaction equation data, reaction energy, and side reaction characteristic information.

16. In Paragraph 15, The step of deriving the above second data is, A method for deriving a design proposal characterized by deriving the second data among the above reaction equation data in which the reaction energy is less than or equal to the reference energy and the side reaction characteristic information is less than or equal to the reference reaction value.

17. In Paragraph 10, The above supply and demand criteria include supply costs and available quantities, and The step of deriving the above third data is, A method for deriving a design proposal characterized by deriving data from the second data above, wherein the supply cost is less than or equal to the standard cost and the available supply quantity is greater than or equal to the standard supply quantity, as the third data.

18. In Paragraph 10, The step of generating the above design proposal is, A method for deriving a design proposal characterized by configuring the above-mentioned third data into a data frame according to priority and generating the above-mentioned data frame into the above-mentioned design proposal.

19. A computer-readable medium storing a program for executing on a computer the method for deriving a design proposal according to any one of claims 10 through 18.

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