Compatibility prediction system for dissimilar resin materials, method for predicting compatibility between dissimilar resin materials

Abstract

This invention provides a compatibility prediction system and method for dissimilar resin materials, which predicts the compatibility between constituent materials, such as compatibility and interfacial adhesion, in blended materials prepared by mixing multiple resin materials. [Solution] The method includes: creating a three-dimensional molecular model of resin materials by inputting numerical information of the chemical structures of multiple resin materials; creating a molecular entanglement model of dissimilar resin materials by combining two types of three-dimensional molecular models of resin materials by inputting the three-dimensional molecular model, temperature, and pressure; performing a simulation based on the molecular entanglement model to calculate the interdiffusion coefficient between dissimilar resin materials; performing a simulation based on the molecular entanglement model to calculate the fracture energy; and a trajectory storage unit that saves the time evolution of the coordinates of the molecular entanglement model during the fracture energy calculation process, outputting the interdiffusion coefficient and fracture energy, and outputting the time evolution of the coordinates as a video.

Description

Technical Field

[0001] The present invention relates to the configuration and method of a compatibility prediction system for predicting the compatibility between different resin materials, and particularly relates to a technology effective for applying to the material design process of polymer blends.

Background Art

[0002] Plastics (resin materials) are used in a wide range of applications. In recent years, in order to reduce the impact of plastic waste on the environment, biodegradable plastics (a general term for plastics made from biomass and biodegradable plastics) and recycled plastics made from recycled plastic waste have been developed.

[0003] Biodegradable plastics and recycled plastics often have inferior mechanical properties compared to general plastics. As one method to compensate for this, polymer blend technology has been studied. A polymer blend is a material that exhibits properties not possessed by a single polymer by mixing multiple polymers. In order to produce a polymer blend with excellent mechanical properties, it is important to optimize various design parameters such as the composition of the material, the type of additive, and the method of chemical modification of the material, and to enhance the compatibility and interfacial adhesion between the polymers constituting the blend.

[0004] As background art in this technical field, for example, there is a technology such as Patent Document 1. Patent Document 1 discloses "a method and apparatus for analyzing the adhesion mechanism between a fiber material and an adhesive liquid and predicting the peeling characteristics of the interface between the fiber material and the adhesive liquid by computer simulation".

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] Incidentally, when preparing the polymer blends mentioned above, if design parameters such as the composition of the materials and the types of additives are optimized based on experiments, a considerable lead time is required to carry out a series of tasks including procuring raw materials, prototyping the blend, and evaluating its properties.

[0007] Furthermore, the properties of polymer blends are affected by various bulk properties of the constituent materials, making it difficult to evaluate compatibility and interfacial adhesion in isolation. This means that incompatible polymer combinations cannot be eliminated in advance, leading to an increase in the number of prototypes required.

[0008] Patent Document 1, mentioned above, describes a method for evaluating the compatibility between an adhesive and a fiber material by investigating the interfacial adhesion between the fiber material and the adhesive using computer simulation.

[0009] However, Patent Document 1 does not provide a method for optimizing the material composition, type of additives, and chemical modification method, taking into account the compatibility and interfacial adhesion between multiple polymers, and therefore it is not possible to obtain a polymer blend with the desired properties.

[0010] Therefore, the object of the present invention is to provide a compatibility prediction system and a compatibility prediction method between dissimilar resin materials that can predict the compatibility between constituent materials, such as compatibility and interfacial adhesion, in a blended material made by mixing multiple resin materials. [Means for solving the problem]

[0011] To solve the above problems, the present invention is characterized by comprising: a three-dimensional molecular model creation unit that creates a three-dimensional molecular model of each resin material using numerical information of the chemical structures of multiple resin materials as input information; a molecular entanglement model creation unit that creates a molecular entanglement model of dissimilar resin materials by combining two types of three-dimensional molecular models of each resin material using the three-dimensional molecular model, temperature, and pressure as input information; a mutual diffusion coefficient calculation unit that performs molecular simulation based on the molecular entanglement model and calculates the mutual diffusion coefficient between dissimilar resin materials; a fracture energy calculation unit that performs molecular simulation based on the molecular entanglement model and calculates the fracture energy; a trajectory storage unit that saves the time evolution of the coordinates of the molecular entanglement model during the fracture energy calculation process; and a simulation result output unit that outputs the mutual diffusion coefficient and the fracture energy as numerical information and outputs the time evolution of the coordinates as a video.

[0012] Furthermore, the present invention relates to a method for predicting compatibility between dissimilar resin materials, which is performed in a compatibility prediction system for dissimilar resin materials, and is characterized by comprising: (a) creating a three-dimensional molecular model of each resin material using numerical information of the chemical structures of a plurality of resin materials as input information; (b) creating a molecular entanglement model of dissimilar resin materials by combining two types of three-dimensional molecular models of each resin material using the three-dimensional molecular models created in step (a) as input information, temperature, and pressure; (c) performing a molecular simulation based on the molecular entanglement model created in step (b) to calculate the interdiffusion coefficient between dissimilar resin materials; (d) performing a molecular simulation based on the molecular entanglement model created in step (b) to calculate the fracture energy; (e) saving the time evolution of the coordinates of the molecular entanglement model during the calculation process of the fracture energy in step (d); and (f) outputting the interdiffusion coefficient obtained in step (c) and the fracture energy obtained in step (d) as numerical information, and outputting the time evolution of the coordinates as a video. [Effects of the Invention]

[0013] According to the present invention, in a blend material made by mixing multiple resin materials, it is possible to realize a compatibility prediction system and a compatibility prediction method between dissimilar resin materials that can predict the compatibility between constituent materials, such as compatibility and interfacial adhesion.

[0014] This makes it possible to efficiently obtain blended materials with the desired properties.

[0015] Other issues, configurations, and effects not mentioned above will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0016] [Figure 1] This figure shows a schematic configuration of a compatibility prediction system between dissimilar resin materials according to Example 1 of the present invention. [Figure 2] Figure 1 is a flowchart showing the processing details of the calculation model creation module 110, the MD calculation module 120, and the simulation result output unit 130. [Figure 3] Figure 1 is a flowchart showing the method for building the learning model of the result prediction module 140. [Figure 4] Figure 1 is a flowchart showing the processing details of the result prediction module 140 and the simulation result prediction value output unit 150. [Figure 5] Figure 1 is a flowchart showing the processing details of the additive structure prediction module 160 and the additive structure output unit 170. [Figure 6] This figure shows a schematic configuration of a compatibility prediction system between dissimilar resin materials according to Example 2 of the present invention. [Figure 7] Figure 6 is a flowchart showing the processing details of the modified chemical structure prediction module 180 and the modified chemical structure output unit 190. [Figure 8] This figure shows a schematic configuration of a compatibility prediction system between dissimilar resin materials according to Example 3 of the present invention. [Modes for carrying out the invention]

[0017] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In each drawing, the same components are denoted by the same reference numerals, and detailed descriptions of overlapping parts will be omitted.

Embodiment

[0018] Referring to FIGS. 1 to 5, a compatibility prediction system between different resin materials and a compatibility prediction method between different resin materials according to Embodiment 1 of the present invention will be described.

[0019] FIG. 1 is a diagram showing a schematic configuration of a compatibility prediction system 100 between different resin materials of this embodiment. FIG. 2 is a flowchart showing the processing contents of the calculation model creation module 110, MD calculation module 120, and simulation result output unit 130 in FIG. 1. FIG. 3 is a flowchart showing a learning model construction method of the result prediction module 140 in FIG. 1. FIG. 4 is a flowchart showing the processing contents of the result prediction module 140 and the output unit 150 of the simulation result predicted value in FIG. 1. FIG. 5 is a flowchart showing the processing contents of the additive structure prediction module 160 and the output unit 170 of the additive structure in FIG. 1 As shown in FIG. 1, the compatibility prediction system 100 between different resin materials of this embodiment mainly includes a calculation model creation module 110, an MD (Molecular Dynamics) calculation module 120, a simulation result output unit 130, a result prediction module 140, an output unit 150 of the simulation result predicted value, an additive structure prediction module 160, and an output unit 170 of the additive structure.

[0020] The calculation model creation module 110 is composed of a three-dimensional molecular model creation unit 111 and a molecular entanglement model creation unit 112. The MD calculation module 120 is composed of a mutual diffusion coefficient calculation unit 121, a destruction energy calculation unit 122, and a trajectory storage unit 123. The result prediction module 140 is composed of a simulation result storage unit 141 and a simulation result prediction unit 142. The additive structure prediction module 160 is composed of a prediction unit 161 of the additive structure.

[0021] The calculation model creation module 110 has the function of converting the chemical structure of the input resin material and generating a three-dimensional molecular model and molecular entanglement model necessary for performing MD (molecular dynamics) calculations. The MD calculation module 120 performs MD calculations using the molecular entanglement model passed from the calculation model creation module 110, calculates the interdiffusion coefficient and fracture energy, and saves the trajectory during the MD calculation. The simulation result output unit 130 outputs the interdiffusion coefficient, fracture energy, and trajectory passed from the MD calculation module 120.

[0022] The three-dimensional molecular model creation unit 111 takes numerical information of the chemical structures of multiple resin materials as input and creates a three-dimensional molecular model for each resin material. The molecular entanglement model creation unit 112 takes the three-dimensional molecular model, temperature, and pressure as input and creates a molecular entanglement model of dissimilar resin materials by combining two types of three-dimensional molecular models for each resin material.

[0023] The interdiffusion coefficient calculation unit 121 performs molecular simulations based on a molecular entanglement model to calculate the interdiffusion coefficient between different resin materials. The fracture energy calculation unit 122 performs molecular simulations based on a molecular entanglement model to calculate the fracture energy. The trajectory storage unit 123 saves the time evolution of the coordinates of the molecular entanglement model during the fracture energy calculation process. The simulation result output unit 130 outputs the interdiffusion coefficient and fracture energy as numerical information and outputs the time evolution of the coordinates as a video.

[0024] The result prediction module 140 has the function of predicting the interdiffusion coefficient and fracture energy, which are the results of MD calculations, without actually performing MD calculations, by using a learning model, with numerical information representing combinations of resin materials obtained by transforming the chemical structures of two or more input resin materials as explanatory variables. The simulation result prediction value output unit 150 outputs the predicted values ​​of the MD calculation results passed from the result prediction module 140.

[0025] The simulation result storage unit 141 stores the combination of chemical structures used in the molecular simulation and the calculated results as a set. The simulation result prediction unit 142 uses the combination of chemical structures of the resin materials as input information and predicts the simulation results based on a learned model. The simulation result prediction value output unit 150 outputs the interdiffusion coefficients and fracture energy prediction values ​​between multiple resin materials calculated by the simulation result prediction unit 142.

[0026] The additive structure prediction module 160 combines numerical information representing the chemical structure of the resin material, obtained by converting the chemical structure of the input resin material, with numerical information representing the chemical structure of the additive, obtained by converting the chemical structure of the acquired additive structure. Using this numerical information representing the combination of resin material and additive as explanatory variables, the module uses a learning model to predict the interdiffusion coefficient and fracture energy, which are the results of MD calculations.

[0027] The additive structure prediction module 160 has the function of repeatedly predicting the MD calculation results as described above, while changing the additive structure until the interdiffusion coefficient and fracture energy exceed the required values. The additive structure output unit 170 outputs the additive structure that is predicted to exhibit an interdiffusion coefficient and fracture energy of a certain resin material that are greater than or equal to a predetermined value, as received from the additive structure prediction module 160.

[0028] The additive structure prediction unit 161 is an additive chemical structure inverse analysis unit that uses the chemical structure of the resin material as input information to predict the chemical structure of additives that exhibit a mutual diffusion coefficient or fracture energy greater than or equal to a predetermined value relative to the resin material.

[0029] The operation of the calculation model creation module 110, the MD calculation module 120, and the simulation result output unit 130 will be explained using Figure 2.

[0030] First, in step S11, the three-dimensional molecular model creation unit 111 of the calculation model creation module 110 converts the chemical structures of two or more input resin materials and creates a three-dimensional single-molecule model for each resin material.

[0031] Next, in step S12, the three-dimensional molecular model creation unit 111 creates a three-dimensional multi-molecule model of the resin material by arranging multiple three-dimensional single-molecule models of the resin material within the simulation box.

[0032] Next, in step S13, the three-dimensional molecular model creation unit 111 performs a thermal equilibrium MD calculation on the three-dimensional multi-molecular model of the resin material to stabilize the structure of the three-dimensional multi-molecular model.

[0033] Next, in step S14, the molecular entanglement model creation unit 112 of the calculation model creation module 110 combines three-dimensional multi-molecular models of two or more resin materials for which thermal equilibrium MD calculations have been performed to create a molecular entanglement model.

[0034] Next, in step S15, the interdiffusion coefficient calculation unit 121 of the MD calculation module 120 performs a thermal equilibrium MD calculation on the molecular entanglement model to stabilize the structure of the molecular entanglement model and calculate the interdiffusion coefficient from the atomic trajectory data during the MD calculation.

[0035] Next, in step S16, the fracture energy calculation unit 122 of the MD calculation module 120 performs a box deformation MD calculation on the molecular entanglement model for which a thermal equilibrium MD calculation has already been performed, and calculates the interfacial fracture energy from the stress and deformation data during the MD calculation.

[0036] Finally, in step S17, the simulation result output unit 130 saves and outputs the interdiffusion coefficient, interface fracture energy, and trajectory.

[0037] The material properties of a polymer blend are influenced by various factors, including the properties of the constituent materials, the mixing ratio of the constituent materials, and the blending conditions. However, the most important aspects are the compatibility and interfacial adhesion between the constituent materials. In polymer blends formed by mixing multiple resin materials, a phase separation structure appears. Therefore, selecting a combination of materials with high compatibility and interfacial adhesion is a necessary condition for creating a tough material.

[0038] However, in reality, as mentioned earlier, various bulk properties affect the properties of polymer blend materials. Therefore, especially when designing a new polymer blend for a new material system, it is difficult to determine whether the problem lies in the bulk properties themselves or in the compatibility between the materials, requiring a vast number of prototypes and trial and error.

[0039] Focusing on properties such as compatibility and interfacial adhesion between constituent materials, molecular dynamics (MD) calculations, a computer simulation method, can be an effective evaluation method. MD calculations define interaction potentials between atoms and molecules and simulate their physical movements using computer simulations, allowing for the visualization of processes such as thermal diffusion and deformation at the atomic and molecular level.

[0040] Generally, polymer systems such as plastics are composed of a vast number of atoms, making it difficult to completely reproduce microphase separation structures and crystalline structures using MD calculations to predict material properties from a computational time perspective. However, for nanoscale structures such as miscibility interfaces between dissimilar materials, simulations can be completed within a realistic timeframe using commercial computing workstations.

[0041] Furthermore, since it is possible to visualize the mechanism by which compatibility and interfacial adhesion occur in a given combination of resin materials at the atomic and molecular level, it is also possible to consider methods to improve the compatibility between resin materials. Therefore, MD calculations can be said to be an extremely effective computer simulation method for predicting the compatibility between resin materials in advance and considering improvement plans prior to the prototyping of polymer blends.

[0042] As shown in the flowchart in Figure 2, the compatibility prediction system 100 for dissimilar resin materials of the present invention calculates the interdiffusion coefficient and interfacial fracture energy when two or more input resin materials are combined using MD calculations, and quantitatively evaluates the compatibility between the resin materials. Furthermore, the time evolution of the interdiffusion phenomenon and the interfacial fracture phenomenon can be confirmed by trajectory analysis. As a result, users who are designing polymer blends can find combinations of compatible resin materials with high interdiffusivity and interfacial fracture energy, thereby reducing the number of prototypes required in material design.

[0043] Figure 3 illustrates the process of creating a learning model in the result prediction module 140.

[0044] First, in step S21, the simulation result prediction unit 142 of the result prediction module 140 converts the chemical structures of two or more input resin materials and generates numerical information that represents the chemical structure of each resin material.

[0045] Next, in step S22, the simulation result prediction unit 142 combines numerical information representing the chemical structures of two or more resin materials to generate numerical information representing a combination of resin materials.

[0046] Next, in step S23, the simulation result prediction unit 142 acquires past MD calculation results performed with combinations of resin materials and associates them with numerical information representing combinations of resin materials.

[0047] Finally, in step S24, the simulation result prediction unit 142 creates a prediction model of the MD calculation results, using numerical information representing the combination of resin materials as explanatory variables and the interdiffusion coefficient and interfacial fracture energy derived from the MD calculation results as objective variables.

[0048] The operation of the result prediction module 140 and the simulation result prediction output unit 150 will be explained using Figure 4. First, in step S31, the simulation result prediction unit 142 of the result prediction module 140 converts the chemical structures of two or more input resin materials and generates numerical information that represents the chemical structure of each resin material.

[0049] Next, in step S32, the simulation result prediction unit 142 combines numerical information representing the chemical structures of two or more resin materials to generate numerical information representing a combination of resin materials.

[0050] Finally, in step S33, the simulation result prediction unit 142 predicts the MD calculation result using numerical information representing the combination of resin materials as explanatory variables, saves it using the simulation result storage unit 141 of the result prediction module 140, and outputs the predicted simulation result value using the output unit 150.

[0051] When converting the chemical structure of resin materials into numerical information, quantitative values ​​corresponding to that chemical structure are determined using molecular descriptors. Existing methods such as the "fingerprint method," "MACCS key method," "ECFP method," "FCFP method," and "MHFP method," or combinations thereof, can be used as molecular descriptors. For numerical information representing combinations of resin materials, the molecular descriptors of each resin material constituting the combination are combined using mathematical methods to define a molecular descriptor representing the combination of resin materials.

[0052] Possible methods for creating molecular descriptors representing combinations of resin materials include using scalar values ​​obtained by averaging the molecular descriptors of individual resin materials, or using vectors or tensors whose components are the molecular descriptors of individual resin materials. This allows users designing polymer blends to easily predict the compatibility between resin materials based on past knowledge without actually performing MD calculations, thus shortening the time required to complete the compatibility prediction.

[0053] The operation of the additive structure prediction module 160 and the additive structure output unit 170 will be explained using Figure 5.

[0054] First, in step S41, the additive structure prediction unit 161 of the additive structure prediction module 160 converts the chemical structures of two or more input resin materials and generates numerical information that represents the chemical structure of each resin material.

[0055] Next, in step S42, the additive structure prediction unit 161 obtains the chemical structure of a substance that is a candidate material for the additive and generates numerical information that represents the chemical structure of the additive.

[0056] Next, in step S43, the additive structure prediction unit 161 combines numerical information representing the chemical structure of the resin material and numerical information representing the chemical structure of the additive to generate numerical information representing the combination of the resin material and the additive.

[0057] Next, in step S44, the additive structure prediction unit 161 predicts and outputs the MD calculation result using numerical information representing the combination of resin material and additive as explanatory variables.

[0058] Next, in step S45, the additive structure prediction unit 161 determines whether the predicted MD calculation result satisfies the required characteristics. If the predicted MD calculation result satisfies the required characteristics (Yes), the process proceeds to step S46; otherwise, it returns to step S42 and repeats the process from step S42 onward.

[0059] Finally, in step S46, the additive structure output unit 170 outputs the chemical structure of the additive that satisfies the required characteristics and the predicted values ​​of the MD calculation results.

[0060] The additive structure prediction module 160 uses the learning model constructed in the result prediction module 140 in an inverse analytical manner to predict the chemical structure of an additive that can exhibit a high interdiffusion coefficient and interfacial fracture energy with respect to a given resin material. When designing polymer blends, introducing additives with high affinity to multiple resin materials is a very effective formulation that enables combinations of resin materials that would not otherwise be compatible. However, selecting additives requires extremely high levels of expertise, and even with that expertise, the investigation of materials, prototyping, and verification of their effectiveness through property evaluation require a very long time, which is a challenge.

[0061] Therefore, by using the additive structure prediction module 160 and the additive structure output unit 170 of the present invention, users who wish to design a polymer blend can reduce the man-hours required for investigating, prototyping, and evaluating the physical properties of additives, and then design a polymer blend material by combining the desired resin materials.

[0062] ≪Variations≫ Although not shown in the figures, as a modification of this embodiment, it is also possible to change the ratio of multiple resin materials instead of the additive structure. In this case, instead of the additive structure prediction module 160, the additive structure prediction unit 161, and the additive structure output unit 170, the system may be configured to include a resin material ratio prediction module, a ratio prediction unit that takes the chemical structure of the resin materials as input information and predicts the ratio of multiple resin materials that exhibit a mutual diffusion coefficient or fracture energy of a predetermined value or higher relative to the resin material, and a resin material ratio output unit that outputs the ratio of multiple resin materials obtained by the ratio prediction unit. [Examples]

[0063] Referring to Figures 6 and 7, a compatibility prediction system and method for predicting compatibility between dissimilar resin materials according to Example 2 of the present invention will be described.

[0064] Figure 6 shows a schematic configuration of the compatibility prediction system 100 between dissimilar resin materials in this embodiment. Figure 7 is a flowchart showing the processing contents of the modified chemical structure prediction module 180 and the modified chemical structure output unit 190 in Figure 6. Matters described in Example 1 but not described in this embodiment are applicable to this embodiment unless there are special circumstances.

[0065] As shown in Figure 6, the compatibility prediction system 100 between dissimilar resin materials in this embodiment differs from the compatibility prediction system 100 between dissimilar resin materials in Embodiment 1 (Figure 1) in that it includes a modified chemical structure prediction module 180, a modified chemical structure prediction unit 181, and a modified chemical structure output unit 190, instead of the additive structure prediction module 160, additive structure prediction unit 161, and additive structure output unit 170 of Embodiment 1 (Figure 1). The other configurations are the same as in Embodiment 1 (Figure 1).

[0066] The modified chemical structure prediction unit 181 of the modified chemical structure prediction module 180 is a side-chain structure inverse analysis unit that takes the chemical structure of the resin material as input information and predicts the chemical structure of the side chains that exhibit a mutual diffusion coefficient or fracture energy greater than or equal to a predetermined value for the resin material. The modified chemical structure output unit 190 outputs the side-chain structure obtained by the modified chemical structure prediction unit 181 (inverse analysis unit).

[0067] In this embodiment, we will describe an example in which a modified chemical structure is used in which a side chain structure is added to the chemical structure of a resin material so that the interdiffusion coefficient and interfacial fracture energy of two or more input resin materials are equal to or greater than the required values. Below, using Figure 7, we will explain the operation by which the modified chemical structure prediction module 180 and the modified chemical structure output unit 190 propose a modified chemical structure in which a side chain structure is added to the chemical structure of a resin material.

[0068] First, in step S51, the modified chemical structure prediction unit 181 of the modified chemical structure prediction module 180 converts the chemical structures of two or more input resin materials and generates numerical information that represents the chemical structure of each resin material.

[0069] Next, in step S52, the modified chemical structure prediction unit 181 introduces candidate side chain structures for the modified chemical structure to part or all of each resin material and modifies the numerical information representing the chemical structure of the resin material.

[0070] Next, in step S53, the modified chemical structure prediction unit 181 combines numerical information representing the chemical structures of two or more resin materials to generate numerical information representing a combination of resin materials.

[0071] Next, in step S54, the modified chemical structure prediction unit 181 predicts and outputs the MD calculation result using numerical information representing the combination of resin materials as explanatory variables.

[0072] Next, in step S55, the modified chemical structure prediction unit 181 determines whether the predicted MD calculation result satisfies the required characteristics. If the predicted MD calculation result satisfies the required characteristics (Yes), the process proceeds to step S56; otherwise, it returns to step S52 and repeats the process from step S52 onward.

[0073] Finally, in step S56, the modified chemical structure output unit 190 outputs the modified chemical structure that satisfies the required characteristics and the predicted values ​​of the MD calculation results.

[0074] When designing polymer blends, introducing functional groups and side chains that enhance the affinity between resin materials is a highly effective way to enable combinations of resins that would not otherwise be compatible. However, selecting functional groups and side chain structures requires advanced chemical knowledge, and experiments to demonstrate their effectiveness are time-consuming and costly. According to this embodiment, polymer blend material designers can know effective functional groups and side chain structures without conducting experiments, allowing users to design polymer blend materials while reducing the man-hours required for investigating, prototyping, and evaluating the physical properties of side chain structures. [Examples]

[0075] Referring to Figure 8, a compatibility prediction system and method for predicting compatibility between dissimilar resin materials according to Example 3 of the present invention will be described.

[0076] Figure 8 shows a schematic configuration of the compatibility prediction system 100 between dissimilar resin materials in this embodiment. Note that matters described in Example 1 but not described in this embodiment are applicable to this embodiment unless there are special circumstances.

[0077] As shown in Figure 8, the compatibility prediction system 100 between dissimilar resin materials in this embodiment differs from the compatibility prediction system 100 between dissimilar resin materials in Example 1 (Figure 1) in that, in addition to the configuration of Example 1 (Figure 1), it further includes a calculation method selection module 200, a low-speed / high-precision calculation / high-speed / low-precision calculation selection unit 201, and an inter-entanglement point molecular weight reference unit 202. The other configurations are the same as in Example 1 (Figure 1).

[0078] The calculation method selection module 200's slow / high-precision calculation / high-speed / low-precision calculation selection unit 201 selects whether to perform a high-precision, slow molecular simulation or a low-precision, high-speed molecular simulation. The entanglement point molecular weight reference unit 202 references information on the entanglement point molecular weight of the resin material.

[0079] In this embodiment, there is a selection unit (slow-speed / high-precision calculation selection unit 201) that selects whether to perform a high-precision, low-speed molecular simulation or a low-precision, high-speed molecular simulation, and a reference unit (inter-entanglement point molecular weight reference unit 202) that refers to information on the inter-entanglement point molecular weight of the resin material. When a high-precision, low-speed molecular simulation is selected, a molecular model is created with a degree of polymerization that is more than twice the inter-entanglement point molecular weight and the simulation is performed. When a low-precision, high-speed molecular simulation is selected, a molecular model is created with a degree of polymerization that is less than one times the inter-entanglement point molecular weight and the simulation is performed.

[0080] Interfacial phenomena between dissimilar resin materials can be analyzed using commercial computing workstations, allowing for MD calculations to be performed within a sufficiently realistic timeframe. However, considering cases where calculations are performed on general-purpose computers or where results need to be obtained quickly, having an optional function that allows selection between a low-speed, high-precision calculation method and a high-speed, low-precision calculation method is useful for the system of the present invention.

[0081] Within polymer materials such as plastics, string-like polymers are intertwined with other string-like polymers, and the mechanical properties of the plastic material are exhibited by the geometric resistance they experience at the entanglement points. In this case, the average molecular weight between two entanglement points is called the inter-entanglement molecular weight, and a sufficient entanglement structure cannot be formed at molecular weights below twice the inter-entanglement molecular weight. Therefore, the mechanical properties of the polymer material change significantly when the molecular weight is around twice the inter-entanglement molecular weight. Consequently, when evaluating mechanical properties such as interfacial fracture energy, it is preferable to use a three-dimensional molecular model set to twice or more the inter-entanglement molecular weight.

[0082] On the other hand, it is known that even if the molecular weight between entanglement points is less than 1, the compatibility characteristics remain generally constant when the molecular weight is between 1000 and 5000 or more. Therefore, it is considered that a three-dimensional molecular model with a molecular weight of this magnitude is sufficient for qualitatively evaluating the interdiffusion coefficient. Accordingly, according to this embodiment, users with sufficient computing resources can obtain information on the compatibility between resin materials, including mechanical properties such as interfacial fracture energy, with high accuracy, while users without sufficient computing resources can obtain the minimum necessary properties related to compatibility, such as the interdiffusion coefficient, in a short time.

[0083] According to the embodiments described above, a materials designer attempting to create a polymer blend can eliminate incompatible polymer combinations in advance and focus on prototyping only promising combinations. Furthermore, it is possible to obtain the material composition and ratios, types of additives, and chemical modification methods necessary for creating a polymer blend with desired material properties.

[0084] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0085] Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, in whole or in part, for example, by designing them as integrated circuits. Alternatively, each of the above configurations, functions, and means may be implemented in software by having the processor interpret and execute programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, or in recording devices and recording media such as hard disks and SSDs (Solid State Drives). [Explanation of Symbols]

[0086] 100... Compatibility prediction system between dissimilar resin materials 110... Computational model creation module 111... Three-dimensional molecular model creation department 112... Molecular Entanglement Model Creation Department 120…MD (Molecular Dynamics) Calculation Module 121... Mutual diffusion coefficient calculation unit 122... Destruction Energy Calculation Unit 123...Trajectory Preservation Department 130...Simulation result output section 140…Result prediction module 141...Simulation result storage unit 142...Simulation Result Prediction Unit 150... Output section for simulation result predictions 160…Additive structure prediction module 161…Prediction section for additive structure 170... Output section of additive structure 180… Modified Chemical Structure Prediction Module 181... Prediction section for modified chemical structure 190... Output section for modified chemical structures 200... Calculation method selection module 201...Selection section for low-speed / high-precision calculation / high-speed / low-precision calculation 202... Molecular weight reference region between entanglement points.

Claims

1. A three-dimensional molecular model creation unit creates a three-dimensional molecular model of each of the resin materials, using numerical information of the chemical structures of multiple resin materials as input information. A molecular entanglement model creation unit creates a molecular entanglement model of dissimilar resin materials by combining two types of three-dimensional molecular models of each resin material, using the aforementioned three-dimensional molecular model, temperature, and pressure as input information. A mutual diffusion coefficient calculation unit that performs molecular simulations based on the aforementioned molecular entanglement model and calculates the mutual diffusion coefficient between different resin materials, A fracture energy calculation unit that performs molecular simulations based on the aforementioned molecular entanglement model and calculates fracture energy, A trajectory storage unit that stores the time evolution of the coordinates of the molecular entanglement model in the calculation process of the fracture energy, A simulation result output unit outputs the aforementioned mutual diffusion coefficient and the aforementioned fracture energy as numerical information, and outputs the time evolution of the coordinates as a video, A compatibility prediction system for dissimilar resin materials.

2. A compatibility prediction system between different resin materials according to claim 1, A simulation result storage unit that stores the combination of chemical structures obtained from molecular simulations and the calculated results as a set, A simulation result prediction unit that uses combinations of chemical structures of resin materials as input information and predicts simulation results based on a learning model, A simulation result prediction value output unit that outputs the interdiffusion coefficient and fracture energy prediction value between multiple resin materials calculated by the simulation result prediction unit, A compatibility prediction system for dissimilar resin materials.

3. A compatibility prediction system between different resin materials according to claim 1, An additive chemical structure inverse analysis unit takes the chemical structure of a resin material as input information and predicts the chemical structure of an additive that exhibits a mutual diffusion coefficient or fracture energy of a predetermined value or higher relative to the resin material; An additive structure output unit outputs the additive structure obtained by the inverse analysis unit, A compatibility prediction system for dissimilar resin materials.

4. A compatibility prediction system between different resin materials according to claim 1, A side-chain structure inverse analysis unit takes the chemical structure of a resin material as input information and predicts the chemical structure of side chains that exhibit a mutual diffusion coefficient or fracture energy of a predetermined value or higher for the resin material, A side chain structure output unit outputs the side chain structure obtained by the inverse analysis unit, A compatibility prediction system for dissimilar resin materials.

5. A compatibility prediction system between different resin materials according to claim 1, A selection unit that allows you to choose whether to perform high-precision, low-speed molecular simulations or low-precision, high-speed molecular simulations, It comprises a reference unit that references information on molecular weight between entanglement points of resin materials, If you choose high-precision, low-speed molecular simulation, create a molecular model with a degree of polymerization that is more than twice the molecular weight between entanglement points and run the simulation. A compatibility prediction system for dissimilar resin materials that, when low-precision and high-speed molecular simulation is selected, creates a molecular model with a degree of polymerization less than one times the molecular weight between entanglement points and performs simulations.

6. A compatibility prediction system between different resin materials according to claim 1, A ratio prediction unit that takes the chemical structure of a resin material as input information and predicts the ratio of a plurality of resin materials that exhibit a mutual diffusion coefficient or fracture energy of a predetermined value or higher relative to the resin material, A resin material ratio output unit that outputs the ratio of the plurality of resin materials obtained by the ratio prediction unit, A compatibility prediction system for dissimilar resin materials.

7. A method for predicting compatibility between dissimilar resin materials, which is performed in a compatibility prediction system for dissimilar resin materials, (a) A step of creating a three-dimensional molecular model of each resin material using numerical information of the chemical structures of multiple resin materials as input information, (b) A step of creating a molecular entanglement model of dissimilar resin materials by combining two types of three-dimensional molecular models of each resin material, using the three-dimensional molecular model created in step (a) above, along with temperature and pressure as input information, (c) A step of performing a molecular simulation based on the molecular entanglement model created in step (b) above and calculating the interdiffusion coefficient between different resin materials, (d) A step of performing a molecular simulation based on the molecular entanglement model created in step (b) above and calculating the fracture energy, (e) A step of saving the time evolution of the coordinates of the molecular entanglement model in the calculation process of the fracture energy in step (d), (f) A step in which the interdiffusion coefficient obtained in step (c) and the fracture energy obtained in step (d) are output as numerical information, and the time evolution of the coordinates is output as a video, A method for predicting compatibility between dissimilar resin materials.

8. A method for predicting compatibility between different resin materials according to claim 7, (g) A step to save the combination of chemical structures for which molecular simulations were performed and the calculated results as a set, (h) A step of predicting the simulation result based on a learning model using combinations of chemical structures of resin materials as input information, (i) A step of outputting the interdiffusion coefficients and fracture energy prediction values ​​between the multiple resin materials obtained in step (h) above, A method for predicting compatibility between dissimilar resin materials.

9. A method for predicting compatibility between different resin materials according to claim 7, (j) A step of predicting the chemical structure of an additive that exhibits a mutual diffusion coefficient or fracture energy of a predetermined value or higher relative to the resin material, using the chemical structure of the resin material as input information, (k) A step of outputting the additive structure obtained in step (j) above, A method for predicting compatibility between dissimilar resin materials.

10. A method for predicting compatibility between different resin materials according to claim 7, (l) A step of predicting the chemical structure of a side chain that exhibits a mutual diffusion coefficient or fracture energy of a predetermined value or higher for the resin material, using the chemical structure of the resin material as input information, (m) A step of outputting the side chain structure obtained in step (l), A method for predicting compatibility between dissimilar resin materials.

11. A method for predicting compatibility between different resin materials according to claim 7, (n) A step of selecting whether to perform a high-precision, low-speed molecular simulation or a low-precision, high-speed molecular simulation, (o) The step of referring to information on the molecular weight between entanglement points of the resin material, In step (n) above, if high-precision and low-speed molecular simulation is selected, a molecular model is created with a degree of polymerization that is more than twice the molecular weight between entanglement points, and the simulation is performed. A method for predicting compatibility between dissimilar resin materials, wherein, in step (n) above, if a low-precision and high-speed molecular simulation is selected, a molecular model is created with a degree of polymerization less than one times the molecular weight between entanglement points and the simulation is performed.

12. A method for predicting compatibility between different resin materials according to claim 7, (p) A step of predicting the ratio of a plurality of resin materials that exhibit a mutual diffusion coefficient or fracture energy of a predetermined value or higher relative to the resin material, using the chemical structure of the resin material as input information, (q) A step of outputting the ratio of the plurality of resin materials obtained in step (p), A method for predicting compatibility between dissimilar resin materials.

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