Simulation method, simulation program, and ophthalmic lens

Abstract

This simulation method comprises: a step (S101) for acquiring information which indicates activation energy; a step (S102) for calculating density and solubility parameters; a step (S103) for calculating a repulsive force parameter; a step (S104) for calculating a reaction probability; a step (S105) for performing structural relaxation; and a step (S107) for analyzing a three-dimensional structure.

Description

Simulation method, simulation program, and ophthalmic lens

[0001] This disclosure relates to a simulation method, a simulation program, and an ophthalmic lens.

[0002] Conventionally, various methods have been known for simulating polymer materials (for example, Patent Document 1). Patent Document 1 describes a method for analyzing the influence of crosslinking configurations between multiple molecular chains on the material properties of polymer materials used in automobile tires, using molecular dynamics.

[0003] Japanese Patent Application Publication No. 2015-169601

[0004] Polymer materials are used in various fields besides automobile tires. Among polymer materials, ophthalmic lens materials used in eye lenses are sometimes produced using raw material components with opposing properties (e.g., hydrophilic and hydrophobic). In this case, the three-dimensional structure formed by each raw material component is a factor that determines the physical properties and functions of the ophthalmic lens material. Small-angle X-ray scattering (SAXS) is a known method for analyzing such three-dimensional structures. However, with such methods, the three-dimensional structure changes depending on the mixing ratio of the raw material components, and it is necessary to analyze the structure each time the mixing ratio is adjusted. Therefore, optimizing the mixing ratio of the raw material components would require an enormous amount of time and cost.

[0005] This disclosure was made in view of the above-mentioned problems and aims to analyze the three-dimensional structure of ophthalmic lens materials through simulation.

[0006] To solve the above problems, a simulation method according to one aspect of the present disclosure is a simulation method for an ophthalmic lens material obtained by copolymerizing at least a first component and a second component, comprising: an acquisition step in which a computer obtains information indicating the activation energy relating to the copolymerization; a first calculation step in which a computer calculates the density and solubility parameters of a first coarse-grained model and a second coarse-grained model obtained by coarsing the first component and the second component, respectively; and, under preset mixing ratio conditions of the first component and the second component, the simulation method calculates the first coarse-grained model based on the density, the solubility parameter, and the mixing ratio. The method includes: a second calculation step of calculating a repulsive force parameter indicating the repulsive force acting between the models in each combination of the first coarse-grained model and the second coarse-grained model; a third calculation step of calculating the reaction probability between the models in each combination of the first coarse-grained model and the second coarse-grained model using the repulsive force parameter and the activation energy; a structural relaxation step of causing the models in each combination of the first coarse-grained model and the second coarse-grained model to react and relax based on the reaction probability; and a structural analysis step of analyzing the three-dimensional structure of the first coarse-grained model and the second coarse-grained model formed by the structural relaxation.

[0007] A simulation device that performs simulations according to each aspect of this disclosure may be implemented by a computer. In this case, a simulation program that enables the computer to implement the simulation device by operating the computer as each part (software element) of the simulation device, and a computer-readable recording medium on which the program is recorded, also fall within the scope of this disclosure.

[0008] According to one aspect of this disclosure, it is possible to analyze the three-dimensional structure of an ophthalmic lens material by simulation.

[0009] This is a diagram illustrating an example of the application of the simulation according to Embodiment 1. This is a block diagram showing an example of the hardware configuration of the simulation device according to Embodiment 1. This is a block diagram showing an example of the functions of the processor of the simulation device according to Embodiment 1. This is a diagram schematically showing coarse-grained particles. This is a diagram explaining the structure of each component. This is a diagram schematically showing the coarse-grained reaction model. This is a graph showing the change in reaction rate according to Embodiment 1. This is a graph showing the analysis results by structure factor analysis. This is a flowchart showing an example of the processing performed by the simulation device according to Embodiment 1. This is a graph showing the analysis results by SAXS. This is a graph showing the analysis results by structure factor analysis of PDMS / PDMS pairs. This is a block diagram showing an example of the functions of the processor of the simulation device according to Embodiment 3. This is a flowchart showing an example of the processing performed by the simulation device according to Embodiment 3. This is a flowchart showing an example of the processing performed by the simulation device according to Embodiment 3. This is a graph showing an example of the simulation results according to Embodiment 3.

[0010] Each embodiment of this disclosure will be described in detail below with reference to the drawings. In the drawings, identical or substantially identical components will be denoted by the same reference numerals and will not be repeated in the description.

[0011] The terms "First," "Second," "Third," etc., used in this disclosure are used to distinguish one component from another, and are not intended to limit the number, order, or priority of such components. For example, the presence of "First Element" and "Second Element" does not mean that only two elements, "First Element" and "Second Element," are adopted, nor does it mean that "First Element" must precede "Second Element."

[0012] [Embodiment 1] (Example of Simulation Application) First, an example of the simulation application according to Embodiment 1 will be described with reference to Figure 1. Figure 1 is a diagram illustrating the outline of an ophthalmic lens to which the simulation according to Embodiment 1 can be applied. That is, the simulation according to Embodiment 1 can be applied, as an example, to the material development of ophthalmic lenses.

[0013] The ophthalmic lens 1 shown in Figure 1 is a lens made from a silicone hydrogel copolymerized with a hydrophilic component and a hydrophobic component (silicone-containing component). An example of a hydrophilic component is the hydrophilic monomer DMAA (N,N-dimethylacrylamide). An example of a hydrophobic component is the silicone monomer PDMS (polydimethylsiloxane) and TRIS (tris(trimethylsiloxy)silylpropyl methacrylate). Note that PDMS is also an amphiphilic component with terminally polymerizable functional groups. The ophthalmic lens 1 made from silicone hydrogel achieves both high oxygen permeability and excellent wearing comfort.

[0014] In order to obtain such high functionality, the scale of the phase-separation structure of the hydrophilic component and the hydrophobic component (for example, the phase-separation structure 2 shown in FIG. 1) is important. Regarding the ratios in which these components are blended and what kind of phase-separation structure can be obtained, it was necessary to conduct experiments such as the small-angle X-ray scattering method (SAXS) described above. That is, conventionally, materials such as silicone hydrogels were created through trial and error, experiments such as SAXS were conducted on the created materials, and searches were made for materials having a phase-separation structure that exhibits the desired functions. However, since such a method was time-consuming and laborious, a method capable of analyzing the phase-separation structure without actually creating the material has been demanded. Therefore, the inventors devised a method (simulation using a computer) capable of analyzing the phase-separation structure without actually creating the material. The details of the simulation will be described below. In Embodiment 1, a case where the phase-separation structure obtained by blending three components of PDMS, DMA, and TRIS is analyzed by simulation will be exemplified, but it is not limited thereto. Another case will be described in Embodiment 2. Note that the ophthalmic lens 1 is assumed to be a soft contact lens, but it is not limited thereto and may be a hard contact lens, or a lens applied to an artificial cornea, corneal onlay, etc.

[0015] (Hardware Configuration of Simulation Device 10) FIG. 2 is a block diagram showing an example of the hardware configuration of a simulation device 10 for analyzing the phase-separation structure 2 (see FIG. 1).

[0016] As shown in FIG. 2, the simulation device 10 includes a processor 11, a memory 12, a storage 13, a communication I / F 14, and an input / output I / F 15. These components are connected to each other via a bus 16 so as to be communicable with each other.

[0017] The processor 11 reads a program from the storage 13, expands it in the memory 12, and executes processing according to the program. As such a program, a program for causing a computer to execute at least a part of the functions described below is used. Note that the program may exhibit its functions by being combined with other programs already stored in the storage 13 or by being combined with other programs implemented in other devices. Further, the program may be distributed to the simulation device 10 by wireless communication. In this case, the processor 11 expands the distributed program in the memory 12 and executes the processing. That is, the program does not necessarily have to be stored in the storage 13.

[0018] The processor 11 is not particularly limited, and is realized, for example, as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an MPU (Micro Processor Unit), an FPGA (Field-Programmable Gate Array), or the like. Note that in FIG. 2, one processor is illustrated, but the present invention is not limited thereto, and a plurality of processors may be provided.

[0019] The memory 12 is a computer-readable recording medium and is configured by, for example, at least one of a RAM (Random Access Memory), a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (registered trademark) (Electrically Erasable Programmable ROM), or the like. Note that such a memory 12 may also be referred to as a register, a cache, a main memory (main storage device), or the like.

[0020] Storage 13 is a computer-readable recording medium that stores programs, various data, etc. Such storage 13 is composed of, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), etc. Storage 13 may also be a portable recording medium such as a flexible disk, optical disk, compact disk, or Blu-ray® disk. Storage 13 is also sometimes referred to as an auxiliary storage device.

[0021] Communication I / F 14 is an interface for communicating with external devices. Communication I / F 14 is implemented, for example, as hardware such as a network adapter, various communication software, or a combination thereof, and is configured to enable wireless or wired communication over a communication network.

[0022] The input / output interface 15 includes various input / output devices for exchanging information with operators, etc. The input / output interface 15 includes, for example, information input devices such as keyboards and pointing devices (e.g., mice, touch panels), audio input devices such as microphones, and image input devices such as cameras. The input / output interface 15 also includes image output devices such as displays and audio output devices such as speakers.

[0023] (Example of Processor 11 Functions) Next, an example of the functions of the processor 11 of the simulation device 10 will be described with reference to Figure 3. Figure 3 is a block diagram showing an example of the functions of the processor 11. The processor 11 reads a program from the storage 13 and executes the program using the memory 12 as a work area, thereby functioning as an activation energy acquisition unit 111, a first calculation unit 112, a second calculation unit 113, a reaction probability calculation unit 114, a structural relaxation unit 115, and a structural analysis unit 116. What is described as "~ unit" here may be rephrased as "~ circuit," "~ device," or "~ equipment," or as "~ step," "~ procedure," or "~ process." In other words, what is described as "~ unit" may be implemented by a program stored in the storage 13 as described above, or by hardware such as elements, devices, boards, and wiring only, or by a combination of software and hardware. These functions will be described below.

[0024] The activation energy acquisition unit 111 acquires information (hereinafter sometimes simply referred to as activation energy) indicating the activation energy related to copolymerization when PDMS, DMAA, and TRIS copolymerize. Such activation energies are classified into activation energies between the same type of material and activation energies between different types of material. Regarding activation energies between the same type of material, the activation energy acquisition unit 111 acquires the activation energy between PDMS and PDMS, the activation energy between DMAA and DMAA, and the activation energy between TRIS and TRIS.

[0025] To explain the activation energy between different types of materials, the activation energy acquisition unit 111 acquires the activation energy between PDMS and DMAA, the activation energy between PDMS and TRIS, and the activation energy between DMAA and TRIS.

[0026] The method for obtaining activation energy is not particularly limited. For example, if the activation energies between the same species and between different species are measured in advance and stored in the storage 13, the activation energy acquisition unit 111 can obtain the activation energies between the same species and between different species by referring to the storage 13.

[0027] The first calculation unit 112 calculates various parameters (e.g., density, solubility parameters) used in dissipative particle dynamics (DPD). Dissipative particle dynamics is a method that coarses up multiple atoms and / or molecules as a single particle and calculates the forces acting on the coarse-grained particle (hereinafter sometimes simply referred to as a coarse-grained particle). Dissipative particle dynamics enables the analysis of structure formation and dynamics on larger time and spatial scales than molecular dynamics (MD). For information on dissipative particle dynamics, please refer to paper 1 (Chandan Kumar Choudhury et al, "Native-Based Dissipative Particle Dynamics Approach for α-Helical Folding," J. Phys. Chem. B, Vol.124 (2020), p.11379-p.11386) and paper 2 (Yue Ma et al, "Dissipative particle dynamics and molecular dynamics simulations on mesoscale structure and proton conduction in a SPEEK / PVDF-g-PSSA membrane," RSC Adv., vol.7 (2017), p.39676-p.39684) if necessary. While this disclosure exemplifies dissipative particle dynamics as a method for analyzing phase separation structures, the method for analyzing phase separation structures is not limited to dissipative particle dynamics.

[0028] In dissipative particle dynamics, the motion of coarse-grained particles follows Newton's equations of motion. The equation of motion for the i-th particle is given by the mass of the particle m. ｉ , speed v ｉ、The force acting on the particle is f ｉ and is expressed by Equation 1.

[0029]

[0030] In the dissipative particle dynamics method, f ｉ is composed of the conservative force F Ｃ acting from other particles (the j-th particle), the dissipative force F Ｄ caused by the exchange of momentum between particles, and the fluctuating force F Ｒ that causes the thermal motion of the particles, as shown in Equation 2.

[0031]

[0032] The conservative force F Ｃ in Equation 2 is such that when the position of the i-th particle is r ｉ , the position of the j-th particle is r ｊ , r ｉｊ = r ｉ - r ｊ , and the cut-off distance is r ｃ , it is expressed by Equation 3.

[0033]

[0034] The a ｉｊ in Equation 3 is a repulsive force parameter indicating how much the coarse-grained particles repel each other, or equivalently, how difficult it is for them to mix.

[0035] Here, with reference to Figure 4, we will explain a model that schematically represents coarse-grained particles (hereinafter sometimes referred to as the coarse-grained model). As shown in Figure 4, the coarse-grained model 24 of the hydrophilic component DMAA exemplifies the case in which one molecule is coarse-grained as one particle. The coarse-grained model 27 of the silicone component TRIS exemplifies the case in which one molecule is coarse-grained as one particle. The coarse-grained model 20 of PDMS, which is both an amphiphilic component and a silicone component, exemplifies the case in which it is coarse-grained (5-mer / 1 bead) by the hydrophilic group R1 indicated by reference numeral 21, the hydrophobic group R2 indicated by reference numeral 22, and the siloxane bond indicated by reference numeral 23. Embodiment 1 describes a case in which three models, coarse-grained model 20, coarse-grained model 24, and coarse-grained model 27, are copolymerized, but the models to be copolymerized are not limited to this combination. For example, the hydrophilic components such as HEMA (2-hydroxyethyl methacrylate) and NVP (N-vinyl-2-pyrrolidone) may be further copolymerized.

[0036] The coarse-grained model shown as an example in Figure 4 may be pre-calculated and stored in storage 13. If the coarse-grained model is stored in storage 13, the first calculation unit 112 can retrieve the coarse-grained model by referring to storage 13 when performing a simulation based on the dissipative particle dynamics method.

[0037] The first calculation unit 112 constructs a bulk liquid system at the single-particle level using the all-atom molecular dynamics method and calculates the density and solubility parameters of PDMS (models 21, 22, and 23, which are subdivisions of the coarse-grained model 20), DMAA (coarse-grained model 24), and TRIS (coarse-grained model 27). The calculation conditions are not particularly limited, but for example, the following conditions may be adopted: NPT ensemble temperature: 300K Pressure: 1atm Force field: DREIDING Charge: QEq

[0038] For more information on DREIDING, please refer to paper 3 (Stephen L. Mayo et al, "DREIDING: a generic force field for molecular simulations," J. Phys. Chem. Vol.94 (1990), pp.8897-8909) if necessary. For more information on QEq, please refer to paper 4 (Anthony K. Rappe et al, "Charge equilibration for molecular dynamics simulations," J. Phys. Chem. Vol.95 (1991), pp.3358-3363) if necessary.

[0039] The Hildebrand solubility parameter (solubility parameter δ) obtained by whole-atom molecular dynamics is expressed by Equation 4.

[0040] Here, E ｃｏｈ : Cohesive energy U ｉ : Average potential energy of a single molecule U ｔｏｔａｌ This is the average potential energy of a condensed system.

[0041] (Density and solubility parameters for each coarse-grained model) The calculation results by the first calculation unit 112, i.e., the density and solubility parameters for each coarse-grained model (PDMS: models 21, 22, and 23 which are subdivisions of coarse-grained model 20, DMAA: coarse-grained model 24, TRIS: coarse-grained model 27), are shown in Table 1.

[0042]

[0043] In Table 1, R part-1 represents the hydrophilic structure of the R part (see Figure 5). R part-2 represents the hydrophobic structure of the R part (see Figure 5). The main difference between R part-1 and R part-2 is the presence or absence of a benzene ring.

[0044] The second calculation unit 113 determines the ratio in which PDMS, DMAA, and TRIS are blended, and in the blending ratio conditions, based on the density and solubility parameters calculated by the first calculation unit 112, the repulsive parameter (as described above a ｉｊ ) is calculated. The repulsive force parameter may also be calculated based on the dissipative particle dynamics method, similar to the density and solubility parameters. The repulsive force parameters calculated based on the dissipative particle dynamics method are classified into heterogeneous repulsive force parameters and homogeneous repulsive force parameters. a above ｉｊ This corresponds to the repulsive force parameter between different types. The second calculation unit 113 calculates the repulsive force parameter a between different types based on the dissipative particle dynamics method. ｉｊ In addition, repulsive parameters between the same species may also be calculated. Below, an example of how to calculate repulsive parameters between different species and between the same species will be described. Note that the above mixing ratio may be set in advance. The method of setting the mixing ratio is not particularly limited, but for example, the mixing ratio may be set by the operator. The operator can set the mixing ratio via the input / output I / F 15 (e.g., keyword, mouse, etc.). Here, we assume that the mixing ratio is "PDMS:TRIS:DMAA = 34:33:33 wt%". Under this assumption, the number-averaged particle volume is expressed by Equation 5.

[0045]

[0046] The reference length of the DPD is r ＤＰＤ If so, r ＤＰＤ Following the convention of setting the number density of DPDs to 3, it is expressed in Equation 6.

[0047]

[0048] (Example of calculating the repulsive force parameter between the same species) The repulsive force parameter between the same species is a ｉｉ If so, a ｉｉ This can be expressed in Equation 7, taking into account the volume of each particle.

[0049] Here, k Ｂ is the Boltzmann constant, and T is the temperature (local temperature of the reaction site).

[0050] As can be seen from Equation 7, the repulsive force parameter a between the same species ｉｉ This varies depending on the density.

[0051] (Example of calculating the repulsive force parameter between different species) Next, the repulsive force parameter a between different species ｉｊ Let's explain one example of how to calculate it. If we let the Flory-Huggins parameter be χ based on the solubility parameter, then χ is expressed by Equation 8.

[0052]

[0053] As can be seen from Equation 8, a large repulsive force acts between particles with different solubility. The repulsive force parameter a between different types of particles. ｉｊ This can be expressed using the parameter χ in equations 9 to 10.

[0054]

[0055]

[0056] (Repulsive force parameters between different and same species) Table 2 shows the repulsive force parameters between different and same species calculated by the second calculation unit 113, that is, calculated considering the mixing ratio.

[0057]

[0058] The reaction probability calculation unit 114 calculates the reaction probability based on the calculation result. An example of how to calculate the reaction probability is described below.

[0059] The reaction probability calculation unit 114 calculates the reaction probabilities of the modeled coarse-grained particles, PDMS (models 21, 22, and 23, which are subdivisions of the coarse-grained model 20), DMAA (coarse-grained model 24), and TRIS (coarse-grained model 27), based on the dissipative particle dynamics method. These polymers are coarse-grained, and the repulsive force (difficulty in approaching) acting between each particle is as described above a ｉｊ This is determined by the following. The reaction probability calculation unit 114 calculates the interparticle distance using the particle coordinates obtained from calculations based on the dissipative particle dynamics method. The reaction probability calculation unit 114 compares the calculated interparticle distance with a preset reaction determination distance and selects particles whose interparticle distance is less than or equal to the reaction determination distance as reaction candidates.

[0060] Here, we will explain the "reaction decision distance." The reaction decision distance is a constant value, which may be set by the operator or a value estimated by other quantum computations may be adopted. For example, the reaction decision distance is the cutoff distance r ｃ It may be set as follows: Cutoff distance r ｃ is, r ｃ = 0.5 × r ＤＰＤ It is calculated as = 4.9 Å (see Equation 6).

[0061] The reaction probability calculation unit 114 calculates the reaction probability p for a candidate reaction, based on the activation energy E. ａ It is calculated from the temperature T (local temperature of the reaction site). The calculation formula is shown in Equation 11.

[0062] Here, A is the collision factor and R is the gas constant.

[0063] In Embodiment 1, the activation energy E used in Equation 11 ａ As the value, we adopted the DMAA polymerization value (3.73 kcal / mol) described in paper 5 (Schrooten et al, "Propagation kinetics of the radical polymerization of methylated acrylamides in aqueous solution," Macromol Chem. Phys. vol.214 (2013) p.2283-p.2294), but we are not limited to this value.

[0064] (Coarse-grained reaction model) Figure 6 is a schematic diagram of the coarse-grained reaction model. Embodiment 1 exemplifies a simulation in which polymerization initiators are not considered and all particles are calculated in a state where they are reactive. However, it is not limited to this, and polymerization initiators may also be considered. As shown in Figure 6, reactive particles (R part-1, DMAA, TRIS) can each react twice. Here, it is assumed that the activation energy is the same for the first reaction (where the other particle is a radical) and the second reaction (where the particle itself is a radical), but it is not limited to this, and different activation energies for different material pairs can also be used.

[0065] (Structural Relaxation) The structural relaxation unit 115 determines whether a reaction has occurred based on the reaction probability p calculated by the reaction probability calculation unit 114, and generates bonds between the reacted particles. Furthermore, the structural relaxation unit 115 relaxes the structure of the post-reaction calculation system based on the dissipation particle dynamics method.

[0066] Table 3 shows the simulation conditions: the mixing ratio of each component (PDMS, DMAA, and TRIS), the number of particles, and the number of molecules. The total number of particles is set to 352512, and the initial box size is L = 47.56 × r ＤＰＤ This was set to 46.7 nm.

[0067]

[0068] The reaction probability calculation unit 114 and the structural relaxation unit 115 repeatedly perform calculations until the desired reaction rate is reached.

[0069] (Changes in reaction rate) Next, with reference to Figure 7, the changes in the reaction rate (differences in reaction rate) obtained by simulation according to Embodiment 1 will be explained. In Figure 7, the horizontal axis shows the overall reaction rate, and the vertical axis shows the reaction rate of each normalized material type pair. In Figure 7, the graph on the right is an enlarged portion of the graph on the left.

[0070] As shown in Figure 7, DMAA-related reactions, which involve a large number of molecules, are dominant. TRIS has a large repulsive parameter (R part-1: 11.16, DMAA: 6.94, TRIS: 94.24), as shown in Table 2, making it difficult for reactants to approach it. In other words, even with the same activation energy, TRIS-related reactions have a slower reaction rate.

[0071] Let's explain the differences in compatibility with TRIS. The compatibility between TRIS and the internal particles of PDMS (PDMS (n=5)) is high (χ = 0.0005). The compatibility between TRIS and the terminal reaction particles of PDMS (R part-1) is low (χ = 1.63). The compatibility between TRIS and DMAA is moderate (χ = 0.12). Therefore, the DMAA / TRIS pair is more reactive than the PDMS / TRIS pair. Thus, according to the simulation described in Embodiment 1, it can be said that a reaction similar to the actual reaction is reproduced.

[0072] The structural analysis unit 116 analyzes the phase separation structure obtained by the structural relaxation calculation performed by the structural relaxation unit 115. The analysis method is not particularly limited, but numerical structural analysis using structural factors may be used.

[0073] (Analysis and evaluation using structural factors) Faber-Ziman's substructure factor S ＦＺ This is expressed in equation 12.

[0074]

[0075] Here, g in equation 12 ｉｊ This is expressed in equation 13.

[0076]

[0077] Figure 8 shows the results of the structural factor analysis. As shown in Figure 8, it can be seen that the structural factor intensity peaks at q = 0.38 in the PDMS / PDMS pair. Also, it can be seen that the structural factor intensity peaks at q = 0.34 in the DMAA / DMAA pair. It can be said that the phase separation structure size within the ophthalmic lens material was predicted by the simulation described in Embodiment 1.

[0078] (Processing Flow) Next, with reference to Figure 9, the processing flow executed by the simulation device 10 will be explained. Figure 9 is a flowchart showing an example of the processing executed by the simulation device 10.

[0079] In step S101, the activation energy acquisition unit 111 acquires the activation energy related to copolymerization when PDMS, DMAA, and TRIS are copolymerized. The method for acquiring the activation energy can be the one described above.

[0080] In step S102, the first calculation unit 112 calculates the density and solubility parameters used in the dissipation particle dynamics method. An example of the calculation method has been described above, so it will not be repeated here.

[0081] In step S103, the second calculation unit 113 calculates the repulsive force parameter based on the density and solubility parameters calculated in step S102, in the blending ratio conditions that indicate the ratio in which PDMS, DMAA, and TRIS are blended. The blending ratio can be predetermined. In Embodiment 1, as an example of the blending ratio, an embodiment in which PDMS:TRIS:DMAA = 34:33:33 wt% was described. The second calculation unit 113 calculates the repulsive force parameter a between the same types ｉｉ and the repulsive force parameter a between different species ｉｊ The second calculation unit 113 can calculate the repulsive force parameter a between the same species using density and mixing ratio. ｉｉ The second calculation unit 113 can calculate the inter-type repulsive force parameter a using the density, mixing ratio, and solubility parameter. ｉｊ It is possible to calculate this.

[0082] In step S104, the reaction probability calculation unit 114 calculates the reaction probability of the modeled coarse-grained particles based on the dissipative particle dynamics method. An example of the calculation method has been described above, so it will not be repeated here.

[0083] In step S105, the structural relaxation unit 115 determines whether a reaction has occurred based on the reaction probability calculated in step S104 and generates bonds between the reacted particles. Furthermore, the structural relaxation unit 115 relaxes the structure of the post-reaction calculation system based on the dissipative particle dynamics method. The processes in steps S104 to S105 are repeatedly executed until the reaction rate exceeds a predetermined value (for example, 95%), that is, until YES is determined in step S106.

[0084] In step S107, the structural analysis unit 116 analyzes the phase separation structure obtained from a series of calculation processes. An example of the analysis method has been described above, so it will not be repeated.

[0085] Note that the processing flow shown in the flowchart in Figure 9 is just one example, and steps may be deleted, new steps added, or the processing order rearranged as long as it does not deviate from the main point.

[0086] [Embodiment 2] Next, Embodiment 2 will be described. Embodiment 1 described a simulation method for analyzing the phase separation structure when three components (PDMS, DMAA, and TRIS) for obtaining a silicone hydrogel are blended, but Embodiment 2 will describe a simulation method for analyzing the phase separation structure when two components (PDMS and DMAA) are blended. The only difference between Embodiment 1 and Embodiment 2 is the type of components blended, so the functions of the activation energy acquisition unit 111, the first calculation unit 112, the second calculation unit 113, the reaction probability calculation unit 114, the structural relaxation unit 115, and the structural analysis unit 116 described in Embodiment 1 operate similarly in Embodiment 2. Therefore, the explanation of configurations that overlap with Embodiment 1 will not be repeated. The following will focus on the differences. In Embodiment 2, the blending ratio of PDMS and DMAA was set to 40:60, 50:50, 60:40, 70:30, 80:20, and 100:0, and simulations were performed for each blending ratio. In Embodiment 2, as in Embodiment 1, the calculation was terminated when the reaction rate exceeded 95%.

[0087] (Comparison of analysis by structural factors and SAXS) Figures 10 and 11 show a comparison of analysis by structural factors and SAXS. Figure 10 shows the results of analysis by SAXS. Figure 11 shows the structural factors (S) of the PDMS / PDMS pair. ＦＺ The analysis results are shown below. As shown in Figure 10, according to SAXS, the intensity peak shifts to the right as the amount of PDMS increases (the domain size decreases). In contrast, in Figure 11, although the case of a 40:60 blending ratio deviates from the trend of SAXS, the other blending ratios show a trend similar to that of SAXS. Therefore, it can be said that the simulation according to Embodiment 2 reproduces the same results as the analysis results of SAXS.

[0088] (Effects) As described above, the following effects can be obtained according to Embodiments 1 and 2.

[0089] The simulation method of this disclosure is a simulation method for an ophthalmic lens material (e.g., silicone hydrogel) obtained by copolymerizing at least a first component (e.g., PDMS) and a second component (e.g., DMAA). The simulation method of this disclosure includes an acquisition step (step S101 in Figure 9) in which a computer acquires information indicating the activation energy related to copolymerization, a first calculation step (step S102 in Figure 9) in which a computer calculates the density and solubility parameters of a first coarse-grained model (e.g., coarse-grained model 20) and a second coarse-grained model (e.g., coarse-grained model 24) obtained by coarse-graining the first and second components respectively, and a repulsive force parameter that indicates the repulsive force acting between the models of each combination of the first and second coarse-grained models based on the density, solubility parameter, and mixing ratio under preset mixing ratio conditions of the first and second components. The process includes: a second calculation step (step S103 in Figure 9) for calculating the ta; a third calculation step (step S104 in Figure 9) for calculating the reaction probability between the models in each combination of the first coarse-grained model and the second coarse-grained model using the repulsion parameter and the activation energy; a structural relaxation step (step S105 in Figure 9) for causing structural relaxation by reacting the models in each combination of the first coarse-grained model and the second coarse-grained model based on the reaction probability; and a structural analysis step (step S107 in Figure 9) for analyzing the three-dimensional structure (e.g., phase separation structure) of the first coarse-grained model and the second coarse-grained model formed by structural relaxation.

[0090] According to the above configuration, for example, the phase separation structure of a silicone hydrogel copolymerized with PDMS and DMAA can be analyzed by simulation. As mentioned above, the simulation of this disclosure can reproduce analyses using SAXS, etc., eliminating the need to actually create materials and perform experiments using SAXS, etc., making it possible to efficiently analyze the phase separation structure of silicone hydrogels. Furthermore, by using well-known optimization methods (e.g., genetic algorithms, grid search, random search, Bayesian optimization, etc.) in conjunction with the simulation of this disclosure, the blending ratio can be optimized. Experiments using SAXS, etc., require adjusting the blending ratio by trial and error, and developing new ophthalmic lens materials that achieve higher oxygen permeability and better wearing comfort takes a tremendous amount of time and cost. In contrast, the simulation of this disclosure can reproduce analyses using SAXS, etc., and it is also possible to optimize the blending ratio, making it possible to efficiently develop new ophthalmic lens materials. This contributes to providing users with more high-performance ophthalmic lenses quickly.

[0091] The components to be blended are not limited to just PDMS and DMAA. As mentioned above, the blend may also consist of three components: PDMS, DMAA, and TRIS. In other words, the ophthalmic lens material may be a material produced by copolymerizing a silicone-containing component (e.g., PDMS), a hydrophilic component (e.g., DMAA), and a third component which is a hydrophobic component (e.g., TRIS). In this case, the computer may, in a first calculation step, calculate the density and solubility parameters of a third coarse-grained model (e.g., coarse-grained model 27) obtained by coarsing the third component; in a second calculation step, calculate the repulsive force parameter used in the dissipative particle dynamics method based on the density, solubility parameter, and mixing ratio under preset mixing ratio conditions of the first, second, and third components; in a third calculation step, calculate the reaction probability between the models in each combination of the first, second, and third coarse-grained models using the repulsive force parameter and activation energy; in a structural relaxation step, react the models in each combination of the first, second, and third coarse-grained models to relax their structure based on the reaction probability; and in a structural analysis step, analyze the three-dimensional structure of the first, second, and third coarse-grained models.

[0092] Even in cases where three components are combined in this manner, the simulations in this disclosure can reproduce analyses using SAXS, etc., eliminating the need to actually create materials and conduct experiments using SAXS, etc., thus enabling efficient analysis of the phase separation structure of silicone hydrogels. Furthermore, the simulations in this disclosure can optimize the mixing ratio of the three components, making it possible to efficiently develop new ophthalmic lens materials. Note that the combinations of components are not limited to combinations of PDMS and DMAA, or PDMS, DMAA, and TRIS. As mentioned above, HEMA, NVP, etc. may also be included. According to the simulations in this disclosure, even when these components are combined arbitrarily, the mixing ratio can be optimized, making it possible to efficiently develop new ophthalmic lens materials.

[0093] Each combination of the first coarse-graining model and the second coarse-graining model may include combinations of the first coarse-graining model and the first coarse-graining model, combinations of the second coarse-graining model and the second coarse-graining model, and combinations of the first coarse-graining model and the second coarse-graining model. Furthermore, the computer may calculate density and solubility parameters based on the all-atom molecular dynamics method in the first calculation step, calculate repulsion parameters based on the dissipative particle dynamics method in the second calculation step, and in the third calculation step calculate the probability, as a reaction probability, that models in each combination will react if their inter-model distance is within a predetermined reaction determination distance. An example of the "inter-model distance" referred to here is the "inter-particle distance" described above.

[0094] Regarding each combination of coarse-graining models, according to Embodiment 1, there are six possible combinations: PDMS / PDMS pair, PDMS / DMAA pair, PDMS / TRIS pair, DMAA / DMAA pair, DMAA / TRIS pair, and TRIS / TRIS pair. Furthermore, regarding each combination of coarse-graining models, according to Embodiment 2, there are three possible combinations: PDMS / PDMS pair, PDMS / DMAA pair, and DMAA / DMAA pair.

[0095] This disclosure may also be expressed as follows: In a first calculation step, the computer may calculate a first solubility parameter and a first density (see Table 1) for a first coarse-grained model of PDMS, a second solubility parameter and a second density (see Table 1) for a second coarse-grained model of DMAA, and a third solubility parameter and a third density (see Table 1) for a third coarse-grained model of TRIS. In this case, the computer may calculate a first repulsive force parameter indicating the repulsive force acting between PDMS and PDMS from the first density and the mixing ratio. The computer may also calculate a second repulsive force parameter indicating the repulsive force acting between DMAA and DMAA from the second density and the mixing ratio. The computer may also calculate a third repulsive force parameter indicating the repulsive force acting between TRIS and TRIS from the third density and the mixing ratio. Furthermore, the computer may calculate a fourth repulsive force parameter, indicating the repulsive force acting between PDMS and DMAA, from the first and second solubility parameters. The computer may also calculate a fifth repulsive force parameter, indicating the repulsive force acting between PDMS and TRIS, from the first and third solubility parameters. Furthermore, the computer may calculate a sixth repulsive force parameter, indicating the repulsive force acting between DMAA and TRIS, from the second and third solubility parameters.

[0096] [Embodiment 3] Next, Embodiment 3 will be described with reference to Figures 12 to 15. First, Figure 12 will be described. Figure 12 is a block diagram showing an example of the functions of the processor 11. The difference between Embodiment 3 and Embodiment 1 is that, as shown in Figure 12, the processor 11 also functions as a transport analysis unit 117. Components that overlap with Embodiment 1 will be referred to by their reference numerals and their descriptions will not be repeated. The differences will be described below.

[0097] (Transport Analysis Unit 117) The transport analysis unit 117 calculates an index indicating the permeability of a predetermined substance within the three-dimensional structure obtained in the structural analysis process described above. The specific calculation method is not particularly limited, but for example, the transport analysis unit 117 may calculate as follows. That is, the transport analysis unit 117 may execute the processes in the order of "binarization processing," "transport processing," and "calculation processing" to calculate the "indicator showing permeability." An example of these processes will be described below.

[0098] Figure 13 is a flowchart showing an example of the process performed by the simulation device 10. The processes in steps S201 to S207 shown in Figure 13 are the same as the processes in steps S101 to S107 shown in Figure 9, so their explanation is omitted. The transport process in step S208 corresponds to the process performed by the transport analysis unit 117.

[0099] Figure 14 is a flowchart showing an example of the "binarization process," "movement process," and "calculation process" performed by the transport analysis unit 117.

[0100] (Step S301: Binarization Process) First, the "binarization process" in Embodiment 3 will be explained. The "binarization process" in Embodiment 3 refers to a process in which a three-dimensional structure is divided into a plurality of grid-like regions, and each grid-like region is binarized into either a first region where the first component (e.g., PDMS) is dominant or a second region where the second component (e.g., DMAA) is dominant. The transport analysis unit 117 uses the three-dimensional structure data (e.g., phase-separated structure data) obtained in the structural analysis process as input data and performs data processing to grid the three-dimensional structure.

[0101] The transport analysis unit 117 determines the cutoff distance r in the DPD simulation described above. ｃ Based on that, for example, 0.5r ｃMultiple grid-like regions having dimensions of a certain size may be set, and the density of coarse-grained model particles contained in each grid-like region may be calculated. In Embodiment 3, the "grid-like regions" may be set as voxels. A voxel is the smallest unit of a cubic element (volume pixel) obtained by dividing three-dimensional space into sections of a certain size, and is a concept that extends the pixel in a two-dimensional image to three dimensions. Each voxel holds positional information and attribute values ​​(e.g., particle density, volume fraction, etc.) in space and is used to represent three-dimensional data in a discrete and computer-friendly form. Embodiment 3 may be a configuration in which the particle distribution is evaluated using the voxels as units, and the phase structure and transport paths of the three-dimensional space are analyzed.

[0102] The method for calculating the density of coarse-grained model particles is not particularly limited, but for example, the transport analysis unit 117 may calculate the density by performing a weighted averaging process that takes into account the relationship between each grid and the surrounding grids. Specifically, a target grid (hereinafter referred to as the "central grid") is used as a reference, and a group of 3 × 3 × 3 grids (27 grids in total) surrounding the central grid are used as reference targets. The transport analysis unit 117 performs a particle density correction process using the weighted averaging method by assigning a weight coefficient of 1 to the particles contained in the central grid and a weight coefficient of 0.3 to the 26 surrounding adjacent grids and performing averaging. Based on the results of this correction process, the transport analysis unit 117 may calculate the final particle density of each grid region.

[0103] Next, the transport analysis unit 117 determines which component is dominant based on the particle density data obtained for each grid region. The transport analysis unit 117 classifies each grid region into either a first region where PDMS is dominant or a second region where DMAA is dominant, and then performs a binarization process.

[0104] Here, the "first region where PDMS is dominant" may be defined as a region in which, for each lattice region within the three-dimensional structure, the density of coarse-grained model particles corresponding to PDMS is determined to be greater than the density of coarse-grained model particles corresponding to DMAA. Similarly, the "second region where DMAA is dominant" may be defined as a region in which the density of coarse-grained model particles corresponding to DMAA is determined to be greater than the density of coarse-grained model particles corresponding to PDMS.

[0105] Through this binarization process, the transport analysis unit 117 can reconstruct the spatial distribution of phase separation regions within the three-dimensional structure as a grid map. This makes it possible to quantitatively analyze the continuity and distribution structure of each region, and furthermore, to numerically and spatially identify the permeation pathways of predetermined substances such as oxygen molecules, water molecules, or sodium ions.

[0106] (Step S302: Movement Processing) The transport analysis unit 117 sets up test particles to simulate the diffusion behavior of a predetermined substance (e.g., oxygen molecules, water molecules, sodium ions, etc.) in the three-dimensional structure binarized in the processing of step S301. Here, "test particles" refers to virtual particles that simulate the diffusion behavior of a predetermined substance. The test particles are not actual molecules or ions, but numerical analysis units controlled by a computer, and are permitted to move probabilistically only within a specific region (first region or second region) in the binarized three-dimensional structure.

[0107] The test particles vary depending on the type of substance being evaluated for permeability. For example, when evaluating oxygen permeability, test particles that can only exist within the first region (hydrophobic region) are used, while when evaluating the permeability of sodium ions or water molecules, test particles that can only exist within the second region (hydrophilic region) are used.

[0108] Test particles are moved discretely, for example, based on a random walk method. The transport analysis unit 117 randomly inserts the test particles into the lower end of the three-dimensional lattice space based on the random walk method and moves them probabilistically in one of six directions: upward, downward, left-right, forward, and backward. The movement probabilities can be set, for example, to simulate a concentration gradient or convection, with the probability of upward movement being 2 / 7 and the probability of movement in the other directions being 1 / 7 each. If a test particle attempts to pass between different regions (for example, from the first region to the second region, or vice versa), the transport analysis unit 117 prohibits that movement and only allows diffusion within the same region. This allows for accurate reproduction of the unique permeation paths (hydrophobic or hydrophilic paths) of each substance. The transport analysis unit 117 may also apply specular reflection boundary conditions to the lower end boundary and periodic boundary conditions to the left-right direction. The transport analysis unit 117 continues to execute a diffusion motion based on the random walk method until the test particle reaches the upper boundary.

[0109] (Step S303: Calculation process) Based on the transport analysis unit 117 obtained in step S302, the transport analysis unit 117 calculates the average transport time required for each test particle to reach the upper boundary. Using this average transport time and the distance between the upper and lower ends (i.e., the transport distance), the transport analysis unit 117 calculates the diffusion coefficient D and the permeability index P based on the diffusion coefficient D.

[0110] The diffusion coefficient D is the effective diffusion coefficient calculated by dividing the mean square displacement of the particles by time (for example, mean travel time). The diffusion coefficient D can be calculated using the following formula, where τ is the mean travel time and L is the travel distance: Diffusion coefficient D = L ２ / 2τ

[0111] The permeability index P is a parameter (an index indicating permeability) used to quantitatively evaluate the degree of bend in the diffusion path. A small value of the permeability index P indicates that the path through which the test particle passes is bent and has low continuity, resulting in reduced effective permeability. On the other hand, a value of the permeability index P close to 1 means that the diffusion path has high continuity and that molecules or ions can permeate. Such a permeability index P is determined by the diffusion coefficient D and the diffusion coefficient D ０ Using this, the following formula can be used to calculate the permeability index P = D / D ０

[0112] Here, the diffusion coefficient D ０ This will be explained. Diffusion coefficient D ０ This is a reference coefficient, and is the value obtained when the simulation region is composed of the same type of material and there are no structural obstacles inside. Such a diffusion coefficient D ０ This represents the free diffusion coefficient when a particle diffuses in free space. In other words, the diffusion coefficient D is the free diffusion coefficient D ０ D / D standardized by ０ The permeability index P can be used as a dimensionless index representing the effective diffusion behavior in a phase-separated structure, i.e., as a parameter for quantitatively evaluating the degree of curvature of the diffusion pathway.

[0113] Through the processing in steps S301 to S303, the transport analysis unit 117 can perform simulations that mimic the random diffusion behavior of actual molecules or ions through the three-dimensional structure represented by the coarse-grained model. As a result, the permeation pathways of oxygen molecules, water molecules, or sodium ions in actual silicone hydrogel materials can be reproduced with high accuracy, enabling comparative analysis of permeation properties based on the three-dimensional structure. This makes it possible to predict the permeability of ophthalmic lens materials, especially silicone hydrogel materials, with high accuracy from the design stage.

[0114] In the transport analysis according to Embodiment 3, the random walk method was used as an example of a particle movement algorithm for moving the test particles, but the particle movement algorithm is not limited to this. For example, to analyze the probabilistic and statistical movement behavior, statistical movement analysis methods such as the Monte Carlo method or the Brownian motion model can be used. As another example, an extended random walk model that reflects an external potential or density gradient in the direction of particle movement may be adopted. As yet another example, in order to improve the accuracy of the numerical analysis, the lattice-Boltzmann method or a reaction-diffusion type simulation method may be applied to determine the diffusion path.

[0115] (Simulation Results) Figure 15 shows the simulation results obtained for the weight fraction (wt%) of PDMS. The horizontal axis of Figure 15 represents the weight fraction of PDMS, and the vertical axis represents the permeability index P value (D / D). ０ The inventors performed simulations for three different compositions with varying weight ratios of PDMS and DMAA: 80 wt%:20 wt%, 60 wt%:40 wt%, and 40 wt%:60 wt%, and also performed simulations for a PDMS-only system (100 wt%), and obtained the free diffusion coefficient D from the 100 wt% PDMS system. ０ It was normalized using this method.

[0116] Under all compositional conditions, permeation of test particles was confirmed in both the PDMS and DMAA regions, indicating the formation of a continuous permeation pathway within the phase-separated structure.

[0117] As the weight fraction of PDMS increases, the permeability index P(D / D) in the PDMS region increases. ０ The permeability index P in the DMAA region increased, which is consistent with the increasing trend in oxygen permeability observed in the experiment. On the other hand, the permeability index P in the DMAA region decreased, which is consistent with the decreasing trend in sodium ion permeability observed in the experiment.

[0118] This behavior is thought to be due to the effect of an increase in the volume fraction occupied by the PDMS region as the weight fraction of PDMS increases, in addition to a reduction in the curvature of the transport path. This behavior leads to improved efficiency of the transport path within the PDMS region, resulting in improved overall permeability.

[0119] The transport analysis based on the phase separation structure obtained by DPD simulation accurately reproduces the oxygen and sodium ion transport characteristics observed in experiments, demonstrating that the simulation method according to Embodiment 3 is effective for evaluating the permeability of silicone hydrogel materials.

[0120] In other words, the improvement in oxygen permeability with increasing weight fraction of PDMS and the increase in sodium ion permeability with increasing weight fraction of DMAA suggest that each phase plays a distinctly different functional role in mass transport. This provides evidence that the phase separation structure in silicone hydrogel materials forms specific transport pathways, demonstrating an important feature for next-generation contact lens materials: the ability to independently optimize oxygen permeability and ion permeability.

[0121] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. The phrase "based on" means both "based solely on" and "based on at least."

[0122] [Example of implementation by software] The function of the simulation device 10 (hereinafter referred to as "device") is a program for causing a computer to function as the device, and can be realized by a program for causing a computer to function as each control block of the device (particularly the activation energy acquisition unit 111, the first calculation unit 112, the second calculation unit 113, the reaction probability calculation unit 114, the structural relaxation unit 115, and the structural analysis unit 116).

[0123] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., memory) as hardware for executing the program. By executing the program using this control device and storage device, the functions described in each of the embodiments above are realized.

[0124] The above program may be recorded on one or more computer-readable recording media, not temporary ones. These recording media may or may not be provided by the above device. In the latter case, the program may be supplied to the above device via any wired or wireless transmission medium.

[0125] Furthermore, some or all of the functions of each of the above control blocks can also be realized by logic circuits. For example, an integrated circuit in which logic circuits functioning as each of the above control blocks are formed is also included in the scope of this disclosure. In addition, it is also possible to realize the functions of each of the above control blocks by, for example, a quantum computer.

[0126] Furthermore, each process described in the above embodiments may be performed by AI (Artificial Intelligence). In this case, the AI ​​may operate on the control device described above, or it may operate on another device (for example, an edge computer or a cloud server).

[0127] This disclosure is not limited to the embodiments described above, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of this disclosure.

[0128] [Additional Notes] This disclosure may also be expressed as follows:

[0129] A simulation method according to Embodiment 1 of the present disclosure is a simulation method for an ophthalmic lens material obtained by copolymerizing at least a first component and a second component, comprising: an acquisition step in which a computer obtains information indicating the activation energy relating to the copolymerization; a first calculation step in which a computer calculates the density and solubility parameters of a first coarse-grained model and a second coarse-grained model obtained by coarsing the first component and the second component, respectively; and, under preset mixing ratio conditions of the first component and the second component, the simulation method calculates the first coarse-grained model and the second coarse-grained model based on the density, the solubility parameter, and the mixing ratio. The method includes: a second calculation step of calculating a repulsive force parameter that indicates the repulsive force acting between the models in each combination of the models; a third calculation step of calculating the reaction probability between the models in each combination of the first coarse-grained model and the second coarse-grained model using the repulsive force parameter and the activation energy; a structural relaxation step of causing the models in each combination of the first coarse-grained model and the second coarse-grained model to react and relax based on the reaction probability; and a structural analysis step of analyzing the three-dimensional structure of the first coarse-grained model and the second coarse-grained model formed by the structural relaxation.

[0130] The simulation method according to aspect 2 of the present disclosure may further include, in aspect 1 above, a transport analysis step in which the computer calculates an index indicating the permeability of a predetermined substance within the three-dimensional structure obtained in the structural analysis step.

[0131] The simulation method according to aspect 3 of the present disclosure, in aspect 2 described above, may involve the computer, in the transport analysis step, dividing the three-dimensional structure into a plurality of grid-like regions, binarizing each grid-like region into either a first region where the first component is dominant or a second region where the second component is dominant, executing a particle movement algorithm to virtually move test particles corresponding to the predetermined substance within either the first or second region, calculating the diffusion coefficient of the predetermined substance in the three-dimensional structure based on the movement time and distance of the test particles, and calculating a permeability index, which is an indicator of permeability, based on the diffusion coefficient.

[0132] The simulation method according to aspect 4 of the present disclosure, in aspect 3 above, wherein the particle movement algorithm includes at least a random walk method, and the predetermined substance may include at least one of oxygen molecules, water molecules, or sodium ions.

[0133] The simulation method according to aspect 5 of the present disclosure, in any of aspects 1 to 4 above, includes a combination of the first coarse-grained model and the second coarse-grained model, a combination of the second coarse-grained model and the second coarse-grained model, and a combination of the first coarse-grained model and the second coarse-grained model, wherein the computer calculates the density and solubility parameters based on the all-atom molecular dynamics method in the first calculation step, calculates the repulsion parameter based on the dissipation particle dynamics method in the second calculation step, and calculates the probability, as the reaction probability, that models in each combination react with each other if the distance between the models is within a preset reaction determination distance.

[0134] In any of the above embodiments 1 to 5, the simulation method according to embodiment 6 of the present disclosure may include, in any of embodiments 1 to 5, a first activation energy between the first component and the first component, a second activation energy between the second component and the second component, and a third activation energy between the first component and the second component.

[0135] In the simulation method according to aspect 7 of the present disclosure, in any of aspects 1 to 6 described above, the computer may, in the structural relaxation step, react the models in each combination of the first coarse-grained model and the second coarse-grained model to relax the structure until the reaction rate of each combination of the first coarse-grained model and the second coarse-grained model exceeds a predetermined value.

[0136] In the simulation method according to aspect 8 of the present disclosure, in any of aspects 1 to 7 above, the first component may be a silicone-containing component and the second component may be a hydrophilic component.

[0137] In the simulation method according to aspect 9 of this disclosure, the three-dimensional structure may be a phase-separated structure in any of the above aspects 1 to 8.

[0138] A simulation method according to aspect 10 of the present disclosure, wherein in aspect 8 above, the ophthalmic lens material is a material produced by copolymerizing the silicone-containing component, the hydrophilic component, and a third component which is a hydrophobic component, and the computer calculates the density and solubility parameters of a third coarse-grained model in which the third component is coarse-grained in the first calculation step, and in the second calculation step, under preset mixing ratio conditions of the first component, the second component, and the third component, the density, the solubility parameter, and the repulsive force parameter used in the dissipative particle dynamics method based on the mixing ratio The meter may be calculated, and in the third calculation step, the reaction probability between the models in each combination of the first coarse-grained model, the second coarse-grained model, and the third coarse-grained model may be calculated using the repulsion parameter and the activation energy, in the structural relaxation step, the models in each combination of the first coarse-grained model, the second coarse-grained model, and the third coarse-grained model may be reacted to relax the structure based on the reaction probability, and in the structural analysis step, the three-dimensional structure of the first coarse-grained model, the second coarse-grained model, and the third coarse-grained model may be analyzed.

[0139] In the simulation method according to aspect 11 of this disclosure, in any of aspects 1 to 10 above, the reaction probability may be defined by the above formula (11). In the above formula (11), p: reaction probability A: collision factor E ａ : Activation energy R: Gas constant T: Local temperature of the reaction site.

[0140] The simulation program according to aspect 12 of this disclosure may be a program that causes a computer to execute the simulation method in any of the above aspects 1 to 11.

[0141] An ophthalmic lens according to aspect 13 of this disclosure may be an ophthalmic lens comprising the ophthalmic lens material formulated using the formulation ratio obtained by the simulation method in any of the above aspects 1 to 11.

[0142] 1. Eye lens 2. Phase separation structure 10. Simulation device 20, 24, 27. Coarse-grained model 111. Activation energy acquisition unit 112. First calculation unit 113. Second calculation unit 114. Reaction probability calculation unit 115. Structural relaxation unit 116. Structural analysis unit 117. Transport analysis unit

Claims

1. A simulation method for an ophthalmic lens material obtained by copolymerizing at least a first component and a second component, comprising: an acquisition step in which a computer acquires information indicating the activation energy related to the copolymerization; a first calculation step in which a computer calculates the density and solubility parameters of a first coarse-grained model and a second coarse-grained model obtained by coarse-graining the first component and the second component, respectively; a second calculation step in which, based on the density, the solubility parameter, and the mixing ratio, a repulsive force parameter indicating the repulsive force acting between the models of each combination of the first coarse-grained model and the second coarse-grained model under preset mixing ratio conditions of the first component and the second component; a third calculation step in which the reaction probability between the models in each combination of the first coarse-grained model and the second coarse-grained model is calculated using the repulsive force parameter and the activation energy; and a structural relaxation step in which the models in each combination of the first coarse-grained model and the second coarse-grained model are reacted to relax the structure based on the reaction probability. A simulation method comprising: a structural analysis step of analyzing the three-dimensional structures of the first coarse-grained model and the second coarse-grained model formed by the structural relaxation; 2. The simulation method according to claim 1, further comprising a transport analysis step in which the computer calculates an index indicating the permeability of a predetermined substance within the three-dimensional structure obtained in the structural analysis step.

3. The simulation method according to claim 2, wherein the computer, in the transport analysis step, divides the three-dimensional structure into a plurality of grid-like regions, binarizes each grid-like region into either a first region where the first component is dominant or a second region where the second component is dominant, executes a particle movement algorithm to virtually move test particles corresponding to the predetermined substance within either the first region or the second region, calculates the diffusion coefficient of the predetermined substance in the three-dimensional structure based on the movement time and distance of the test particles, and calculates a permeability index, which is an indicator of permeability, based on the diffusion coefficient.

4. The simulation method according to claim 3, wherein the particle movement algorithm includes at least a random walk method, and the predetermined substance includes at least one of oxygen molecules, water molecules, or sodium ions.

5. The simulation method according to any one of claims 1 to 4, wherein each combination of the first coarse-grained model and the second coarse-grained model includes a combination of the first coarse-grained model and the first coarse-grained model, a combination of the second coarse-grained model and the second coarse-grained model, and a combination of the first coarse-grained model and the second coarse-grained model, wherein the computer calculates the density and solubility parameters based on the all-atom molecular dynamics method in the first calculation step, calculates the repulsion parameter based on the dissipation particle dynamics method in the second calculation step, and calculates the probability that models in each combination react with each other if the distance between the models is within a predetermined reaction determination distance, as the reaction probability.

6. The simulation method according to any one of claims 1 to 5, wherein the activation energy includes a first activation energy between the first component and the first component, a second activation energy between the second component and the second component, and a third activation energy between the first component and the second component.

7. The simulation method according to any one of claims 1 to 6, wherein the computer, in the structural relaxation step, causes the models in each combination of the first coarse-grained model and the second coarse-grained model to react with each other to relax the structure until the reaction rate of each combination of the first coarse-grained model and the second coarse-grained model exceeds a predetermined value.

8. The simulation method according to any one of claims 1 to 7, wherein the first component is a silicone-containing component and the second component is a hydrophilic component.

9. The simulation method according to any one of claims 1 to 8, wherein the three-dimensional structure is a phase-separated structure.

10. The ophthalmic lens material is a material produced by copolymerizing the silicone-containing component, the hydrophilic component, and a third component which is a hydrophobic component, wherein the computer, in the first calculation step, calculates the density and solubility parameters of a third coarse-grained model in which the third component is coarse-grained; in the second calculation step, calculates the repulsive force parameter used in the dissipative particle dynamics method based on the density, the solubility parameter, and the mixing ratio under preset mixing ratio conditions of the first component, the second component, and the third component; in the third calculation step, calculates the reaction probability between the models in each combination of the first coarse-grained model, the second coarse-grained model, and the third coarse-grained model using the repulsive force parameter and the activation energy; and in the structural relaxation step, reacts the models in each combination of the first coarse-grained model, the second coarse-grained model, and the third coarse-grained model to relax the structure based on the reaction probability. The simulation method according to claim 8, wherein the three-dimensional structures of the first coarse-grained model, the second coarse-grained model, and the third coarse-grained model are analyzed in the structural analysis step.

11. The simulation method according to any one of claims 1 to 10, wherein the reaction probability is defined by the following formula (1). Here, p: reaction probability, A: collision factor, E ａ : Activation energy R: Gas constant T: Local temperature of the reaction site 12. A simulation program for causing a computer to execute the simulation method described in any one of claims 1 to 11.

13. An ophthalmic lens comprising the ophthalmic lens material formulated using a formulation ratio obtained by the simulation method described in any one of claims 1 to 11.

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