Patent Description:
In recent years, machine learning has been used to predict physical properties of organic compounds such as polymers by using a computer.

Conventional techniques for predicting physical properties of polymers use various structure descriptors configured by humans to predict physical properties. Techniques for mechanically identifying substructure patterns of molecules and calculating structure descriptors based on algorithms have also been proposed. <CIT> discloses relevant background art for estimating the property for a multicomponent chemical composition using regression models.

Conventionally, it is necessary for a human to determine a structural pattern, such as the number of carbon atoms, that serves as a structure descriptor of a polymer in advance. With respect to this, there is a method for automatically extracting substructures of low-molecular weight organic molecules, but there is a problem that the accuracy of predicting the physical properties of a polymer is low. Thus, the method has not been applied to polymer structures.

Therefore, according to one aspect, it is an object to accurately predict a physical property of a polymer.

According to one aspect, a polymer physical property prediction device including a substructure number calculating unit configured to read a structural unit from a storage unit and use the structural unit to calculate numbers each indicating how many substructures are in a polymer, the polymer being formed of repetition of the structural unit, an atom calculating unit configured to calculate a number indicating how many atoms are in the structural unit, a substructure number density calculating unit configured to calculate number densities of the substructures from the numbers of substructures and the number of atoms in the structural unit, a regression model construction unit configured to construct a regression model that predicts a physical property value by using an experimental value of a physical property of the polymer and the number densities of the substructures, the experimental value being obtained from the storage unit, a polymer structure input unit configured to input a polymer structure of which the physical property value is to be predicted, and a polymer physical property prediction unit configured to predict the physical property value corresponding to the input polymer structure by using the regression model, is provided.

A means to solve the above-described problem can be a program or a method for predicting the physical properties of a polymer.

The physical properties of a polymer can be accurately predicted.

In the following, an embodiment of the present invention will be described based on the drawings. First, an example of a system configuration according to the present embodiment will be described. <FIG> is a drawing illustrating the example of the system configuration according to the present embodiment In <FIG>, a system <NUM> includes a physical property prediction device <NUM> and multiple terminals <NUM>, and each of the terminals can be connected through a network <NUM>.

The terminal <NUM> is an information processing terminal used by a user, is connected to the physical property prediction device <NUM>, and utilizes processing performed by a prediction processing unit <NUM>.

The physical property prediction device <NUM> is an information processing device including the prediction processing unit <NUM> that predicts a physical property of a polymer. In the physical property prediction device <NUM>, the prediction processing unit <NUM> performs machine learning using a structure of the polymer and data indicating the physical property of the polymer that are provided in advance to generate a regression model. In response to receiving a polymer structure 6a from the terminal <NUM>, a physical property value is predicted using the generated regression model with respect to the received polymer structure 6a, and a predicted physical property value 6b is sent to the terminal <NUM>.

A configuration of the system <NUM> is not limited to the example of <FIG>. As an example, the physical property prediction device <NUM> may be utilized in a stand-alone manner. In this case, when the polymer structure 6a is input by a user operation of an input device <NUM> (<FIG>), the physical property value 6b is output to a display device <NUM> (<FIG>).

The physical property prediction device <NUM> according to the present embodiment has a hardware configuration as illustrated in <FIG> is a drawing illustrating an example of the hardware configuration of the physical property prediction device. In <FIG>, the physical property prediction device <NUM> is an information processing device controlled by a computer and includes a central processing unit (CPU) <NUM>, a main storage device <NUM>, an auxiliary storage device <NUM>, the input device <NUM>, the display device <NUM>, a communication interface (I/F) <NUM>, and a drive device <NUM>, which are connected to a bus B.

The CPU <NUM> corresponds to a processor that controls the physical property prediction device <NUM> according to a program stored in the main storage device <NUM>. As the main storage device <NUM>, a random access memory (RAM) and a read only memory (ROM) are used to store or temporarily store a program executed by the CPU <NUM>, data necessary for processing by the CPU <NUM>, and data obtained by processing by the CPU <NUM>, for example.

A hard disk drive (HDD) is used for the auxiliary storage device <NUM>, for example, to store data such as a program for executing various processing. A portion of the program stored in the auxiliary storage device <NUM> is loaded into the main storage device <NUM> and is executed by the CPU <NUM> to achieve various processing. Hereinafter, the main storage device <NUM>, the auxiliary storage device <NUM>, and an external storage device accessible by the physical property prediction device <NUM> will be collectively referred to as a storage unit <NUM>.

The input device <NUM> includes a mouse and a keyboard and is used by a user to input various information required for processing by the physical property prediction device <NUM>. The display device <NUM> displays various necessary information under control of the CPU <NUM>. The input device <NUM> and the display device <NUM> may be a user interface, such as an integrated touch panel. The communication I/F <NUM> performs communication through a network, such as a wired or wireless network. The communication by the communication I/F <NUM> is not limited to the wired or wireless communication. The drive device <NUM> interfaces a recording medium <NUM> set in the drive device <NUM> (e.g., a compact disc read-only memory (CD-ROM)) with the physical property prediction device <NUM>.

A program for implementing the processing performed by the physical property prediction device <NUM> is provided to the physical property prediction device <NUM> through the recording medium <NUM>, such as a CD-ROM. A program for implementing various processing according to the present embodiment, which will be described later, is stored in the recording medium <NUM>, and the program stored in the recording medium <NUM> is installed in the physical property prediction device <NUM> through the drive device <NUM>. The installed program can be executed by the physical property prediction device <NUM>.

The recording medium <NUM> for storing the program is not limited to a CD-ROM, but may be one or more non-transitory and tangible media having a computer readable data structure. As a computer readable recording medium in addition to a CD-ROM, a portable storage medium, such as a digital versatile disk (DVD) and a USB memory, or a semiconductor memory, such as a flash memory, may be used.

<FIG> is a drawing illustrating a first functional configuration example of the physical property prediction device. In <FIG>, the physical property prediction device <NUM> includes the prediction processing unit <NUM>. The prediction processing unit <NUM> corresponds to a prediction process performed by the CPU <NUM> executing a corresponding program.

The storage unit <NUM> stores a polymer structure and physical property DB <NUM>, a model structure 33a, the number of substructures 33b, the number of atoms 33c, a substructure number density 33d, an important substructure 33e, a regression model 33f, a polymer structure 6a, a physical property value 6b, for example.

The prediction processing unit <NUM> obtains the regression model 33f for predicting the physical properties of the polymer structure 6a specified by a user by performing machine learning, predicts the physical properties of the polymer structure 6a by using the obtained regression model 33f, and outputs the obtained physical property value 6b. The prediction processing unit <NUM> includes a model generator <NUM> and a physical property prediction unit <NUM> as processing units.

The model generator <NUM> learns the physical property of the polymer structure in which a structural unit is repeatedly replicated and linked using the polymer structure and physical property DB31 and determines the regression model 33f. A structural unit forming a polymer by repetition is called a "repeating unit". In the polymer structure and physical property DB <NUM>, a repeating unit and an experimental value of a physical property experimentally obtained in advance are stored in association with each polymer.

The model generator <NUM> includes a model structure generator 43a, a substructure number calculating unit 43b, an atom calculating unit 43c, a substructure number density calculating unit 43d, a dimension reduction unit 43e, and a regression model construction unit 43f as processing units. Either or both of the model structure generator 43a and the dimension reduction unit 43e can be removed.

The model structure generator 43a obtains the repeating unit from the polymer structure and physical property DB <NUM> and repeats the process of replicating and linking the obtained repeating unit a predetermined number of times to generate the model structure 33a. The repeating unit includes a substructure. The repeating unit may also be an entire of a substructure. The repeating unit are linked multiple times to represent a polymer.

The generated model structure 33a is stored in the storage unit <NUM>. The repeating unit and the model structure 33a may be represented in a predetermined form. The first functional configuration example describes the repeating unit and the model structure 33a in the simplified molecular input line entry syntax (SMILES) notation, but the notation is not limited to this.

The substructure number calculating unit 43b calculates the number of substructures 33b based on the model structure 33a. The numbers of substructures 33b are represented by an n-dimensional vector and stored in the storage <NUM>. The atom calculating unit 43c calculates the number of atoms 33c based on the model structure 33a. The obtained number of atoms 33c is stored in the storage unit <NUM>.

The substructure number density calculating unit 43d calculates the substructure number density 33d by using the numbers of substructures 33b and the number of atoms 33c. The substructure number density 33d can be obtained by the following equation: <MAT> The obtained substructure number densities 33d are represented by a vector of the same n-dimensional vector as the vector of the substructure number 33b and are stored in the storage unit <NUM>.

The dimension reduction unit 43e constructs a temporal regression model by using the substructure number density 33d and the experimental value, and reduces the dimensions of the temporal regression model based on a predetermined dimension reduction method to obtain the important substructure 33e. The important substructure 33e is stored in the storage unit <NUM>. The important substructure 33e is data specifying an element (e.g., a vector) that will contribute to the prediction accuracy of the regression model 33f to be constructed in the substructure number density 33d.

For the dimension reduction method, a procedure corresponding to a regression analysis method to be used is determined in advance. For example, when the lasso regression is used, the dimensions can be reduced by removing a coefficient term of <NUM>. When the random forest regression is used, the dimensions can be reduced by calculating the importance of explanatory variables and excluding variables that have the importance lower than or equal to a threshold. In the stepwise method, the dimensions can be reduced by searching for combinations of explanatory variables having highest prediction accuracy by adding or reducing an explanatory variable one by one when a temporal regression model is constructed.

The regression model construction unit 43f constructs the regression model 33f by performing a regression analysis based on the important substructure 33e obtained by the dimension reduction unit 43e by using the substructure number density 33d and the experimental value. The regression model 33f is stored in the storage unit <NUM>.

In the dimension reduction unit 43e and the regression model construction unit 43f, the experimental value can be obtained from the polymer structure and physical property DB <NUM>. The regression model 33f obtained by the regression model construction unit 43f is expressed in the following equation. <MAT> By substituting the substructure number density 33d for a variable x, a predicted value y of the physical property is obtained.

To the regression analysis for obtaining the temporal regression model and the regression model 33f, any one of Lasso regression, random forest regression, a stepwise method, partial linear regression, ridge regression, kernel ridge regression, support vector regression, decision tree regression, Gaussian process regression, and so on can be applied. As an example, a temporal regression model can be obtained by the Lasso regression, and the regression model 33f can be obtained by the Gaussian process regression.

Next, the physical property prediction unit <NUM> predicts the physical properties of the polymer based on the polymer structure 6a by using the regression model 33f in response to an input of the polymer structure 6a specified by a user. The polymer structure 6a may specify the repeating unit by the SMILES notation or the like. The physical property prediction unit <NUM> includes a polymer structure input unit 47a and a polymer physical property prediction unit 47b as processing units.

The polymer structure input unit 47a inputs the polymer structure 6a specified by a user and the polymer structure 6a is stored in the storage unit <NUM>. The polymer structure input unit 47a may input the polymer structure 6a by the communication I/F <NUM> receiving the polymer structure 6a through the network <NUM> or may input the polymer structure 6a in accordance with a user's specification by the input device <NUM> of the property prediction device <NUM>.

The polymer physical property prediction unit 47b obtains the model structure, the numbers of substructures, and the number of atoms, from the polymer structure 6a, to calculate the substructure number density, and predicts the physical property value by using the regression model 33f stored in the storage unit <NUM>. The method of obtaining the model structure, the numbers of substructures, the number of atoms, and the substructure number density based on the polymer structure 6a is similar to the method of the model generator <NUM>.

The predicted physical property value 6b is output to the storage unit <NUM>. When the polymer structure 6a is specified through the network <NUM>, the obtained physical property value 6b is sent to the terminal <NUM> of the user and displayed. When the polymer structure 6a is specified by a user operation at the physical property prediction device <NUM>, the obtained physical property value 6b is displayed on the display device <NUM>.

<FIG> is a drawing illustrating an example of a data configuration of the polymer structure and physical property DB. In <FIG>, the polymer structure and physical property DB <NUM> is a database that manages the repeating unit and experimental values of various physical properties, such as a refractive index, for each polymer, and includes items such as a polymer name, the repeating unit, the refractive index, and a glass transition point.

The polymer name indicates specific information specifying the polymer structure. An example in which the repeating unit is represented by the SMILES notation is used for the following description, but the notation of the repeating unit is not limited to the SMILES notation. The refractive index, the glass transition point, and the like are examples of various physical properties of the polymer experimentally obtained in advance, and the experimental values are shown for each name of the physical property.

In this example, a repeating unit "*CC*" is associated with a polymer name "poly(ethylene)" and the experimental values, such as the refractive index "<NUM>", are shown. Similarly, an experimental value of each physical property is shown for another polymer.

In the model generator <NUM>, referenced data, generated data, and the like may be managed in a work table <NUM>. The work table <NUM> is a table in which calculated values are stored for each repeating unit of the polymer of which the substructure number density 33d is obtained, and includes items such as the polymer name, the repeating unit, the number of atoms, and the substructure number density.

The polymer name indicates a polymer name registered in the polymer structure and physical property DB <NUM>. The number of substructures indicates the number of substructures 33b obtained by the substructure number calculating unit 43b. The number of atoms indicates the number of atoms 33c obtained by the atom calculating unit 43c. The substructure number density indicates the substructure number density 33d obtained by the substructure number density calculating unit 43d.

The substructure number density of the work table <NUM> and the experimental values of the physical property of the polymer structure and physical property DB <NUM> are used for the machine learning by the regression analysis. Thus, the work table <NUM> may be associated with the polymer structure and physical property DB <NUM> by the polymer name. Here, the data configuration example of the polymer structure and physical property DB <NUM> and the work table <NUM> illustrated in <FIG> is only one example, but is not limited to this example.

An outline of an entire prediction process performed by the prediction processing unit <NUM> will be described using an example of the repeating unit. <FIG> is a drawing for describing the overview of the prediction process according to the present embodiment. As illustrated in <FIG>, in the model generator <NUM> of the prediction processing unit <NUM>, the model structure generator 43a obtains the repeating unit from the polymer structure and physical property DB <NUM> (as described in (a) of <FIG>). For example, a repeating unit "*CCO*" is obtained. In the following description, "*" indicating a start and an end of a certain structure in the SMILES notation will be omitted.

The model structure generator 43a uses the obtained substructure as a repeating unit, and replicates the repeating unit to generate the model structure 33a (as described in (b) of <FIG>). In this example, the model structure generator 43a repeats replicating the repeating unit "CCO" to the right a predetermined number of times, to generate the model structure 33a "CCOCOCOCCCOCCCOCCCOCCCOCCCOCCO", in which the repeating unit "CCO" are linked multiple times, in the memory unit <NUM>.

The substructure number calculating unit 43b generates a substructure number vector x<NUM> (i.e., the numbers of substructures 33b) of the structure model 33a (as described in (c) in <FIG>). The substructure number vector x<NUM> is represented in n dimensions by a structure descriptor, such as extended circular finger print (ECFP), atom pair, topological torsion, and neural finger print. In the first functional configuration example, a structure descriptor represented by the number of C atoms, the number of O atoms, the number of COCs, or the like is used for the description. However, the structure descriptor is not limited to this.

In the structure model "CCOCCCOCCCOCCCOCCCOCCCOCCO", an n-dimensional substructure number vector x<NUM>, which consists of the number of C atoms "<NUM>", the number of O atoms "<NUM>",. , and the number of COCs "<NUM>", is generated. A structure assigned to each vector, such as "C", "O", "COC", indicates the type of the substructure of the polymer. Here, n is an arbitrary natural number, and is usually about <NUM> to <NUM>.

The atom calculating unit 43c calculates the number of atoms included in the model structure 33a (as described in (d) of <FIG>). In the structure model "CCOCOCOCOCCCOCCCOCCCOCCCO", the number of atoms is "<NUM>".

The substructure number density calculating unit 43d divides the substructure number vector x<NUM> (i.e., the numbers of substructures 33b) obtained by the substructure number calculating unit 43b in (b) of <FIG> by the number of atoms 33c obtained by the atom calculating unit 43c in (c) of <FIG> to generate the substructure number density vector x (i.e., the substructure number densities 33d) in (e) of <FIG>.

Specifically, in the structure model "CCOCCCOCCOCCOCCCOCCCOCCCOCCCOCCO", each value of the substructure number vector x<NUM> is divided by the number of atoms "<NUM>" to obtain the substructure number density vector x representing the C atom number density "<NUM>/<NUM> (= <NUM>/<NUM>)", the O atom number density "<NUM>/<NUM> (= <NUM>/<NUM>)",. , and the COC density "<NUM>/<NUM>. " The number of dimensions of the substructure number density vector x is n, which is the same number as the number of dimensions of the substructure number vector x<NUM>.

For each record of the polymer structure and physical property DB <NUM>, (a) of <FIG> to (e) of <FIG> are repeated. The number of substructures 33b, the number of atoms 33c, and the substructure number density 33d that are obtained may be stored in the work table <NUM> in association with the polymer name.

Before constructing the regression model 33f, a temporal regression model is constructed by the dimension reduction unit 43e and the dimension reduction is performed (in (f) of <FIG>). This can reduce n dimensions to n' dimensions (n' < n). By the dimension reduction, the important substructure 33e that contributes to the prediction accuracy is determined. Here, n' is a natural number smaller than n, and is usually about <NUM> to <NUM>.

The regression model construction unit 43f constructs the regression model 33f used in predicting the physical property value based on the important substructure 33e (in (g) of <FIG>). The regression model 33f, whose dimensions are reduced to several tens dimensions, is obtained. The regression model 33f is expressed by the following equation (<NUM>) that obtains the predicted value y of the physical property of the polymer by the substructure number density vector x (i.e., the substructure number densities 33d). <MAT> When dimensions are not reduced, the number of sub-structure number density vector x is n, which is the number of dimensions. But after dimensions are reduced, the dimensions of the substructure number density vector x are n'. Because the predicted values are calculated using only the dimensions contributing to the prediction accuracy by using the important substructure 33e, the physical property prediction process can be efficiently performed.

The above-described process from (a) to (e) in <FIG> corresponds to the model generating process of the model generator <NUM>. After the model generating process has been performed, in response to receiving the input of the polymer structure 6a desired to be processed from the user, the physical property prediction process is performed by the physical property prediction unit <NUM>.

In response to the input of the polymer structure A of which the physical property is to be obtained, the physical property prediction unit <NUM> generates the substructure number density vector xA, and predicts the physical property value yA (in (h) of <FIG>). The polymer structure input unit 47a inputs the polymer structure A, and the polymer physical property prediction unit 47b predicts the physical property value yA by using the regression model 33f.

The physical property value yA with respect to the polymer structure A desired to be processed can be obtained by substituting the substructure number density xA in the above equation (<NUM>).

If the regression model 33f is for only one physical property, the value of a predetermined physical property is predicted. If the regression model 33f is obtained with respect to each of the various physical properties, the user may specify the polymer structure A and all physical properties or a specific physical property. In this case, the polymer physical property prediction unit 47b predicts the physical property value 6b by using the regression model 33f corresponding to each physical property and the important substructure 33e.

The process of calculating the numbers of substructures 33b in (c) of <FIG> and the process of calculating the number of atoms in (d) of <FIG> may be performed in any order. After calculating the number of atoms 33c is performed, the numbers of substructures 33b may be calculated, or the number of atoms 33c and the numbers of substructures 33b may be calculated in parallel. In the present embodiment, a "predicted value" of the physical property and a predicted "physical property value" are identical.

In the first functional configuration example, the substructure number density calculating unit 43d of the prediction processing unit <NUM> can recognize whether polymer structures are identical. <FIG> is a drawing illustrating an example of the number density. In <FIG>, the number of carbons of a polyethylene oxide is illustrated as an example.

Referring to <FIG>, when a structure A and a structure B are compared, a notation of the structure B is longer and different from a notation of the structure A. However, polymers represented by the structure A and the structure B are identical.

In an existing technique in which the number of substructures is used as a structure descriptor, the number of carbons of the structure A is "<NUM>," the number of carbons of the structure B is "<NUM>". Since the numbers of carbons of the structure A and the structure B are different, the polymers are mistakenly recognized as being different. This example indicates that when polymers having different lengths of the repeating unit are compared, the number of substructures cannot appropriately capture structural features of the polymer.

In the first functional configuration example, the substructure number density calculating unit 43d obtains the same substructure number density (carbon number density) "<NUM>/<NUM>" with respect to the structure A and the structure B. Thus, the dimension reduction unit 43e and the regression model construction unit 43f can correctly recognize that the structure A and the structure B represent the same polymer. Based on these examples, the inventors have found that by using the substructure number density 33d, polymers having different lengths of the repeating unit can be treated equivalently.

The inventors have also found that replicating and linking the repeating unit can improve the existing technique having low prediction accuracy. A difference between when replication is performed and when no replication is performed is described in <FIG> is a drawing for describing the difference between when replication of the repeating unit is performed and when no replication is performed. In <FIG>, a polyethylene oxide is used as an example. A repeating unit when replication is performed and a repeating unit when no replication is performed are identical.

When the repeating unit is not replicated, there is no substructure "COC", which is expected to exist, at both ends of the repeating unit. Thus, COC number density is "<NUM>". With respect to this, in the first functional configuration example, the repeating unit is replicated and the right side of the repeating unit is linked to an oxygen atom on the left side of the next repeating unit. Two repeating units causes the substructure "COC" to appear and "<NUM>/<NUM>" is obtained with respect to the number density of "COC", which is expected to exist. In the first functional configuration example, the regression model 33f is obtained using the substructure number density 33d obtained in such a manner, thereby improving the prediction accuracy of the existing technique.

As described above, the prediction process is performed by the prediction processing unit <NUM> as illustrated in <FIG> and <FIG>. <FIG> and <FIG> are flowcharts for describing the prediction process. First, a model generating process P51 is performed by the model generator <NUM>.

In step S511, in the model generator <NUM>, the model structure generator 43a selects and reads one record from the polymer structure and physical property DB <NUM>, and obtains the repeating unit and the experimental value. In step S512, the model structure generator 43a repeats an operation of replicating and linking the obtained repeating unit a predetermined number of times to generate the model structure 33a.

In step S513, the substructure number calculating unit 43b calculates the number of substructures 33b of the model structure 33a by using a predetermined structure descriptor (e.g., ECFP). In step S514, the atom calculating unit 43c calculates the number of atoms 33c of the model structure 33a. Step S513 may be performed after step S514 has been performed. Alternatively, steps S513 and S514 may be performed in parallel.

In step S515, the substructure number density calculating unit 43d calculates the substructure number density 33d by dividing the number of substructures 33b by the number of atoms 33c. As described above, the numbers of substructures 33b and the substructure number densities 33d are represented by an n-dimensional vector.

In step S516, the model generator <NUM> determines whether all records of the polymer structure and the physical property DB <NUM> have been completed. When all records have not been completed (NO in step S516), the model generator <NUM> returns to step S511 and performs a process similar to the process described above.

When all records have been completed (YES in step S516), the dimension reduction unit 43e performs a first regression analysis for obtaining a temporal regression model f' by using the substructure number density 33d corresponding to the important substructure 33e as a variable x' and an experimental value as y'. In step S517, the dimension reduction unit 43e reduces the dimensions of the temporal regression model f' by a predetermined dimension reduction method and obtains the important substructure 33e.

Next, the regression model construction unit 43f performs a second regression analysis. In step S518, the regression model construction unit 43f obtains the regression model f by using the substructure number density 33d corresponding to the important substructure 33e as the variable x and the experimental value as y. The experimental value y is a value determined by the polymer name and the physical property in the polymer structure and physical property DB <NUM>. The regression model f, that is, the regression model 33f, is stored in the storage unit <NUM>.

When the regression model f is obtained for all or some of the physical properties registered in the polymer structure and physical property DB <NUM>, an experimental value may be obtained for each physical property, and steps S517 to S518 may be repeated.

Subsequently, the physical property prediction process P55 is performed by the physical property prediction unit <NUM>. When the polymer structure input unit 47a receives the polymer structure 6a in the prediction processing unit <NUM> (step S521), the model structure, the number of substructures, the number of atoms, and the substructure number density are obtained using the polymer structure 6a as the repeating unit (step S522). The model structure, the number of substructures, the number of atoms, and the substructure number density of the polymer structure 6a are obtained by performing a process similar to the process from step S512 to step S515 in the model generating process P51.

In step S524, the polymer physical property prediction unit 47b predicts the physical property value 6b based on the important substructure 33e obtained in step S517 by using the regression model f (i.e., the regression model 33f stored in the storage unit <NUM>) obtained in step S518 by reducing the dimensions of the substructure number density of the polymer structure 6a. The obtained physical property value 6b is sent to the terminal <NUM> or displayed on the display device <NUM>.

When the regression model 33f corresponding to each of multiple physical properties is generated, it is desirable to manage the generated regression models 33f in a table as illustrated in <FIG> is a drawing illustrating an example of a data configuration of a regression model management table.

In <FIG>, a regression model management table <NUM> is a table for managing the regression model 33f for each physical property, and includes items such as the physical property, the regression model, and the important substructure. The physical property represents a name for specifying the physical property, such as the refractive index, and the glass transition point. The regression model represents the regression model 33f corresponding to the physical property, constructed in the regression model construction unit 43f.

The important substructure specifies a valid vector of the vector of the substructure number density 33d, obtained after dimension reduction (i.e., a valid vector after dimension reduction) for using the regression model 33f. When the dimension reduction is omitted, the item of the important substructure is omitted.

In the physical property prediction unit <NUM>, the polymer physical property prediction unit 47b obtains the regression model 33f and the important substructure 33e that are corresponding to the physical property to be predicted from the regression model management table <NUM>.

The inventors have verified the prediction accuracy of various functional configurations described below. According to verification results, the first functional configuration example described above can improve the prediction accuracy the most. Further, the inventors have verified that the prediction accuracy can be sufficiently improved even in a configuration in which either or both of the model structure generator 43a and the dimension reduction unit 43e are removed.

First, other examples of the functional configuration in which the prediction accuracy is verified will be described below. In the following description, a second functional configuration example is a functional configuration in which both of the model structure generator 43a and the dimension reduction unit 43e are removed, a third functional configuration example is a functional configuration in which the model structure generator 43a is removed, and a fourth functional configuration example is a functional configuration in which the dimension reduction unit 43e is removed. In the second functional configuration example to the fourth functional configuration example, processing units similar to the processing units of the first functional configuration example illustrated in <FIG> are referenced by the same reference numerals, and the detailed description is omitted.

<FIG> is a drawing illustrating the second functional configuration example of the physical property prediction device. In <FIG>, in the second functional configuration example, the model structure generator 43a and the dimension reduction unit 43e are removed from the first functional configuration example illustrated in <FIG>. Thus, the model structure 33a and the important substructure 33e are not generated.

In the model generator <NUM>, the substructure number calculating unit 43b and the atom calculating unit 43c obtains the number of substructures 33b and the number of atoms 33c by obtaining the repeating unit from the polymer structure and physical property DB <NUM>.

Also in the second functional configuration example, the substructure number density calculating unit 43d calculates the substructure number density 33d. Then, the regression model construction unit 43f obtains the regression model 33f by using the calculated substructure number density 33d and the experimental value of the physical property.

In the physical property prediction unit <NUM>, when the polymer structure input unit 47a inputs the polymer structure 6a, the polymer physical property prediction unit 47b calculates the number of substructures and the number of atoms by using the polymer structure 6a without creating the model structure 33a. The polymer physical property prediction unit 47b obtains the substructure number density by dividing the obtained number of substructures by the number of atoms, and uses the substructure number density 33d to obtain the property value 6b by the regression model 33f.

In the second functional configuration example, the regression analysis is performed only once. The number of dimensions of the regression model 33f is the same number as the number of dimensions of the substructure number densities 33d, and the important substructure 33e is not determined. However, if the values of the substructure number density 33d are identical, even polymers with different lengths of the repeating unit can be treated equivalently, and the prediction accuracy can be sufficiently improved.

<FIG> is a drawing illustrating the third functional configuration example of the physical property prediction device. In <FIG>, in the third functional configuration example, the model structure generator 43a is removed from the first functional configuration example illustrated in <FIG>. Thus, the model structure 33a is not generated.

In a manner similar to the second functional configuration example, in the model generator <NUM>, the substructure number calculating unit 43b and the atom calculating unit 43c obtain the repeating unit from the polymer structure and physical property DB <NUM> to obtain the number of substructures 33b and the number of atoms 33c. The substructure number density calculating unit 43d obtains the substructure number density 33d. Then, the dimension reduction unit 43e obtains the important substructure 33e and reduces the dimensions of the substructure number densities 33d, and the regression model construction unit 43f outputs the regression model 33f.

In the physical property prediction unit <NUM>, when the polymer structure input unit 47a inputs the polymer structure 6a, the polymer physical property prediction unit 47b calculates the number of substructures and the number of atoms by using the polymer structure 6a without generating the model structure 33a. Then, the polymer physical property prediction unit 47b obtains the substructure number density by dividing the obtained number of substructures by the number of atoms, reduces the number of dimensions of the substructure number densities based on the substructure number density 33d, and outputs the property value 6b by using the regression model 33f.

In the third functional configuration example, when the values of the substructure number density 33d are identical, even polymers with different lengths of the repeating unit can be equivalently treated. Although the model structure 33a is not generated, the important substructure 33e is determined by the dimension reduction unit 43e, thereby improving the prediction accuracy of the regression model 33f generated by the regression model construction unit 43f.

<FIG> is a drawing illustrating the fourth functional configuration example of the physical property prediction device. In <FIG>, in the fourth functional configuration example, the dimension reduction unit 43e is removed from the first functional configuration example illustrated in <FIG>. Thus, the important substructure 33e is not generated.

In the model generator <NUM>, the model structure generator 43a generates the model structure 33a, and the substructure number calculating unit 43b and the atom calculating unit 43c obtain the number of substructures 33b and the number of atoms 33c based on the model structure 33a.

In the physical property prediction unit <NUM>, when the polymer structure input unit 47a inputs the polymer structure 6a, the polymer physical property prediction unit 47b generates the model structure 33a and calculates the number of substructures and the number of atoms by using the model structure 33a. Then, the polymer physical property prediction unit 47b obtains the substructure number density by dividing the obtained number of substructures by the number of atoms, and uses the substructure number density 33d to obtain the physical property value 6b by the regression model 33f. The dimensions of the substructure number densities obtained from polymer structure 6a are not reduced.

In the fourth functional configuration, when the values of the substructure number density 33d are identical, even polymers with different lengths of the repeating unit can be equivalently treated. Although the important substructure 33e is not generated, the model structure 33a and the substructure number density 33d can improve the prediction accuracy of the regression model 33f generated by the regression model construction unit 43f.

Next, a functional configuration example based on the existing technique will be described as a comparative example.

<FIG> is a drawing illustrating a functional configuration example of a physical property prediction device based on the existing technique. In <FIG>, a physical property prediction device <NUM> has a functional configuration based on the existing technique. In the comparative example illustrated in <FIG>, a prediction processing unit <NUM> of the physical property prediction device <NUM> includes a model generator <NUM> and a physical property prediction unit <NUM>.

The model generator <NUM> is a processing unit that generates a regression model 83f and includes a substructure number calculating unit 93b and a regression model construction unit 98f. The substructure number calculating unit 93b obtains the repeating structure from the polymer structure and physical property DB <NUM> and stores the number of substructures 83b in the storage unit <NUM>. The regression model construction unit 93f constructs the regression model 83f by performing a regression analysis using the number of substructures 83b and an experimental value obtained from the polymer structure and physical property DB <NUM>, and stores the regression model 83f in the storage unit <NUM>.

The physical property prediction unit <NUM> is a processing unit that predicts the physical property value by applying the polymer structure 6a desired to be processed by a user to the regression model 83f, and includes a polymer structure input unit 97a and a polymer physical property prediction unit 97b. The polymer structure input unit 97a stores the polymer structure 6a in the storage unit <NUM> when receiving the polymer structure 6a. The polymer physical property prediction unit 97b obtains the number of substructures of the polymer structure 6a and applies the obtained number of substructures to the regression model 83f to obtain the predicted physical property value 6b. The physical property value 6b is presented to the user.

Next, results of verifying the prediction accuracy of the functional configuration example as a comparative example and the first to fourth functional configuration examples will be described. In each functional configuration example, the inventors have verified the prediction accuracy when the refractive index is predicted.

In Example <NUM>, the second functional configuration example (<FIG>) in which the model structure generator 43a and the dimension reduction unit 43e are removed was used for the verification. In Example <NUM>, the model structure 33a is not generated. In addition, the regression analysis is performed only once by the regression model construction unit 43f.

In the above-described second functional configuration example, structures of <NUM> general infinite linear polymers, such as polyethylene and polypropylene, (i.e., repeated units) and experimental values of the refractive index were used as training data. ECFP4 was used as the structure descriptor. Here, ECFP4 indicates ECFP having a radius of <NUM> and <NUM> dimensions. Hereinafter, ECPF4 will be referred to as an ECFP descriptor.

The ECFP descriptor is represented by a vector, each dimension represents a substructure classification, and a value of each dimension corresponds the number of substructures. Software such as RDKit was used to generate the ECFP descriptor. For convenience, the ECFP Descriptor is defined as a substructure number descriptor. Then, a substructure number density descriptor is obtained by dividing each element of the substructure number descriptor by the number of constituent atoms.

As a regression method, the Gaussian process regression was used. The regression model 33f was constructed using the substructure number density descriptor as an explanatory variable and the experimental value of the refractive index as an objective variable.

A data set was divided into a training data set and a verification data set by leave-one-out cross-verification, a regression model was constructed after hyperparameter tuning was performed using the training data set, and the refractive index of a polymer that was not learned was predicted using the verification data set. The coefficient of determination R<NUM> and the root mean squared error (RMSE) were used as evaluation measures of the prediction accuracy. There are various definition equations of R<NUM>, but the following equation was used this time. <MAT> Here, yi is an experimental value of an ith polymer, y_bar is a measured average value, and fi is the predicted value of the ith polymer.

In the description above, the number of constituent atoms corresponds to the number of atoms of the repeating unit, the substructure number descriptor corresponds to the numbers of substructures 33b, and the substructure number density descriptor corresponds to the substructure number densities <NUM> d.

In Example <NUM>, the third functional configuration example (<FIG>) in which the model structure generator 43a was removed was used for the verification. Since the model structure 33a was not generated, the substructure number density 33d was obtained from the repeating unit of the polymer structure and physical property DB <NUM>. The regression analysis was performed twice by the dimension reduction unit 43e and the regression model construction unit 43f.

Before constructing the regression model 33f, the dimension reduction unit 43e constructs a Lasso regression model (corresponding to the temporal regression model) from the substructure number density 33d and the experimental value of the physical property of the polymer obtained from the polymer structure and physical property DB <NUM>, and performs the dimension reduction by excluding variables with coefficients of <NUM>. In the regression model construction unit 43f, the regression model 33f was constructed by the Gaussian process regression in a manner similar to Example <NUM>. Then, the prediction accuracy of the refractive index was verified by a method similar to the method of Example <NUM>.

In Example <NUM>, the fourth functional configuration example (<FIG>) in which the dimension reduction unit 43e is removed was used for the verification. The important substructure 33e was not obtained. The regression model 33f with the number of dimensions of the substructures number densities 33d was constructed.

First, after <NUM> repeating units were generated as the model structure 33a, the substructure number descriptor and the number of atoms were calculated, and the substructure number density descriptor was calculated. After the substructure number density descriptor was calculated, a predicted value of the refractive index was obtained by a method similar to the method of Example <NUM>, and the prediction accuracy was verified.

In Example <NUM>, the first functional configuration example (<FIG>) including the important substructure 33e and the dimension reduction unit 43e was used for the verification.

First, after <NUM> repeating units were generated as the model structure 33a, the substructure number descriptor and the number of atoms were calculated, and the substructure number density descriptor was calculated. In addition, the dimension reduction unit 43e constructed a Lasso regression model (corresponding to the temporal regression model) and constructed the regression model 33f by the Gaussian process regression after reducing the dimensions by excluding variables with coefficients of <NUM>. Then, the prediction accuracy of the refractive index was verified by a method similar to the method of Example <NUM>.

In Comparative example <NUM>, the existing functional configuration example (<FIG>) in which only the substructure number calculating unit 93b and the regression model construction unit 93f obtains the regression model 83f was used for the verification.

The substructure number calculating unit 93b obtained the regression model 83f from the number of substructures 83b (i.e., the substructure number descriptor) obtained using the repeating unit and the experimental value of the refractive index. The regression model 83f was constructed using the Gaussian process regression in a manner similar to the manner of Example <NUM>. The predictive accuracy was verified in a manner similar to the manner of Example <NUM>.

<FIG> is a drawing illustrating an example of evaluation results. <FIG> illustrates the evaluation results of the prediction accuracy of the refractive index evaluated for each of Comparative example <NUM> and Examples <NUM> to <NUM> as described above. Results of R<NUM> and RMSE are shown as the evaluation results.

In the evaluation result of R<NUM>, compared with "-<NUM>" of Comparative Example <NUM>, results of "<NUM>" in Example <NUM>, "<NUM>" in Example <NUM>, "<NUM>" in Example <NUM>, and "<NUM>" in Example <NUM> are shown. The closer to "<NUM>" R<NUM> is, the higher the prediction accuracy becomes. Thus, from the evaluation results, it is found that the prediction accuracy can be significantly improved in any of the functional configuration examples according to the present embodiment.

In RMSE, compared with "<NUM>" in Comparative example <NUM>, results of "<NUM>" in Example <NUM>, "<NUM>" in Example <NUM>, "<NUM>" in Example <NUM>, and "<NUM>" in Example <NUM> are shown. The closer to "<NUM>" RMSE is, the higher the prediction accuracy becomes. Thus, it is found that the prediction accuracy can be significantly improved in all the functional configuration examples according to the present embodiment in the evaluation results.

From such evaluation results, it can be said that the substructure number density 33d, which the inventors have focused on, greatly contributes to the improvement of the prediction accuracy in the present embodiment.

Since the physical property value can be predicted in advance using the physical property prediction device <NUM>, the polymer structure 6a to be manufactured can be accurately selected. Thus, it is possible to reduce waste of costs, time, and so on related to polymer manufacturing.

As described above, according to the present embodiment, the physical properties of the polymer can be accurately predicted by generating the structure descriptor based on the polymer structure.

Next, a manufacturing method of a polymer using the desired polymer structure 6a and the predicted physical property will be described. <FIG> is a drawing illustrating an example of a manufacturing method of the polymer.

From <FIG>, the manufacturing method of the polymer in the present embodiment includes a step of obtaining a polymer structure having a property value predicted by the polymer physical property prediction method according to the present embodiment (S601) and a step of preparing raw materials for forming the polymer structure (S602), and a step of causing the raw materials to react to obtain a polymer represented by the polymer structure (S603).

In S601, a polymer structure predicted to have a predetermined physical property value is appropriately selected from the input polymer structures. For example, a polymer structure whose refractive index is within a predetermined range may be selected from the input polymer structures.

In S602, raw materials of monomers, oligomers, and the like are prepared as raw materials for forming the polymer structure selected in S1. For example, if the selected polymer structure is polyethylene, ethylene is prepared.

In S603, the raw materials prepared in S602 are caused to react to obtain a polymer represented by the polymer structure selected in S601. For example, ethylene is polymerized to form polyethylene.

The present invention is not limited to the specifically disclosed embodiments and various variations and modifications can be made without departing from the scope of the claims.

Claim 1:
A polymer physical property prediction device comprising:
a substructure number calculating unit configured to read a structural unit from a storage unit and use the structural unit to calculate numbers each indicating how many substructures are in a polymer, the polymer being formed of repetition of the structural unit;
an atom calculating unit configured to calculate a number indicating how many atoms are in the structural unit;
a substructure number density calculating unit configured to calculate number densities of the substructures from the numbers of substructures and the number of atoms in the structural unit;
a regression model construction unit configured to construct a regression model that predicts a physical property value by using an experimental value of a physical property of the polymer, experimentally obtained in advance, and the number densities of the substructures, the experimental value being obtained from the storage unit;
a polymer structure input unit configured to input a polymer structure of which the physical property value is to be predicted; and
a polymer physical property prediction unit configured to predict the physical property value corresponding to the input polymer structure by using the regression model;
wherein the polymer physical property prediction device further comprises:
a model structure generator configured to generate a model structure by repeatedly using the structural unit obtained from the storage unit; and
a dimension reduction unit configured to remove part of a structure descriptor representing the number densities of the substructures calculated by the substructure number density calculating unit,
wherein the substructure number calculating unit and the atom calculating unit calculate the numbers of substructures and the number of atoms from the generated model structure.