THERMODYNAMIC EQUILIBRIUM STATE PREDICTION DEVICE, PREDICTION METHOD AND PREDICTION PROGRAM

1 . A prediction device for predicting a thermodynamic equilibrium state of a target material, comprising:

a processor; and

a memory storing program instructions that cause the processor to:

generate training data including inputs related to predetermined design conditions and outputs related to the thermodynamic equilibrium state that may occur based on the predetermined design conditions, for a model configured to output target variables related to the thermodynamic equilibrium state based on input explanatory variables related to design conditions of the target material;

perform machine learning by using the generated training data so that an input-output relation of the model approaches an input-output relation of the training data;

set predictive explanatory variables that are used to predict the thermodynamic equilibrium state of the target material; and

a prediction unit configured to output predictive target variables from the model, based on the predictive explanatory variables, wherein the predictive explanatory variables are input into the model on which the machine learning has been performed, and wherein the predictive target variables are predicted results of the thermodynamic equilibrium state.

2 . The prediction device as claimed in claim 1 , wherein the program instructions cause the processor to further

generate a phase diagram of the thermodynamic equilibrium state based on the output predictive target variables, and display the generated phase diagram.

3 . The prediction device as claimed in claim 1 , wherein the model is a multi-layer neural network and the processor trains the model by using deep learning.

4 . The prediction device as claimed in claim 3 ,

wherein the target variables that are output from the model are phase fractions of the target material in the thermodynamic equilibrium state; and

wherein a softmax function is used for neurons in an output layer of the multi-layer neural network.

5 . The prediction device as claimed in claim 1 , wherein the processor generates the explanatory variables including a combination of predetermined ranges of the design conditions, calculates the target variables by using a CALPHAD method, and generates the training data including the generated explanatory variables and the calculated target variables.

6 . The prediction device as claimed in claim 1 , wherein the target material is an aluminum alloy, the explanatory variables include a composition and a manufacturing condition of the aluminum alloy, and the target variables include phase fractions of the aluminum alloy in the thermodynamic equilibrium state.

7 . A prediction method for predicting a thermodynamic equilibrium state of a target material, comprising:

generating training data including inputs related to predetermined design conditions and outputs related to the thermodynamic equilibrium state that may occur based on the predetermined design conditions, for a model configured to output target variables related to the thermodynamic equilibrium state based on input explanatory variables related to design conditions of the target material;

performing machine learning by using the generated training data so that an input-output relation of the model approaches an input-output relation of the training data;

setting predictive explanatory variables that are used to predict the thermodynamic equilibrium state of the target material; and

outputting predictive target variables from the model, based on the predictive explanatory variables, wherein the predictive explanatory variables are input into the model on which the machine learning has been performed, and wherein the predictive target variables are predicted results of the thermodynamic equilibrium state.

8 . A non-transitory computer-readable recording medium having stored therein a prediction program for predicting a thermodynamic equilibrium state of a target material, the prediction program causing a computer to achieve functions execute a process comprising:

generating training data including inputs related to predetermined design conditions and outputs related to the thermodynamic equilibrium state that may occur based on the predetermined design conditions, for a model configured to output target variables related to the thermodynamic equilibrium state based on input explanatory variables related to design conditions of the target material;

performing machine learning by using the generated training data so that an input-output relation of the model approaches an input-output relation of the training data;

setting predictive explanatory variables that are used to predict the thermodynamic equilibrium state of the target material; and

outputting predictive target variables from the model, based on the predictive explanatory variables-, wherein the predictive explanatory variables are input into the model on which the machine learning has been performed, and wherein the predictive target variables are predicted results of the thermodynamic equilibrium state.