Abstract: An information processing device for assisting creation of an Ising model receives an objective function that formulates characteristics of a composite material according to a formulation and a condition of constraint on the formulation, and creates the Ising model to solve an optimal solution of the formulation that satisfies a constraint condition expression and optimizes the characteristics. The condition of constraint on the formulation includes a condition related to a mix number of substances included in a substance group and to be mixed in the composite material, for each substance group composing the composite material, and a condition related to an amount of substance to be mixed in the composite material, for each substance included in the substance group.

Abstract: Prediction accuracy of trained prediction models is improved by setting an appropriate weight using a trained clustering model and classified clusters. A method of generating a model for predicting a material characteristic includes a step of acquiring a training dataset, a step of generating a trained clustering model using the training dataset and a clustering model, and classifying the training dataset into N clusters, a step of calculating a distance between centroids of the clusters, a step of calculating a weight between the clusters using the distance between the centroids of the clusters and a parameter representing a feature of the training dataset, and a step of generating, for the N clusters, respective trained prediction models {Mi} 1?i?N using the clusters and the weight.

Abstract: A property prediction device includes a processor; and a memory storing program instructions that cause the processor to: create a prediction model by using a training dataset of a composite material including raw materials in first and second categories to perform machine learning of a correspondence relationship between a property of the composite material, which is an objective variable, versus a mixing amount of the raw material in the first category and a weighted feature of the raw material in the second category, which are explanatory variables; and input, as explanatory variables, a mixing amount of a raw material in the first category and a weighted feature of a raw material in the second category, created based on prediction data of a composite material whose property is to be predicted, into the prediction model so as to predict the property of the composite material corresponding to the prediction data.

Abstract: A physical property prediction device for predicting a physical property of a compound including: a generation unit configured to generate a first stage trained model, by using, as training data, first stage synthesis information and synthesis result information; a generation unit configured to generate second stage through last stage trained models, by using, as training data, n-th stage (n?2) synthesis information, n-th stage synthesis result information, and n?1-th stage synthesis result information; a reception unit configured to receive a synthesis information setting for the compound for which the physical property is predicted; a prediction unit configured to predict a physical property value of a product synthesized by the first stage chemical reaction of the compound; a prediction unit configured to repeat, from a second stage chemical reaction to a last stage chemical reaction of the compound, a process of predicting a physical property value of a product synthesized by the n-th stage chemical react

Abstract: Prediction accuracy is increased in a prediction device using a trained model. A prediction device includes: a first trained model and a second trained model configured to respectively output first output data and second output data in response to input of input data of a prediction target; and an output portion configured to obtain the first output data and the second output data and calculate a weighted average value or take a weighted majority, thereby outputting prediction data. The first trained model is configured such that prediction accuracy for the input data of an interpolation region becomes higher than in the second trained model. The second trained model is configured such that prediction accuracy for the input data of an extrapolation region becomes higher than in the first trained model.