Abstract: A model generation method according to one aspect of the present invention acquires first data and second data regarding a crystal structure of a material, and performs machine learning for a first encoder and a second encoder by using the first data and the second data. The second data indicates a property of the material with an index different from that of the first data. The first encoder is configured to convert the first data into a first feature vector, and the second encoder is configured to convert the second data into a second feature vector. The dimension of the first feature vector is the same as the dimension of the second feature vector. In machine learning, the first encoder and the second encoder are trained so that the values of the feature vectors of the positive samples are positioned close to each other, and the feature vector of the negative sample is positioned far from the feature vector of the positive sample.