Model generation method, data presentation method, data generation method, inference method, model generation device, data presentation device, data generation device, and inference device

An estimation method comprising the steps of:

• acquiring, by a computer, at least one of first data and second data regarding a crystal structure of a target material;
• converting, by the computer, at least one of the first data and second data acquired into at least one of a first feature vector and a second feature vector by using at least one of a trained first encoder and a trained second encoder;
• and
• estimating, by the computer, a characteristic of the target material from a value of at least one of the obtained first feature vector and second feature vector by using a trained estimator,
• wherein
• the second data indicates a property of a material with an index different from an index of the first data,
• a dimension of the first feature vector is the same as a dimension of the second feature vector,
• the trained first encoder and the trained second encoder are generated by machine learning using first data and second data for learning,
• the first data and the second data for learning include a positive sample and a negative sample,
• the positive sample includes a combination of first data and second data for the same material,
• the negative sample includes at least one of first data and second data for a material different from the material of the positive sample,
• machine learning for the first encoder and the second encoder is performed by training the first encoder and the second encoder so that values of a first feature vector and a second feature vector calculated from the first data and the second data of the positive sample are positioned close to each other, and a value of at least one of a first feature vector and a second feature vector calculated from at least one of the first data and the second data of the negative sample is positioned far from a value of at least one of the first feature vector and the second feature vector calculated from the positive sample,
• the trained estimator is generated by machine learning further using correct information indicating a characteristic of a material for learning, and
• the machine learning for the estimator is configured by training the estimator so that a result of estimating the characteristic of the material for learning from at least one of the first feature vector and the second feature vector, calculated from at least one of the first data and the second data for learning by using at least one of the first encoder and the second encoder, matches the correct information.

An estimation device comprising:

• a target data acquisition unit configured to acquire at least one of first data and second data regarding a crystal structure of a target material;
• a conversion unit configured to convert at least one of the first data and second data acquired into at least one of a first feature vector and a second feature vector by using at least one of a trained first encoder and a trained second encoder;
• and
• an estimation unit configured to estimate a characteristic of the target material from a value of at least one of the obtained first feature vector and second feature vector by using a trained estimator,
• wherein
• the second data indicates a property of a material with an index different from an index of the first data,
• a dimension of the first feature vector is the same as a dimension of the second feature vector,
• the trained first encoder and the trained second encoder are generated by machine learning using first data and second data for learning,
• the first data and the second data for learning include a positive sample and a negative sample,
• the positive sample includes a combination of first data and second data for the same material,
• the negative sample includes at least one of first data and second data for a material different from the material of the positive sample,
• machine learning for the first encoder and the second encoder is performed by training the first encoder and the second encoder so that values of a first feature vector and a second feature vector calculated from the first data and the second data of the positive sample are positioned close to each other, and a value of at least one of a first feature vector and a second feature vector calculated from at least one of the first data and the second data of the negative sample is positioned far from a value of at least one of the first feature vector and the second feature vector calculated from the positive sample,
• the trained estimator is generated by machine learning further using correct information indicating a characteristic of a material for learning, and
• the machine learning for the estimator is configured by training the estimator so that a result of estimating a characteristic of material for learning from at least one of the first feature vector and the second feature vector, calculated from at least one of the first data and the second data for learning by using at least one of the first encoder and the second encoder, matches the correct information.