Machine learning apparatus and machine learning method

Claims

1. An input unit that acquires a first data set and a second data set that differs from the first data set in at least one of its structure, object, and generation means, A conversion unit that converts the second data set into the data space to which the first data set belongs, A learning unit that learns a predictive model for predicting material performance using the converted second data set and the first data set, The system includes an output unit that outputs learning results using the aforementioned prediction model. Machine learning device.

2. The aforementioned first data set is a data set based on experimental data, The second data set is a data set based on computational data. The machine learning apparatus according to claim 1.

3. The aforementioned structure includes at least one of the following: a chemical formula, a reaction formula, an empirical formula, a structural formula, element species, atomic arrangement, a molecular graph, a SMILES (simplified molecular input line entry system) string, and a descriptor created using a portion of these as input. The aforementioned object includes at least one of the following: energy, wave function, band structure, density of states, spectral density, density matrix, energy difference between two states, free energy, or quantities obtained as functions thereof. The learning result output from the output unit is obtained by aligning at least one of the structure, the target, and the generation means in the second data set with that of the first data set. The machine learning apparatus according to claim 2.

4. The second data set utilizes three or more heterogeneous data sets. The machine learning apparatus according to claim 2.

5. The conversion unit has a mechanism for projecting multiple data from the first data set and the second data set into a common data space. The machine learning apparatus according to claim 2.

6. Furthermore, the system includes an estimation unit that performs data transformation by constructing the function used in the transformation unit using a machine learning model. The machine learning apparatus according to claim 2.

7. Furthermore, it includes a feedback mechanism that proposes promising compositions in new measurements based on the results obtained by the predictive model. The machine learning apparatus according to claim 2.

8. A first data set and a second data set that differs from the first data set in at least one of its structure, object, and generation means are obtained. The second data set is transformed into the data space to which the first data set belongs. Using the transformed second data set and the first data set, a predictive model for predicting material performance is trained. Output the learning results using the aforementioned predictive model. Machine learning methods.

9. The transformation unit transforms the second data set into the data space to which the first data set belongs, so that it can be used as source data in transfer learning for the purpose of training the prediction model of the first data set. The machine learning apparatus according to claim 1.

10. The experimental data is measurement data relating to a chemical reaction represented by a chemical formula consisting of the relationship between the target reactants, products, and catalyst. The aforementioned calculation data corresponds to the elementary processes of a catalytic reaction. The machine learning apparatus according to claim 3.