| Budget Amount *help |
¥43,940,000 (Direct Cost: ¥33,800,000、Indirect Cost: ¥10,140,000)
Fiscal Year 2020: ¥12,740,000 (Direct Cost: ¥9,800,000、Indirect Cost: ¥2,940,000)
Fiscal Year 2019: ¥12,350,000 (Direct Cost: ¥9,500,000、Indirect Cost: ¥2,850,000)
Fiscal Year 2018: ¥18,850,000 (Direct Cost: ¥14,500,000、Indirect Cost: ¥4,350,000)
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| Outline of Final Research Achievements |
We developed methodologies on parallel synthesis experiments, a synthesis predictor of unexperimented materials, and a feeding back model of synthesis process. They are expected to support rational materials design from the machine learning technique. We constructed a recommender system that predicts the successful or unsuccessful synthesis conditions for unexperimented compounds by applying the tensor decomposition method under the low rank assumption of the tensor. The training dataset was constructed with 1,000 of the 240,000 oxide synthesis conditions. Good prediction ability was demonstrated. These frameworks can accelerate the discovery of unknown materials.
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