Застосування машинного навчання для прогнозування властивостей термоелектричних матеріалів на основі Bi2Te3
Ключові слова:
методи машинного навчання, термоелектричне матеріалознавствоАнотація
У роботі наводяться приклади оцінки ефективності застосування машинного навчання для прогнозування властивостей термоелектричних матеріалів на основі Bi2Te3. Оглянуто результати їх застосування та способи вибору оптимальних параметрів вхідних даних, описано відмінності та особливості вибору алгоритмів, етапи роботи та навчання машинних моделей, а також критерії оцінки ефективності та валідації отриманих прогнозів.
The paper provides examples of assessing the effectiveness of machine learning for predicting the properties of Bi2Te3-based thermoelectric materials. The results of their application and methods for selecting optimal input data parameters are considered, the differences and features of choosing algorithms, the stages of work and training machine models, as well as the criteria for assessing the effectiveness and validation of the obtained forecasts are described.
Посилання
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