Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intelligence are discussed in a new report. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, "The evaluation of thermoelectric materials relies significantly on the thermoelectric figure of merit, ZT, which serves as a crucial parameter in assessing their properties." Funders for this research include National Natural Science Foundation of China; Science And Technology Innovation Plan of Shanghai Science And Technology Commission. Our news editors obtained a quote from the research from Shanghai Polytechnic University: "The accurate prediction of ZT values can be accomplished by utilizing machine learning models to learn material characteristics. However, factors such as the size of the dataset, model hyperparameters, and data quality can all impact the accuracy of machine learning. In contrast to previous research where high-dimensional features were simply discarded to transform them into low-dimensional ones, deep learning models such as autoencoder can extract more effective information. Therefore, in this article, the combination of autoencoders and the Light Gradient Boosting Machine (LightGBM) is employed to learn the chemical characteristics and ZT values of various materials. The reliability of the model was confirmed by achieving an R2 score of 0.94 during tenfold cross-validation. 130 000 materials were predicted and screened, the temperature dependence of the screened materials was studied in depth, and 13 materials with high ZT values were identified."
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