Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials. Issue 37 (8th September 2020)

• Record Type: Journal Article
• Authors: Chen, Lihao; Xu, Ben; Chen, Jia; Bi, Ke; Li, Changjiao; Lu, Shengyu; Hu, Guosheng; Lin, Yuanhua
• Is Part Of: Journal of materials chemistry. Volume 8:Issue 37(2020)
• Page Start: 13079
• Page End: 13089
• Publication Date: 2020-09-08
• Subjects: Materials -- Periodicals; Chemistry, Analytic -- Periodicals; Optical materials -- Research -- Periodicals; Electronics -- Materials -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d0tc02855j
• Languages: English
• ISSNs: 2050-7526

Abstract

Abstract : Machine learning can significantly help to predict the thermoelectric properties of materials, such as the Seebeck coefficient and electrical conductivity. Abstract : Machine learning can significantly help to predict the thermoelectric properties of materials, such as the Seebeck coefficient and electrical conductivity. However, the mechanism underlying the excellent performance of such models is not known. In this study, a new dual-route machine learning system (DMLS) is developed to extract the relationship between the features from materials and the ones from band structure. These findings can help us to set up a bridge between the feature significance and the thermal electric properties, such as Seebeck coefficient, which can provide theoretical guidance regarding the designing of a material with excellent thermoelectric properties.