Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects

Dopants play an important role in synthesizing materials to improve target materials properties or stabilize the materials. In particular, the dopants are essential to improve thermoelectic performances of the materials. However, existing machine learning methods cannot accurately predict the materi...

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Bibliographic Details
Published in:npj computational materials 2021-07, Vol.7 (1), p.1-11, Article 106
Main Authors: Na, Gyoung S., Jang, Seunghun, Chang, Hyunju
Format: Article
Language:English
Subjects:
639/301/1034/1037
639/301/299/2736
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Dopants
Embedding
Error analysis
Figure of merit
Learning algorithms
Machine learning
Material properties
Materials Science
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
Neural networks
Theoretical
Thermoelectric materials
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Summary:Dopants play an important role in synthesizing materials to improve target materials properties or stabilize the materials. In particular, the dopants are essential to improve thermoelectic performances of the materials. However, existing machine learning methods cannot accurately predict the materials properties of doped materials due to severely nonlinear relations with their materials properties. Here, we propose a unified architecture of neural networks, called DopNet, to accurately predict the materials properties of the doped materials. DopNet identifies the effects of the dopants by explicitly and independently embedding the host materials and the dopants. In our evaluations, DopNet outperformed existing machine learning methods in predicting experimentally measured thermoelectric properties, and the error of DopNet in predicting a figure of merit (ZT) was 0.06 in mean absolute error. In particular, DopNet was significantly effective in an extrapolation problem that predicts ZTs of unknown materials, which is a key task to discover novel thermoelectric materials.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-021-00564-y