Journal
MATERIALS LETTERS
Volume 319, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.matlet.2022.132299
Keywords
Deep learning; Thermoelectric materials; Figure-of-merit; Seebeck effect
Funding
- National Research Foundation (NRF) of South Korea [2020R1A2C1004720]
Ask authors/readers for more resources
This study employed deep learning to investigate the performance of thermoelectric materials, achieving reliable and accurate predictions, successfully reproducing experimental behaviors, and making extensive predictions for a large number of compositions.
In the present work, we employed a deep learning (DL) technique to investigate thermoelectric (TE) materials performance. The figure-of-merit (ZT) determined experimentally for various compositions at a wide range of temperatures with composition features were used to train the DL model. The validation results showed that the built DL model exhibited a reliable accuracy with a cross-validation score R2 value of 0.91. In addition, by this model, the experimental behaviors of TE materials (ZT vs temperature) were successfully reproduced and a general prediction of 300,000 composition have been done.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available