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Hu, Y, Wu, M, Yuan, M, Wen, Y, Ren, P, Ye, S, Liu, F, Zhou, B, Fang, H, Wang, R, Ji, Z and Huang, R (2024) Accurate prediction of dielectric properties and bandgaps in materials with a machine learning approach. Applied Physics Letters, 125. ISSN 0003-6951
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1766240_2_unknown_upload_24673538_sjtzn7.pdf - Accepted Version Download (4MB) | Preview |
Abstract
The conventional approach to exploring suitable dielectrics for future logic and memory devices relies on first-principle calculations, which are expensive and time-consuming. In this work, we adopt a data-driven machine learning (ML)-based approach to build a model for predicting these properties. By incorporating structural information into the input descriptors, we achieve record-high accuracy in predicting the dielectric constant, with an R2 of 0.886 and an RMSE of 0.083. Additionally, we achieve high predictions for the band gap, with accuracies of 0.832 and 0.533 for R2 and RMSE, respectively. The features corresponding to specific properties are analyzed to obtain physical insights. Finally, we employ first-principle calculations to validate the feasibility of this model. This work proposes a highly efficient approach for using ML to predict material properties.
| Item Type: | Article |
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| Additional Information: | This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Yilin Hu, Maokun Wu, Miaojia Yuan, Yichen Wen, Pengpeng Ren, Sheng Ye, Fayong Liu, Bo Zhou, Hui Fang, Runsheng Wang, Zhigang Ji, Ru Huang; Accurate prediction of dielectric properties and bandgaps in materials with a machine learning approach. Appl. Phys. Lett. 7 October 2024; 125 (15): 152905. https://doi.org/10.1063/5.0223890 and may be found at https://doi.org/10.1063/5.0223890 |
| Uncontrolled Keywords: | 02 Physical Sciences; 09 Engineering; 10 Technology; Applied Physics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science and Mathematics |
| Publisher: | American Institute of Physics |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 30 Sep 2024 13:04 |
| Last Modified: | 14 Oct 2024 15:00 |
| DOI or ID number: | 10.1063/5.0223890 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/24295 |
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