Abstract
Prediction models of the formation energies of H, B, C, N, and O atoms in various interstitial sites of hcp-Ti, Zr, and Hf crystals are developed based on machine learning. Parametric models such as linear regression and brute force search (BFS) as well as nonparametric algorithms including the support vector regression (SVR) and the Gaussian process regression (GPR) are employed. Readily accessible chemical and geometrical descriptors allow straightforward implementation of the prediction models without any expensive computational modeling. The models based on BFS, SVR, and GPR show the excellent performance with R2 > 96%.
Original language | English |
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Pages (from-to) | 1-5 |
Number of pages | 5 |
Journal | Scripta Materialia |
Volume | 183 |
DOIs | |
Publication status | Published - 2020 Jul 1 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) under Grant No. NRF-2017R1E1A1A01078324 and Samsung Research Funding and Incubation Center for Future Technology under Grant No. SRFC-MA1802-06 .
Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) under Grant No. NRF-2017R1E1A1A01078324 and Samsung Research Funding and Incubation Center for Future Technology under Grant No. SRFC-MA1802-06.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Condensed Matter Physics
- Mechanics of Materials
- Mechanical Engineering
- Metals and Alloys