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Machine Learning-Based Prediction Models for Formation Energies of Interstitial Atoms in HCP Crystals

21 Pages Posted: 22 Nov 2019 First Look: Under Review

See all articles by Daegun You

Daegun You

Sungkyunkwan University - School of Mechanical Engineering

Shraddha Ganorkar

Sungkyunkwan University - School of Mechanical Engineering

Sooran Kim

Kyungpook National University - Department of Physics Education

Keonwook Kang

Yonsei University - Department of Mechanical Engineering

Won-Yong Shin

Yonsei University - Department of Computational Science and Engineering

Dongwoo Lee

Sungkyunkwan University - School of Mechanical Engineering

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%.

Keywords: Interstitial atom, hcp crystal, Formation energy, Machine learning, First-principles calculation

Suggested Citation

You, Daegun and Ganorkar, Shraddha and Kim, Sooran and Kang, Keonwook and Shin, Won-Yong and Lee, Dongwoo, Machine Learning-Based Prediction Models for Formation Energies of Interstitial Atoms in HCP Crystals. Available at SSRN: https://ssrn.com/abstract=3490391

Daegun You (Contact Author)

Sungkyunkwan University - School of Mechanical Engineering

Korea, Republic of (South Korea)

Shraddha Ganorkar

Sungkyunkwan University - School of Mechanical Engineering

Korea, Republic of (South Korea)

Sooran Kim

Kyungpook National University - Department of Physics Education

Korea, Republic of (South Korea)

Keonwook Kang

Yonsei University - Department of Mechanical Engineering

Korea, Republic of (South Korea)

Won-Yong Shin

Yonsei University - Department of Computational Science and Engineering ( email )

Korea, Republic of (South Korea)

Dongwoo Lee

Sungkyunkwan University - School of Mechanical Engineering ( email )

Korea, Republic of (South Korea)

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