Machine learning-based prediction models for formation energies of interstitial atoms in HCP crystals

Daegun You, Shraddha Ganorkar, Sooran Kim, Keon Wook Kang, Won Yong Shin, Dongwoo Lee

Research output: Contribution to journalArticle

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 languageEnglish
Pages (from-to)1-5
Number of pages5
JournalScripta Materialia
Volume183
DOIs
Publication statusPublished - 2020 Jul 1

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys

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