Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

Medasani, Bharat; Gamst, Anthony; Ding, Hong; Chen, Wei; Persson, Kristin A.; Asta, Mark; Canning, Andrew; Haranczyk, Maciej

doi:10.1038/s41524-016-0001-z

Title: Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

Journal Article · Fri Dec 09 00:00:00 EST 2016 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-016-0001-z· OSTI ID:1494077

Medasani, Bharat ^[1]; Gamst, Anthony ^[2]; Ding, Hong ^[3]; Chen, Wei ^[4]; Persson, Kristin A. ^[3]; Asta, Mark ^[3]; Canning, Andrew ^[5]; Haranczyk, Maciej ^[5]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Univ. of California, San Diego, CA (United States)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Illinois Inst. of Technology, Chicago, IL (United States)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic (A-B) compounds, using as an example systems with the cubic B2 crystal structure (with equiatomic AB stoichiometry). To the best of our knowledge, this work is the first application of machine learning-models for point defect properties. High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds. The systems are classified into two groups: (i) those for which the intrinsic defects are antisites for both A and B rich compositions, and (ii) those for which vacancies are the dominant defect for either or both composition ranges. The data was analyzed by machine learning-techniques using decision tree, and full and reduced multiple additive regression tree (MART) models. Among these three schemes, a reduced MART (r-MART) model using six descriptors (formation energy, minimum and difference of electron densities at the Wigner-Seitz cell boundary, atomic radius difference, maximal atomic number and maximal electronegativity) presents the highest fit (98 %) and predictive (75 %) accuracy. This model is used to predict the defect behavior of other B2 compounds, and it is found that 45 % of the compounds considered feature vacancies as dominant defects for either A or B rich compositions (or both). The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds, and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1494077

Journal Information:: npj Computational Materials, Vol. 2, Issue 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 43 works

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