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Prediction of new iodine-containing apatites using machine learning and density functional theory

Published online by Cambridge University Press:  27 August 2019

Timothy Q. Hartnett
Affiliation:
Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904, USA
Mukil V. Ayyasamy
Affiliation:
Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904, USA
Prasanna V. Balachandran
Affiliation:
Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904, USA Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA 22904, USA
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Abstract

The authors develop a computational approach that integrates machine learning (ML) and density functional theory (DFT) with experimental data to predict formable and thermodynamically stable iodine-containing apatites. This is an important problem because radioactive iodine is toxic and capturing it in solid waste forms have implications in remediation treatments. The authors train ML models using 336 compositions and screen 54 iodine-containing compounds in apatite stoichiometry. ML models predict 18 as formable and 24 as nonformable in the apatite structure; 12 compounds were identified to be uncertain. DFT convex hull predicted two to be thermodynamically stable, one as metastable, and nine as unstable.

Type
Artificial Intelligence Research Letters
Copyright
Copyright © The Author(s) 2019 

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Footnotes

*

These two authors contributed equally to this work.

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