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

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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.

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Acknowledgments

All authors acknowledge support from the Advanced Research Computing Services at the University of Virginia (UVa), which maintains the Rivanna supercomputing cluster where we performed the DFT calculations. M.V.A and P.V.B acknowledge support from the UVa start-up funds. T.Q.H acknowledges support from the DARPA-Topological Excitations in Electronics program (grant# D18AP00009). The content ofthe information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. Approved for public release; distribution is unlimited. All authors thank Dr Shruba Gangopadhyay for providing insightful comments on the manuscript.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA, 22904, USA

    Timothy Q. Hartnett, Mukil V. Ayyasamy & Prasanna V. Balachandran

  2. Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, 22904, USA

    Prasanna V. Balachandran

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  1. Timothy Q. Hartnett
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  2. Mukil V. Ayyasamy
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Correspondence to Prasanna V. Balachandran.

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These two authors contributed equally to this work.

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Hartnett, T.Q., Ayyasamy, M.V. & Balachandran, P.V. Prediction of new iodine-containing apatites using machine learning and density functional theory. MRS Communications 9, 882–890 (2019). https://doi.org/10.1557/mrc.2019.103

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