Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-23T13:29:46.689Z Has data issue: false hasContentIssue false
  • English
  • Français

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 ,
Mukil V. Ayyasamy  and
Prasanna V. Balachandran Open the ORCID record for Prasanna V. Balachandran [Opens in a new window]
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
*
Address all correspondence to Prasanna V. Balachandran at pvb5e@virginia.edu
Get access

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
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 882 - 890
Copyright
Copyright © The Author(s) 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

These two authors contributed equally to this work.

References

1.Weber, W.J., Ewing, R.C., Catlow, C.R.A., de la Rubia, T.D., Hobbs, L.W., Kinoshita, C., Matzke, H., Motta, A.T., Nastasi, M., Salje, E.K.H., Vance, E. R. and Zinkle, S. J.: Radiation effects in crystalline ceramics for the immobilization of high-level nuclear waste and plutonium. J. Mater. Res. 13, 1434–1484 (1998).Google Scholar
2.Donald, I.W., Metcalfe, B.L., and Taylor, R.N.J.: The immobilization of high level radioactive wastes using ceramics and glasses.. J. Mater. Sci. 32, 5851–5887 (1997).Google Scholar
3.Watanabe, Y., Ikoma, T., Yamada, H., Suetsugu, Y., Komatsu, Y., Stevens, G.W., Moriyoshi, Y., and Tanaka, J.: Novel long-term immobilization method for radioactive iodine-129 using a Zeolite/Apatite composite sintered body. ACS Appl. Mater. Interfaces 1, 1579–1584 (2009).Google Scholar
4.Masuda, K., Kato, O., Tanaka, Y., Nakajima, S., Okamoto, S., Sakuragi, T., and Yoshida, S.: Iodine immobilization: Development of solidification process for spent silver-sorbent using hot isostatic press technique. Prog. Nucl. Energy 92, 267–272 (2016).Google Scholar
5.Riley, B.J., Vienna, J.D., Strachan, D.M., McCloy, J.S., and Jerden, J.L. Jr: Materials and processes for the effective capture and immobilization of radioiodine: a review. J. Nucl. Mater. 470, 307–326 (2016).Google Scholar
6.White, T.J. and ZhiLi, D.: Structural derivation and crystal chemistry of apatites. Acta Crystallogr. B 59, 1–16 (2003).Google Scholar
7.White, T., Ferraris, C., Kim, J., and Madhavi, S.: Apatite-An adaptive framework structure. Rev. Mineral. Geochem. 57, 307–401 (2005).Google Scholar
8.Kim, J.Y., Dong, Z., and White, T.J.: Model apatite systems for the stabilization of toxic metals: II, cation and metalloid substitutions in chlorapatites. J. Am. Ceram. Soc. 88, 1253–1260 (2005).Google Scholar
9.Wopenka, B. and Pasteris, J.D.: A mineralogical perspective on the apatite in bone. Mater. Sci. Eng. C 25, 131–143 (2005).Google Scholar
10.Ardanova, L.I., Get'man, E.I., Loboda, S.L., Prisedsky, V.V., Tkachenko, T.V., Marchenko, V.I., Antonovich, V.P., Chivireva, N.A., Chebishev, K.A., and Lyashenko, A.S.: Isomorphous substitutions of rare earth elements for calcium in synthetic hydroxyapatite. Inorg. Chem. 49, 10687–10693 (2010).Google Scholar
11.Stennett, M., Pinnock, I., and Hyatt, N.: Rapid synthesis of Pb5(VO4)3I, for the immobilisation of iodine radioisotopes, by microwave dielectric heating. J. Nucl. Mater. 414, 352–359 (2011).Google Scholar
12.Yao, T., Lu, F., Sun, H., Wang, J., Ewing, R.C., and Lian, J.: Bulk iodoapatite ceramic densified by spark plasma sintering with exceptional thermal stability. J. Am. Ceram. Soc. 97, 2409–2412 (2014).Google Scholar
13.Legrain, F., Carrete, J., van Roekeghem, A., Madsen, G.K., and Mingo, N.: Materials screening for the discovery of new half-heuslers: Machine learning versus ab Initio methods. J. Phys. Chem. B 122, 625–632 (2018).Google Scholar
14.Balachandran, P.V., Broderick, S.R., and Rajan, K.: Identifying the “inorganic gene” for high temperature piezoelectric perovskites through statistical learning. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 467, 2271–2290 (2011).Google Scholar
15.Balachandran, P.V., Emery, A.A., Gubernatis, J.E., Lookman, T., Wolverton, C., and Zunger, A.: Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory. Phys. Rev. Mater. 2, 043802 (2018).Google Scholar
16.Pilania, G., Balachandran, P.V., Kim, C., and Lookman, T.: Finding new perovskite halides via machine learning. Front. Mater. 3, 19 (2016).Google Scholar
17.Li, Z., Xu, Q., Sun, Q., Hou, Z., and Yin, W.-J.: Thermodynamic stability landscape of halide double perovskites via high-throughput computing and machine learning. Adv. Funct. Mater. 29, 1807280 (2019).Google Scholar
18.Li, W., Jacobs, R., and Morgan, D.: Predicting the thermodynamic stability of perovskite oxides using machine learning models. Comput. Mater. Sci. 150, 454–463 (2018).Google Scholar
19.Bartel, C.J., Sutton, C., Goldsmith, B.R., Ouyang, R., Musgrave, C.B., Ghiringhelli, L.M., and Scheffler, M.: New tolerance factor to predict the stability of perovskite oxides and halides. Sci. Adv. 5, eaav0693 (2019). doi:10.1126/sciadv.aav0693.Google Scholar
20.Islam, N., Huang, W., and Zhuang, H.L.: Machine learning for phase selection in multi-principal element alloys. Comput. Mater. Sci. 150, 230–235 (2018).Google Scholar
21.Ren, F., Ward, L., Williams, T., Laws, K.J., Wolverton, C., Hattrick-Simpers, J., and Mehta, A.: Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci. Adv. 4, eaaq1566 (2018), doi:10.1126/sciadv.aaq1566.Google Scholar
22.Wang, J.: Incorporation of iodine into apatite structure: A crystal chemistry approach using artificial neural network. Front. Earth Sci. 3, 20 (2015).Google Scholar
23.Optiz, D. and Maclin, R.: Popular ensemble methods: An empirical study. J. Artif. Intell. Res. 11, 169–198 (1999).Google Scholar
24.Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., and Wolverton, C.: Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD). JOM 65, 1501–1509 (2013).Google Scholar
25.Kirklin, S., Saal, J.E., Meredig, B., Thompson, A., Doak, J.W., Aykol, M., Rühl, S., and Wolverton, C.: The Open Quantum Materials Database (OQMD): Assessing the accuracy of DFT formation energies. npj Comput. Mater. 1, 15010 (2015).Google Scholar
26.Ito, J.: Silicate apatites and oxyapatites. Am. Miner. 53, 890–907 (1968).Google Scholar
27.Cockbain, A.G.: The crystal chemistry of apatites. Mineral. Mag. 37, 654–660 (1968).Google Scholar
28.Grisafe, D.A. and Hummel, F.A.: Crystal chemistry and color in apatites containing cobalt, nickel and rare-earth ions. Am. Mineral. 55, 1131–1145 (1970).Google Scholar
29.Grisafe, D.A. and Hummel, F.A.: Pentavalent ion substitutions in the apatite structure: Part a. crystal chemistry. J. Solid State Chem. 2, 160–166 (1970).Google Scholar
30.Kriedler, E.R. and Hummel, F.A.: The crystal chemistry of apatite: Structure fields of fluor- and chlorapatite. Am. Mineral. 55, 170–184 (1970).Google Scholar
31.Dordević, T., Šutovic, S., Stojanovic, J., and Karanović, L.: Sr, Ba and Cd arsenates with the apatite-type structure. Acta Crystallogr. C 64, i82–i86 (2008).Google Scholar
32.Dong, Z., White, T.J., Wei, B., and Laursen, K.: Model apatite systems for the stabilization of toxic metals: I, calcium lead vanadate. J. Am. Ceram. Soc. 85, 2515–2522 (2002).Google Scholar
33.Silva, C.C., Vasconcelos, I.F., Sombra, A.S.B., and Valente, M.A.: Magnetic properties study on fe-doped calcium phosphate. Phys. Scr. 80, 055706 (2009).Google Scholar
34.Baikie, T., Mercier, P.H.J., Elcombe, M.E., Kim, J.Y., Page, Y.L., Mitchell, L.D., White, T.J., and Whitfield, P.S.: Tricilinic apatites. Acta Crystallogr. B 63, 251–256 (2007).Google Scholar
35.Suzuki, T., Hatsushika, T., and Miyake, M.: Synthetic hydroxyapatites as inorganic cation exchangers: Part 2. J. Chem. Soc.: Faraday Trans. I 78, 3605–3611(1982).Google Scholar
36.Flora, N.J., Yoder, C.H., and Jenkins, H.D.B.: Lattice energies of apatites and the estimation of ΔHf° (PO3−4, g). Inorg. Chem. 43, 2340–2345 (2004).Google Scholar
37.Sugiyama, S.: Approach using apatite to studies of energy and environment. Phosphorus Res. Bull. 21, 1–8 (2007).Google Scholar
38.Matsunaga, K., Inamori, H., and Murata, H.: Theoretical trend of ion exchange ability with divalent cations in hydroxyapatite. Phys. Rev. B 78, 094101 (2008).Google Scholar
39.Balachandran, P.V. and Rajan, K.: Structure maps for AI4AII6 (BO4)6X2 apatite compounds via data mining. Acta Crystallogr. B 68, 24–33 (2012).Google Scholar
40.Muller, O. and Roy, R.: The Major Ternary Structural Families (Springer-Verlag, New York, 1974).Google Scholar
41.Ahrens, L.H.: Anion affinity and polarizing power of cations. Nature 169, 463 (1952).Google Scholar
42.Shannon, R.D.: Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides. Acta Crystallogr. A 32, 751–767 (1976).Google Scholar
43.Pauling, L.: The Nature of the Chemical Bond (Cornell University Press, Ithaca, 1960).Google Scholar
44.Anslyn, E.V. and Dougherty, D.A.: Introduction to Structure and Models of Bonding. In Modern Physical Organic Chemistry, edited by Murdzek, J. (University Science Books, Sausalito, California, 2006).Google Scholar
45.Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001).Google Scholar
46.Liaw, A. and Wiener, M.: Classification and regression by randomForest. R News 2, 18 (2002).Google Scholar
47.Friedman, J.H.: Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).Google Scholar
48.Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002).Google Scholar
49.Greenwell, B., Boehmke, B., Cunningham, J., and Developers, G.: gbm: Generalized Boosted Regression Models, r package version 2.1.5 (2019).Google Scholar
50.Giannozzi, P., Baroni, S., Bonini, N., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Chiarotti, G.L., Cococcioni, M., Dabo, I., Dal Corso, A., de Gironcoli, S., Fabris, S., Fratesi, G., Gebauer, R., Gerstmann, U., Gougoussis, C., Kokalj, A., Lazzeri, M., Martin-Samos, L., Marzari, N., Mauri, F., Mazzarello, R., Paolini, S., Pasquarello, A., Paulatto, L., Sbraccia, C., Scandolo, S., Sclauzero, G., Seitsonen, A.P., Smogunov, A., Umari, P., and Wentzcovitch, R.M.: QUANTUM ESPRESSO: A modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter 21, 395502 (2009).Google Scholar
51.Perdew, J.P., Ruzsinszky, A., Csonka, G.I., Vydrov, O.A., Scuseria, G.E., Constantin, L.A., Zhou, X., and Burke, K.: Restoring the density-gradient expansion for exchange in solids and surfaces. Phys. Rev. Lett. 100, 136406 (2008).Google Scholar
52.Corso, A.D.: Pseudopotentials periodic table: From H to Pu. Comput. Mater. Sci. 95, 337–350 (2014).Google Scholar
53.Balachandran, P.V., Rajan, K., and Rondinelli, J.M.: Electronically driven structural transitions in A10(BO4)6F2 apatites (A = Ca, Sr, Pb, Cd and Hg). Acta Crystallogr. B 70, 612–615 (2014).Google Scholar
54.Fay, M.P. and Proschan, M.A.: Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat. Surv. 4, 1 (2010).Google Scholar
55.Cao, C., Chong, S., Thirion, L., Mauro, J.C., McCloy, J.S., and Goel, A.: Wet chemical synthesis of apatite-based waste forms-A novel room temperature method for the immobilization of radioactive iodine. J. Mater. Chem. A 5, 14331–14342 (2017).Google Scholar
56.Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., and Persson, K.A.: Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013), doi:10.1063/1.4812323.Google Scholar
57.Wang, Q., Kim, D., Dionysiou, D.D., Sorial, G.A., and Timberlake, D.: Sources and remediation for mercury contamination in aquatic systems - a literature review. Enviorn. Pollut. 131, 323–336 (2004).Google Scholar
58.Toro-González, M., Clifford, D.M., Copping, R., Mirzadeh, S., and Rojas, J.V.: Synthesis and characterization of intrinsically radiolabeled lanthanide phosphate nanoparticles toward biomedical and environmental applications. J. Nanopart. Res. 20, 238 (2018).Google Scholar
Supplementary material: File

Hartnett et al. supplementary material

Hartnett et al. supplementary material 1

Download Hartnett et al. supplementary material(File)
File 373.6 KB