On-the-fly machine-learning for high-throughput experiments: Search for rare-earth-free permanent magnets

Scientific Reports

Volumn 4, Issue , 2014, Pages

  Kusne, Aaron Gilad ^a,b   Gao, Tieren ^a   Mehta, Apurva ^c   Ke, Liqin ^d   Nguyen, Manh Cuong ^d,e   Ho, Kai Ming ^d,e   Antropov, Vladimir ^d   Wang, Cai Zhuang ^d,e   Kramer, Matthew J ^d   Long, Christian ^a   Takeuchi, Ichiro ^a  

^a UNIVERSITY OF MARYLAND   (United States)

^b NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY   (United States)

^c SLAC NATIONAL ACCELERATOR LABORATORY   (United States)

^d IOWA STATE UNIVERSITY   (United States)

^e IOWA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84923314848     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep06367     Document Type: Article

References (60)

1. Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191-201 (2013).
2. Jain, A. et al. A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci. 50, 2295-2310 (2011).
3. Klintenberg, M. The Electronic Structure Project. Electron. Struct. Proj. (2012). at 〈http://gurka.fysik.uu.se/esp-fs/〉 (Date of access: 01/05/2014).
4. Landis, D. D. et al. The Computational Materials Repository. Comput. Sci. Eng. 14, 51-57 (2012).
5. Mihalkovič, M. & Widom, M. Ab initio calculations of cohesive energies of Fe-based glass-forming alloys. Phys. Rev. B 70, 144107 (2004).
6. Fischer, C. C., Tibbetts, K. J., Morgan, D. & Ceder, G. Predicting crystal structure by merging data mining with quantum mechanics. Nat. Mater. 5, 641-646 (2006).
7. Saad, Y. et al. Data mining for materials: Computational experiments with AB compounds. Phys. Rev. B 85, 104104 (2012).
8. Pilania, G., Wang, C., Jiang, X., Rajasekaran, S. & Ramprasad, R. Accelerating materials property predictions using machine learning. Sci. Rep. 3, 1-6 (2013).
9. Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R. & Burke, K. Finding Density Functionals with Machine Learning. Phys. Rev. Lett. 108, 253002 (2012).
10. Montavon, G. et al. Machine learning of molecular electronic properties in chemical compound space. New J. Phys. 15, 095003 (2013).
11. Hautier, G., Fischer, C. C., Jain, A., Mueller, T. & Ceder, G. Finding Nature's Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory. Chem. Mater. 22, 3762-3767 (2010).
12. Rupp, M., Tkatchenko, A., Müller, K.-R. & von Lilienfeld, O. A. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Phys. Rev. Lett. 108, 058301 (2012).
13. Balabin, R. M. & Lomakina, E. I. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies. J. Chem. Phys. 131, 074104 (2009).
14. Hansen, K. et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. J. Chem. Theory Comput. 9, 3404-3419 (2013).
15. D'Avezac, M., Luo, J.-W., Chanier, T. & Zunger, A. Genetic-Algorithm Discovery of a Direct-Gap and Optically Allowed Superstructure from Indirect-Gap Si and Ge Semiconductors. Phys. Rev. Lett. 108, 027401 (2012).
16. Behler, J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys. Chem. Chem. Phys. 13, 17930-17955 (2011).
17. Meredig, B. et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B 89, 094104 (2014).
18. Huffman, L., Simmons, J., De Graef, M. & Pollak, I. Shape priors for MAP segmentation of alloy micrographs using graph cuts. in Proc. IEEE Stat. Signal Process. Workshop 661-664 (2011).
19. Simmons, J. P. et al. Application and further development of advanced image processing algorithms for automated analysis of serial section image data. Model. Simul. Mater. Sci. Eng. 17, 025002 (2009).
20. Comer, M., Bouman, C. A., Graef, M. D. & Simmons, J. P. Bayesian methods for image segmentation. JOM 63, 55-57 (2011).
21. MacSleyne, J. P., Simmons, J. P. & De Graef, M. On the use of 2-D moment invariants for the automated classification of particle shapes. Acta Mater. 56, 427-437 (2008).
22. Gulsoy, E. B., Simmons, J. P. & De Graef, M. Application of joint histogram and mutual information to registration and data fusion problems in serial sectioning microstructure studies. Scr. Mater. 60, 381-384 (2009).
23. Niezgoda, S. R., Yabansu, Y. C. & Kalidindi, S. R. Understanding and visualizing microstructure and microstructure variance as a stochastic process. Acta Mater. 59, 6387-6400 (2011).
24. Kalidindi, S. R., Niezgoda, S. R. & Salem, A. A. Microstructure informatics using higher-order statistics and efficient data-mining protocols. JOM 63, 34-41 (2011).
25. Lavrač, N. in Artif. Intell. Med. (Horn, W., Shahar, Y., Lindberg, G., Andreassen, S. & Wyatt, J.) 47-62 (Springer Berlin Heidelberg, 1999).
26. Cleophas, T. J. & Zwinderman, A. H. Machine Learning in Medicine. (Springer, 2013).
27. Boyarshinov, V. Machine Learning In Computational Finance: Practical algorithms for building artificial intelligence applications. (LAP LAMBERT Academic Publishing, 2012).
28. Kovalerchuk, B. & Vityaev, E. in Data Min. Knowl.Discov. Handb. (Maimon, O. &Rokach, L.) 1203-1224 (Springer US, 2005).
29. Jurafsky, D. & Martin, J. H. Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. (Pearson Prentice Hall, 2009).
30. Long, C. et al. Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis. Rev. Sci. Instrum. 78, 072217 (2007).
31. Long, C., Bunker, D., Li, X., Karen, V. & Takeuchi, I. Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization. Rev. Sci. Instrum. 80, 103902 (2009).
32. Barr, G., Dong, W. & Gilmore, C. J. High-throughput powder diffraction. II. Applications of clustering methods and multivariate data analysis. J. Appl. Crystallogr. 37, 243-252 (2004).
33. Hunter, D. et al. Giant magnetostriction in annealed Co1-xFex thin-films. Nat. Commun. 2, 518 (2011).
34. Kan, D., Long, C. J., Steinmetz, C., Lofland, S. E. & Takeuchi, I. Combinatorial search of structural transitions: Systematic investigation of morphotropic phase boundaries in chemically substituted BiFeO3. J. Mater. Res. 27, 2691-2704 (2012).
35. Gao, T. et al. Combinatorial exploration of rare-earth-free permanent magnets: Magnetic and microstructural properties of Fe-Co-W thin films. Appl. Phys. Lett. 102, 022419 (2013).
36. Sourmail, T. Near equiatomic FeCo alloys: Constitution, mechanical and magnetic properties. Prog. Mater. Sci. 50, 816-880 (2005).
37. Fukunaga, K. & Hostetler, L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21, 32-40 (1975).
38. Baumes, L. A., Moliner, M., Nicoloyannis, N. & Corma, A. Areliable methodology for high throughput identification of a mixture of crystallographic phases from powder X-ray diffraction data. CrystEngComm 10, 1321-1324 (2008).
39. LeBras, R. et al. in Princ. Pract. Constraint Program. 2011 508-522 (Springer, 2011).
40. Mueller, T., Kusne, A. G. & Ramprasad, R. Machine learning in materials science: Recent progress and emerging applications. Rev. Comput. Chem. (Accepted for publication).
41. Comaniciu, D. & Meer, P. Mean shift: A robust approach toward feature space analysis. Pattern Anal. Mach. Intell. IEEE Trans. On 24, 603-619 (2002).
42. Kramer, M. J., McCallum, R. W., Anderson, I. A. & Constantinides, S. Prospects for Non-Rare Earth Permanent Magnets for Traction Motors and Generators. JOM 64, 752-763 (2012).
43. Burkert, T., Nordström, L., Eriksson, O. & Heinonen, O. Giant Magnetic Anisotropy in Tetragonal FeCo Alloys. Phys. Rev. Lett. 93, 027203 (2004).
44. Andersson, G. et al. Perpendicular Magnetocrystalline Anisotropy in Tetragonally Distorted Fe-Co Alloys. Phys. Rev. Lett. 96, 037205 (2006).
45. Yildiz, F., Przybylski, M., Ma, X.-D. & Kirschner, J. Strong perpendicular anisotropy in Fe1-xCox alloy films epitaxially grown on mismatching Pd(001), Ir(001), and Rh(001) substrates. Phys. Rev. B 80, 064415 (2009).
46. Weller, D., Brändle, H., Gorman, G., Lin, C.-J. & Notarys, H. Magnetic and magneto-optical properties of cobalt-platinum alloys with perpendicular magnetic anisotropy. Appl. Phys. Lett. 61, 2726-2728 (1992).
47. Deaven, D. & Ho, K. Molecular geometry optimization with a genetic algorithm. Phys. Rev. Lett. 75, 288-291 (1995).
48. Ji, M., Wang, C.-Z. & Ho, K.-M. Comparing efficiencies of genetic and minima hopping algorithms for crystal structure prediction. Phys. Chem. Chem. Phys. 12, 11617-11623 (2010).
49. Ke, L., Belashchenko, K. D., van Schilfgaarde, M., Kotani, T. & Antropov, V. P. Effects of alloying and strain on the magnetic properties of Fe16N2. Phys. Rev. B 88, 024404 (2013).
50. Curtarolo, S. et al. AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations. Comput. Mater. Sci. 58, 227-235 (2012).
51. Krause, E. F. Taxicab Geometry: An Adventure in Non-Euclidean Geometry. (Dover Publications, 1987).
52. Takeuchi, I. et al. Identification of novel compositions of ferromagnetic shape-memory alloys using composition spreads. Nat. Mater. 2, 180-184 (2003).
53. Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1-19 (1995).
54. Zhou, X., Johnson, R. & Wadley, H. Misfit-energy-increasing dislocations in vapor-deposited CoFe/NiFe multilayers. Phys. Rev. B 69, 144113 (2004).
55. Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133-A1138 (1965).
56. Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169-11185 (1996).
57. Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953 (1994).
58. Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758-1775 (1999).
59. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865-3868 (1996).
60. Monkhorst, H. J. & Pack, J. D. Special points for Brillouin-zone integrations. Phys. Rev. B 13, 5188-5192 (1976).