Crossref
journal-article
Springer Science and Business Media LLC
Scientific Reports (297)
Authors
11
- Aaron Gilad Kusne (first)
- Tieren Gao (additional)
- Apurva Mehta (additional)
- Liqin Ke (additional)
- Manh Cuong Nguyen (additional)
- Kai-Ming Ho (additional)
- Vladimir Antropov (additional)
- Cai-Zhuang Wang (additional)
- Matthew J. Kramer (additional)
- Christian Long (additional)
- Ichiro Takeuchi (additional)
References
60
Referenced
230
-
Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).
(
10.1038/nmat3568
) / Nat. Mater. by S Curtarolo (2013) -
Jain, A. et al. A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci. 50, 2295–2310 (2011).
(
10.1016/j.commatsci.2011.02.023
) / Comput. Mater. Sci. by A Jain (2011) - Klintenberg, M. The Electronic Structure Project. Electron. Struct. Proj. (2012). at <http://gurka.fysik.uu.se/esp-fs/> (Date of access: 01/05/2014).
-
Landis, D. D. et al. The Computational Materials Repository. Comput. Sci. Eng. 14, 51–57 (2012).
(
10.1109/MCSE.2012.16
) / Comput. Sci. Eng. by DD Landis (2012) -
Mihalkovič, M. & Widom, M. Ab initio calculations of cohesive energies of Fe-based glass-forming alloys. Phys. Rev. B 70, 144107 (2004).
(
10.1103/PhysRevB.70.144107
) / Phys. Rev. B by M Mihalkovič (2004) -
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).
(
10.1038/nmat1691
) / Nat. Mater. by CC Fischer (2006) -
Saad, Y. et al. Data mining for materials: Computational experiments with AB compounds. Phys. Rev. B 85, 104104 (2012).
(
10.1103/PhysRevB.85.104104
) / Phys. Rev. B by Y Saad (2012) -
Pilania, G., Wang, C., Jiang, X., Rajasekaran, S. & Ramprasad, R. Accelerating materials property predictions using machine learning. Sci. Rep. 3, 1–6 (2013).
(
10.1038/srep02810
) / Sci. Rep. by G Pilania (2013) -
Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R. & Burke, K. Finding Density Functionals with Machine Learning. Phys. Rev. Lett. 108, 253002 (2012).
(
10.1103/PhysRevLett.108.253002
) / Phys. Rev. Lett. by JC Snyder (2012) -
Montavon, G. et al. Machine learning of molecular electronic properties in chemical compound space. New J. Phys. 15, 095003 (2013).
(
10.1088/1367-2630/15/9/095003
) / New J. Phys. by G Montavon (2013) -
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).
(
10.1021/cm100795d
) / Chem. Mater. by G Hautier (2010) -
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).
(
10.1103/PhysRevLett.108.058301
) / Phys. Rev. Lett. by M Rupp (2012) -
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).
(
10.1063/1.3206326
) / J. Chem. Phys. by RM Balabin (2009) -
Hansen, K. et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. J. Chem. Theory Comput. 9, 3404–3419 (2013).
(
10.1021/ct400195d
) / J. Chem. Theory Comput. by K Hansen (2013) -
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).
(
10.1103/PhysRevLett.108.027401
) / Phys. Rev. Lett. by M D' Avezac (2012) -
Behler, J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys. Chem. Chem. Phys. 13, 17930–17955 (2011).
(
10.1039/c1cp21668f
) / Phys. Chem. Chem. Phys. by J Behler (2011) -
Meredig, B. et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B 89, 094104 (2014).
(
10.1103/PhysRevB.89.094104
) / Phys. Rev. B by B Meredig (2014) -
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).
(
10.1109/SSP.2011.5967788
) -
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).
(
10.1088/0965-0393/17/2/025002
) / Model. Simul. Mater. Sci. Eng. by JP Simmons (2009) -
Comer, M., Bouman, C. A., Graef, M. D. & Simmons, J. P. Bayesian methods for image segmentation. JOM 63, 55–57 (2011).
(
10.1007/s11837-011-0113-3
) / JOM by M Comer (2011) -
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).
(
10.1016/j.actamat.2007.09.039
) / Acta Mater. by JP MacSleyne (2008) -
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).
(
10.1016/j.scriptamat.2008.11.004
) / Scr. Mater. by EB Gulsoy (2009) -
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).
(
10.1016/j.actamat.2011.06.051
) / Acta Mater. by SR Niezgoda (2011) -
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).
(
10.1007/s11837-011-0057-7
) / JOM by SR Kalidindi (2011) - Lavrač, N. in Artif. Intell. Med. (Horn,W., Shahar, Y., Lindberg,G., Andreassen, S. & Wyatt, J.) 47–62 (Springer Berlin Heidelberg, 1999).
-
Cleophas, T. J. & Zwinderman, A. H. Machine Learning in Medicine. (Springer, 2013).
(
10.1007/978-94-007-5824-7
) - Boyarshinov, V. Machine Learning In Computational Finance: Practical algorithms for building artificial intelligence applications. (LAP LAMBERT Academic Publishing, 2012).
- Kovalerchuk, B. & Vityaev, E. in Data Min. Knowl. Discov. Handb. (Maimon, O. & Rokach, L.) 1203–1224 (Springer US, 2005).
- Jurafsky, D. & Martin, J. H. Speech and language processing: an introduction to natural language processing, computational linguistics and speech recognition. (Pearson Prentice Hall, 2009).
-
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).
(
10.1063/1.2755487
) / Rev. Sci. Instrum. by C Long (2007) -
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).
(
10.1063/1.3216809
) / Rev. Sci. Instrum. by C Long (2009) -
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).
(
10.1107/S0021889804000391
) / J. Appl. Crystallogr. by G Barr (2004) -
Hunter, D. et al. Giant magnetostriction in annealed Co1-xFex thin-films. Nat. Commun. 2, 518 (2011).
(
10.1038/ncomms1529
) / Nat. Commun. by D Hunter (2011) -
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).
(
10.1557/jmr.2012.314
) / J. Mater. Res. by D Kan (2012) -
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).
(
10.1063/1.4775581
) / Appl. Phys. Lett. by T Gao (2013) -
Sourmail, T. Near equiatomic FeCo alloys: Constitution, mechanical and magnetic properties. Prog. Mater. Sci. 50, 816–880 (2005).
(
10.1016/j.pmatsci.2005.04.001
) / Prog. Mater. Sci. by T Sourmail (2005) -
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).
(
10.1109/TIT.1975.1055330
) / IEEE Trans. Inf. Theory by K Fukunaga (1975) -
Baumes, L. A., Moliner, M., Nicoloyannis, N. & Corma, A. A reliable methodology for high throughput identification of a mixture of crystallographic phases from powder X-ray diffraction data. CrystEngComm 10, 1321–1324 (2008).
(
10.1039/b812395k
) / CrystEngComm by LA Baumes (2008) -
LeBras, R. et al. in Princ. Pract. Constraint Program. 2011 508–522 (Springer, 2011).
(
10.1007/978-3-642-23786-7_39
) - Mueller, T., Kusne, A. G. & Ramprasad, R. Machine learning in materials science: Recent progress and emerging applications. Rev. Comput. Chem. (Accepted for publication).
-
Comaniciu, D. & Meer, P. Mean shift: A robust approach toward feature space analysis. Pattern Anal. Mach. Intell. IEEE Trans. On 24, 603–619 (2002).
(
10.1109/34.1000236
) / Pattern Anal. Mach. Intell. IEEE Trans. On by D Comaniciu (2002) -
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).
(
10.1007/s11837-012-0351-z
) / JOM by MJ Kramer (2012) -
Burkert, T., Nordström, L., Eriksson, O. & Heinonen, O. Giant Magnetic Anisotropy in Tetragonal FeCo Alloys. Phys. Rev. Lett. 93, 027203 (2004).
(
10.1103/PhysRevLett.93.027203
) / Phys. Rev. Lett. by T Burkert (2004) -
Andersson, G. et al. Perpendicular Magnetocrystalline Anisotropy in Tetragonally Distorted Fe-Co Alloys. Phys. Rev. Lett. 96, 037205 (2006).
(
10.1103/PhysRevLett.96.037205
) / Phys. Rev. Lett. by G Andersson (2006) -
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).
(
10.1103/PhysRevB.80.064415
) / Phys. Rev. B by F Yildiz (2009) -
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).
(
10.1063/1.108074
) / Appl. Phys. Lett. by D Weller (1992) -
Deaven, D. & Ho, K. Molecular geometry optimization with a genetic algorithm. Phys. Rev. Lett. 75, 288–291 (1995).
(
10.1103/PhysRevLett.75.288
) / Phys. Rev. Lett. by D Deaven (1995) -
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).
(
10.1039/c004096g
) / Phys. Chem. Chem. Phys. by M Ji (2010) -
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).
(
10.1103/PhysRevB.88.024404
) / Phys. Rev. B by L Ke (2013) -
Curtarolo, S. et al. AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations. Comput. Mater. Sci. 58, 227–235 (2012).
(
10.1016/j.commatsci.2012.02.002
) / Comput. Mater. Sci. by S Curtarolo (2012) - Krause, E. F. Taxicab Geometry: An Adventure in Non-Euclidean Geometry. (Dover Publications, 1987).
-
Takeuchi, I. et al. Identification of novel compositions of ferromagnetic shape-memory alloys using composition spreads. Nat. Mater. 2, 180–184 (2003).
(
10.1038/nmat829
) / Nat. Mater. by I Takeuchi (2003) -
Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 117, 1–19 (1995).
(
10.1006/jcph.1995.1039
) / J. Comput. Phys. by S Plimpton (1995) -
Zhou, X., Johnson, R. & Wadley, H. Misfit-energy-increasing dislocations in vapor-deposited CoFe/NiFe multilayers. Phys. Rev. B 69, 144113 (2004).
(
10.1103/PhysRevB.69.144113
) / Phys. Rev. B by X Zhou (2004) -
Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133–A1138 (1965).
(
10.1103/PhysRev.140.A1133
) / Phys. Rev. by W Kohn (1965) -
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).
(
10.1103/PhysRevB.54.11169
) / Phys. Rev. B by G Kresse (1996) -
Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953 (1994).
(
10.1103/PhysRevB.50.17953
) / Phys. Rev. B by PE Blöchl (1994) -
Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758–1775 (1999).
(
10.1103/PhysRevB.59.1758
) / Phys. Rev. B by G Kresse (1999) -
Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).
(
10.1103/PhysRevLett.77.3865
) / Phys. Rev. Lett. by JP Perdew (1996) -
Monkhorst, H. J. & Pack, J. D. Special points for Brillouin-zone integrations. Phys. Rev. B 13, 5188–5192 (1976).
(
10.1103/PhysRevB.13.5188
) / Phys. Rev. B by HJ Monkhorst (1976)
Dates
Type | When |
---|---|
Created | 10 years, 11 months ago (Sept. 15, 2014, 5:04 a.m.) |
Deposited | 2 years, 7 months ago (Jan. 6, 2023, 2:47 a.m.) |
Indexed | 4 days, 11 hours ago (Aug. 21, 2025, 2:07 p.m.) |
Issued | 10 years, 11 months ago (Sept. 15, 2014) |
Published | 10 years, 11 months ago (Sept. 15, 2014) |
Published Online | 10 years, 11 months ago (Sept. 15, 2014) |
@article{Kusne_2014, title={On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets}, volume={4}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/srep06367}, DOI={10.1038/srep06367}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Kusne, Aaron Gilad and Gao, Tieren and Mehta, Apurva and Ke, Liqin and Nguyen, Manh Cuong and Ho, Kai-Ming and Antropov, Vladimir and Wang, Cai-Zhuang and Kramer, Matthew J. and Long, Christian and Takeuchi, Ichiro}, year={2014}, month=sep }