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Convolutional neural networks for grazing incidence x-ray scattering patterns: thin film structure identification

Published online by Cambridge University Press:  15 March 2019

Shuai Liu ,
Charles N. Melton ,
Singanallur Venkatakrishnan ,
Ronald J. Pandolfi ,
Guillaume Freychet ,
Dinesh Kumar ,
Haoran Tang ,
Alexander Hexemer  and
Daniela M. Ushizima Open the ORCID record for Daniela M. Ushizima [Opens in a new window]
Shuai Liu
Affiliation:
University of California, Berkeley, CA 94720, USA
Charles N. Melton
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Singanallur Venkatakrishnan
Affiliation:
Oak Ridge National Laboratory, Oakridge, TN 37830, USA
Ronald J. Pandolfi
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Guillaume Freychet
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Dinesh Kumar
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Haoran Tang
Affiliation:
University of California, Berkeley, CA 94720, USA
Alexander Hexemer
Affiliation:
Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
Daniela M. Ushizima*
Affiliation:
University of California, Berkeley, CA 94720, USA Lawrence Berkeley National Laboratory, 1 Cyclotron Rd. Berkeley, CA 94720, USA
*
Address all correspondence to Daniela M. Ushizima at [email protected]
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Abstract

Nano-structured thin films have a variety of applications from waveguides, gaseous sensors to piezoelectric devices. Grazing Incidence Small Angle x-ray Scattering images enable classification of such materials. One challenge is to determine structure information from scattering patterns alone. This paper highlights the design of multiple Convolutional Neural Networks (CNN) to classify nanoparticle orientation in a thin film by learning scattering patterns. The network was trained on several thin films with a success rate of 94%. We demonstrate CNN robustness under different noises as well as demonstrate the potential of our proposed approach as a strategy to decrease scattering pattern analysis time.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 586 - 592
Copyright
Copyright © Materials Research Society 2019 

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References

1.Dong, A., Chen, J., Vora, P.M., Kikkawa, J.M., and Murray, C.B.: Binary nanocrystal superlattice membrane self-assembled at the liquid-air interface. Nature 466 474 (2010).Google Scholar
2.Renaud, G., Lazzari, R., and Leroy, F.: Probing surface and interface morphology with Grazing Incidence Small Angle X-Ray Scattering. Surf. Sci. Rep. 64, 255–380 (2009).Google Scholar
3.Hexemer, A. and Müller-Buschbaum, P.: Advanced grazing-incidence techniques for modern soft-matter materials analysis. IUCrJ 2, 106–125 (2015).Google Scholar
4.Williams, T.E., Ushizima, D., Zhu, C., Anders, A., Milliron, D.J., and Helms, B.A.: Nearest-neighbor nanocrystal bonding dictates framework stability or collapse in colloidal nanocrystal frameworks. Chem. Commun. 53, 4853–4856 (2017).Google Scholar
5.Smilgies, D.: GISAXS – Grazing-Incidence Small-Angle Scattering. The SAXS Guide, 4th ed. (Anton Paar GmbH., Austria, 2017), pp. 109–123.Google Scholar
6.Vineyard, G.H.: Grazing-incidence diffraction and the distorted-wave approximation for the study of surfaces. Phys. Rev. B 26, 4146–4159 (1982).Google Scholar
7.Sinha, S.K., Sirota, E.B., Garoff, S., and Stanley, H.B.: X-ray and neutron scattering from rough surfaces. Phys. Rev. B 38, 2297–2311 (1988).Google Scholar
8.Deyhle, H., White, S.N., Botta, L., Liebi, M., Guizar-Sicairos, M., Bunk, O., and Müller, B.: Automated analysis of spatially resolved x-ray scattering and micro computed tomography of artificial and natural enamel carious lesions. J. Imaging 4, 81 (2018).Google Scholar
9.Rasmussen, C. E.: Gaussian processes in machine learning Advanced lectures in machine learning (Springer 2014) pp. 63–71.Google Scholar
10.Snoek, J., Larochelle, H., and Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. Adv. Neural. Inf. Process. Syst. (25), 2951–2959 (2012).Google Scholar
11.LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).Google Scholar
12.Krizhevsky, A., Sutskever, I., and Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012).Google Scholar
13.Araujo, F.H.D., Silva, R.R.V., Medeiros, F.N.S., Parkinson, D.D., Hexemer, A., Carneiro, C.M., and Ushizima, D.M.: Reverse image search for scientific data within and beyond the visible spectrum. Expert Syst. Appl. 109, 35–48 (2018).Google Scholar
14.Ling, J., Hutchinson, M., Antono, E., DeCost, B., Holm, E.A, and Meredig, B.: Building data-driven models with microstructure images: generalization and interpretability. Mater. Discov. 10, 19–28 (2017).Google Scholar
15.Pelt, D.M. and Sethian, J.A.: A mixed-scale dense convolutional neural network for image analysis. Proc. Natl. Acad. Sci. USA 115, 254–259 (2018).Google Scholar
16.Oliynik, A., Antono, E., Sparks, T., Ghadbeigi, L., Gaultois, M., Meredig, B., and Mar, A.: High-throughput machine-learning-driven synthesis of full-heusler compounds. Chem. Mater. 28, 7324–7331 (2016).Google Scholar
17.Douarre, C., Schielein, R., Frindel, C., Gerth, S., and Rousseau, D.: Transfer learning from synthetic data applied to soil-root segmentation in x-ray tomography images. J. Imaging 4, 65 (2018).Google Scholar
18.Kiapour, M.H., Yager, K., Berg, A.C., and Berg, T.L.: Materials discovery: fine-grained classification of x-ray scattering images. IEEE Winter Conference on Applications of Computer Vision, 933–940 (2014).Google Scholar
19.Wang, B., Yager, K., Yu, D., and Hoai, M.: X-ray scattering image classification using deep learning. IEEE Winter Conference on Applications of Computer Vision (WACV), 697–704 (2017).Google Scholar
20.Li, Y., Cheng, W., Yu, L.H., and Rainer, R.: Genetic algorithm enhanced by machine learning in dynamic aperture optimization. Phys. Rev. Accel. Beams 21, 054601 (2018).Google Scholar
21.Rossou, D., Burdet, P., de la Peña, F., Ducati, C., Knappett, B., Edward Henry Wheatley, A., and Anthony Midgley, P.: Multicomponent signal unmixing from nanoheterostructures: overcome the traditional challenges of nanoscale x-ray analysis via machine learning. Nanoletters 15(4), 2716–2720 (2015).Google Scholar
22.Laanait, N., Zhang, Z., and Schlepütz, C.M.: Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data. Nanotechnology 27, 374002 (2016).Google Scholar
23.Timoshenko, J., Lu, D., Lin, Y., and Frenkel, A.I.: Supervised machine-learning-based determination of three-dimensional structure of metallic nanoparticles. J. Phys. Chem. Lett. 8, 5091–5098 (2017).Google Scholar
24.Chourou, S.T., Sarje, A., Li, X.S., Chan, E.R., and Hexemer, A.: HipGISAXS: a high-performance computing code for simulating grazing-incidence x-ray scattering data. J. Appl. Crystallogr. 46, 1781–1795 (2013). https://hipgisaxs.github.io/.Google Scholar
25.Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning (MIT Press, Cambridge, MA, 2016), pp. 173–174.Google Scholar
26.Nasrabadi, N.M: Pattern recognition and machine learning. J. Electron. Imaging 16, 049901 (2007).Google Scholar
27.Simonyan, K. and Zisserman, A.: Very deep convolutional networks for large-scale image recognition. The International Conference on Learning Representations (ICLR), 1–14 (2015).Google Scholar
28.He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image recognition. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778 (2016).Google Scholar
29.Wold, S., Esbensen, K., and Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2, 37–52 (1987).Google Scholar
30.Ye, X., Zhu, C., Ercius, P., Raja, S.N., He, B., Jones, M.R., Hauwiller, M.R., Liu, Y., Xu, T., and Alivisatos, P.: Structural diversity in binary superlattices self-assembled from polymer-grafter nanocrystals. Nature 6, 1–10 (2015).Google Scholar
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