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Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

Published online by Cambridge University Press:  24 February 2020

Changwoo Do ,
Wei-Ren Chen  and
Sangkeun Lee
Changwoo Do*
Affiliation:
Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, USA
Wei-Ren Chen
Affiliation:
Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, USA
Sangkeun Lee
Affiliation:
Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, USA
*
*Changwoo Do, doc1@ornl.gov
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Abstract

Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

Keywords

small angle scatteringdata analysismachine learning
Type
Articles
Information
MRS Advances , Volume 5 , Issue 29-30: Materials Theory, Computation and Characterization , 2020 , pp. 1577 - 1584
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Copyright
Copyright © Materials Research Society 2020

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References

References:

Breßler, I., Kohlbrecher, J., and Thünemann, A. F., J. Appl. Crystallogr. 48, 1587 (2015).CrossRefGoogle Scholar
Ilavsky, J., Jemian, P. R., and IUCr, , J. Appl. Crystallogr. 42, 347 (2009).CrossRefGoogle Scholar
Bressler, I., Pauw, B. R., Thünemann, A. F., and IUCr, , J. Appl. Crystallogr. 48, 962 (2015).CrossRefGoogle Scholar
Pauw, B. R., Pedersen, J. S., Tardif, S., Takata, M., Iversen, B. B., and IUCr, , J. Appl. Crystallogr. 46, 365 (2013).CrossRefGoogle Scholar
Förster, S., Apostol, L., Bras, W., and IUCr, , J. Appl. Crystallogr. 43, 639 (2010).CrossRefGoogle Scholar
Brookes, E., Vachette, P., Rocco, M., Pérez, J., and IUCr, , J. Appl. Crystallogr. 49, 1827 (2016).CrossRefGoogle Scholar
Nielsen, S. S., Toft, K. N., Snakenborg, D., Jeppesen, M. G., Jacobsen, J. K., Vestergaard, B., Kutter, J. P., Arleth, L., and IUCr, , J. Appl. Crystallogr. 42, 959 (2009).CrossRefGoogle Scholar
http://www.sasview.org/, (2019).Google Scholar
He, K., Zhang, X., Ren, S., and Sun, J., IEEE Conf. Comput. Vis. Pattern Recognit. 770 (2016).Google Scholar
Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu, T., Cheng-Yue, R., Mujica, F., Coates, A., and Ng, A. Y., (2015).Google Scholar
Bahdanau, D., Cho, K., and Bengio, Y., (2014).Google Scholar
Saito, K., Yano, M., Hino, H., Shoji, T., Asahara, A., Morita, H., Mitsumata, C., Kohlbrecher, J., and Ono, K., Sci. Rep. 9, 1526 (2019).CrossRefGoogle Scholar
Kiapour, M. H., Yager, K., Berg, A. C., and Berg, T. L., 2014 IEEE Winter Conf. Appl. Comput. Vision, WACV 2014 933 (2014).Google Scholar
Zhao, B., Greenberg, J. A., and Wolter, S., in Anom. Detect. Imaging with X-Rays III, edited by Ashok, A., Neifeld, M. A., Gehm, M. E., and Greenberg, J. A. (SPIE, 2018), p. 4.Google Scholar
Franke, D., Jeffries, C. M., and Svergun, D. I., Biophys. J. 114, 2485 (2018).CrossRefGoogle Scholar
Botu, V. and Ramprasad, R., Int. J. Quantum Chem. 115, 1074 (2015).CrossRefGoogle Scholar
Li, Z., Kermode, J. R., and De Vita, A., Phys. Rev. Lett. 114, 096405 (2015).CrossRefGoogle Scholar
Deringer, V. L., Bernstein, N., Bartók, A. P., Cliffe, M. J., Kerber, R. N., Marbella, L. E., Grey, C. P., Elliott, S. R., and Csányi, G., J. Phys. Chem. Lett. 9, 2879 (2018).CrossRefGoogle Scholar
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., and Walsh, A., Nature 559, 547 (2018).CrossRefGoogle Scholar
Meredig, B., Agrawal, A., Kirklin, S., Saal, J. E., Doak, J. W., Thompson, A., Zhang, K., Choudhary, A., and Wolverton, C., Phys. Rev. B 89, 094104 (2014).CrossRefGoogle Scholar
Liu, Y., Zhao, T., Ju, W., and Shi, S., J. Mater. 3, 159 (2017).Google Scholar
Sasmodels, , (2019).Google Scholar
Pedersen, J. S., Adv. Colloid Interface Sci. 70, 171 (1997).CrossRefGoogle Scholar
Feigin, L. A., Svergun, D. I., and Taylor, G. W., Structure Analysis by Small-Angle X-Ray and Neutron Scattering (n.d.).Google Scholar
Chen, W.-R., Butler, P. D., and Magid, L. J., Langmuir 22, 6539 (2006).CrossRefGoogle Scholar
Lindner, P. and Zemb, T., editors , Neutrons, X-Rays and Light: Scattering Methods Applied to Soft Condensed Matter (North-Holland, 2002).Google Scholar
Lemmich, J., Mortensen, K., Ipsen, J. H., Honger, T., Bauer, R., and Mouritsen, O. G., Phys. Rev. E 53, 5169 (1996).CrossRefGoogle Scholar
Pabst, G., Biophys. Rev. Lett. 1, 57 (2006).CrossRefGoogle Scholar
Lee, S., (2019).Google Scholar
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A., BMC Bioinformatics 9, 307 (2008).CrossRefGoogle Scholar