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.
Similar content being viewed by others
References
I. Breßler, J. Kohlbrecher, and A. F. Thünemann, J. Appl. Crystallogr. 48, 1587 (2015).
J. Ilavsky, P. R. Jemian, and IUCr, J. Appl. Crystallogr. 42, 347 (2009).
I. Bressler, B. R. Pauw, A. F. Thünemann, and IUCr, J. Appl. Crystallogr. 48, 962 (2015).
B. R. Pauw, J. S. Pedersen, S. Tardif, M. Takata, B. B. Iversen, and IUCr, J. Appl. Crystallogr. 46, 365 (2013).
S. Förster, L. Apostol, W. Bras, and IUCr, J. Appl. Crystallogr. 43, 639 (2010).
E. Brookes, P. Vachette, M. Rocco, J. Pérez, and IUCr, J. Appl. Crystallogr. 49, 1827 (2016).
S. S. Nielsen, K. N. Toft, D. Snakenborg, M. G. Jeppesen, J. K. Jacobsen, B. Vestergaard, J. P. Kutter, L. Arleth, and IUCr, J. Appl. Crystallogr. 42, 959 (2009).
http://www.sasview.org/, (2019).
K. He, X. Zhang, S. Ren, and J. Sun, IEEE Conf. Comput. Vis. Pattern Recognit. 770 (2016).
B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, F. Mujica, A. Coates, and A. Y. Ng, (2015).
D. Bahdanau, K. Cho, and Y. Bengio, (2014).
K. Saito, M. Yano, H. Hino, T. Shoji, A. Asahara, H. Morita, C. Mitsumata, J. Kohlbrecher, and K. Ono, Sci. Rep. 9, 1526 (2019).
M. H. Kiapour, K. Yager, A. C. Berg, and T. L. Berg, 2014 IEEE Winter Conf. Appl. Comput. Vision, WACV 2014933 (2014).
B. Zhao, J. A. Greenberg, and S. Wolter, 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.
D. Franke, C. M. Jeffries, and D. I. Svergun, Biophys. J. 114, 2485 (2018).
V. Botu and R. Ramprasad, Int. J. Quantum Chem. 115, 1074 (2015).
Z. Li, J. R. Kermode, and A. De Vita, Phys. Rev. Lett. 114, 096405 (2015).
V. L. Deringer, N. Bernstein, A. P. Bartók, M. J. Cliffe, R. N. Kerber, L. E. Marbella, C. P. Grey, S. R. Elliott, and G. Csányi, J. Phys. Chem. Lett. 9, 2879 (2018).
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, Nature 559, 547 (2018).
B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, Phys. Rev. B 89, 094104 (2014).
Y. Liu, T. Zhao, W. Ju, and S. Shi, J. Mater. 3, 159 (2017).
Sasmodels, (2019).
J. S. Pedersen, Adv. Colloid Interface Sci. 70, 171 (1997).
L. A. Feigin, D. I. Svergun, and G. W. Taylor, Structure Analysis by Small-Angle X-Ray and Neutron Scattering (n.d.).
W.-R. Chen, P. D. Butler, and L. J. Magid, Langmuir 22, 6539 (2006).
P. Lindner and T. Zemb, editors, Neutrons, X-Rays and Light: Scattering Methods Applied to Soft Condensed Matter (North-Holland, 2002).
J. Lemmich, K. Mortensen, J. H. Ipsen, T. Honger, R. Bauer, and O. G. Mouritsen, Phys. Rev. E 53, 5169 (1996).
G. Pabst, Biophys. Rev. Lett. 1, 57 (2006).
S. Lee, (2019).
C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, BMC Bioinformatics 9, 307 (2008).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Do, C., Chen, WR. & Lee, S. Small Angle Scattering Data Analysis Assisted by Machine Learning Methods. MRS Advances 5, 1577–1584 (2020). https://doi.org/10.1557/adv.2020.130
Published:
Issue Date:
DOI: https://doi.org/10.1557/adv.2020.130