Hostname: page-component-89b8bd64d-mmrw7 Total loading time: 0 Render date: 2026-05-05T19:07:25.978Z Has data issue: false hasContentIssue false
  • English
  • Français

Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow

Published online by Cambridge University Press:  07 January 2020

Ming-Ching Chang ,
Yi Wei ,
Wei-Ren Chen  and
Changwoo Do Open the ORCID record for Changwoo Do [Opens in a new window]
Ming-Ching Chang
Affiliation:
University at Albany, State University of New York, New York, NY, USA
Yi Wei
Affiliation:
University at Albany, State University of New York, New York, NY, USA
Wei-Ren Chen
Affiliation:
Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Changwoo Do*
Affiliation:
Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
*
Address all correspondence to Changwoo Do at doc1@ornl.gov
Get access

Abstract

The authors propose an alternative route to circumvent the limitation of neutron flux using the recent deep learning super-resolution technique. The feasibility of accelerating data collection has been demonstrated by using small-angle neutron scattering (SANS) data collected from the EQ-SANS instrument at Spallation Neutron Source (SNS). Data collection time can be reduced by increasing the size of binning of the detector pixels at the sacrifice of resolution. High-resolution scattering data is then reconstructed by using a deep learning-based super-resolution method. This will allow users to make critical decisions at a much earlier stage of data collection, which can accelerate the overall experimental workflow.

Information

Type
Research Letters
Information
MRS Communications , Volume 10 , Issue 1 , March 2020 , pp. 11 - 17
Check for updates
Copyright
Copyright © Materials Research Society 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.Lindner, P. and Zemb, T. (eds): Neutrons, X-Rays and Light: Scattering Methods Applied to Soft Condensed Matter (North-Holland, The Netherlands, 2002).Google Scholar
2.Richter, D., Monkenbusch, M., Arbe, A., and Colmenero, J.: Neutron Spin Echo in Polymer Systems, Vol. 174 (Springer, Berlin, Heidelberg, 2005), p. 1.CrossRefGoogle Scholar
3.Narayanan, T., Wacklin, H., Konovalov, O., and Lund, R.: Recent applications of synchrotron radiation and neutrons in the study of soft matter. Crystallogr. Rev. 23, 160 (2017).CrossRefGoogle Scholar
4.Milne, C.J., Penfold, T.J., and Chergui, M.: Recent experimental and theoretical developments in time-resolved X-ray spectroscopies. Coord. Chem. Rev. 277–278, 44 (2014).CrossRefGoogle Scholar
5.Granroth, G.E., An, K., Smith, H.L., Whitfield, P., Neuefeind, J.C., Lee, J., Zhou, W., Sedov, V.N., Peterson, P.F., Parizzi, A., Skorpenske, H., Hartman, S.M., Huq, A., and Abernathy, D.L.: Event-based processing of neutron scattering data at the Spallation Neutron Source. J. Appl. Crystallogr. 51, 616 (2018).CrossRefGoogle Scholar
6.Lund, R., Willner, L., Richter, D., Iatrou, H., Hadjichristidis, N., Lindner, P., and IUCr: Unraveling the equilibrium chain exchange kinetics of polymeric micelles using small-angle neutron scattering—architectural and topological effects. J. Appl. Crystallogr. 40, s327 (2007).CrossRefGoogle Scholar
7.Bruetzel, L.K., Walker, P.U., Gerling, T., Dietz, H., and Lipfert, J.: Time-resolved small-angle X-ray scattering reveals millisecond transitions of a DNA origami switch. Nano Lett. 18, 2672 (2018).CrossRefGoogle ScholarPubMed
8.Sauter, A., Roosen-Runge, F., Zhang, F., Lotze, G., Jacobs, R.M.J., and Schreiber, F.: Real-time observation of nonclassical protein crystallization kinetics. J. Am. Chem. Soc. 137, 1485 (2015).CrossRefGoogle ScholarPubMed
9.Vegso, K., Siffalovic, P., Jergel, M., Nadazdy, P., Nadazdy, V., and Majkova, E.: Kinetics of polymer–fullerene phase separation during solvent annealing studied by table-top X-ray scattering. ACS Appl. Mater. Interfaces 9, 8241 (2017).CrossRefGoogle ScholarPubMed
10.Taylor, A., Dunne, M., Bennington, S., Ansell, S., Gardner, I., Norreys, P., Broome, T., Findlay, D., and Nelmes, R.: A route to the brightest possible neutron source? Science 315, 1092 (2007).CrossRefGoogle ScholarPubMed
11.Wang, Z., Chen, J., and Hoi, S.C.H.: Deep Learning for Image Super-Resolution: A Survey (2019). arXiv:1902.06068 [Cs.CV].Google Scholar
12.Yang, J.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19, 2861 (2010).Google ScholarPubMed
13.Dong, W., Zhang, L., Shi, G., and Wu, X.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20, 1838 (2011).CrossRefGoogle ScholarPubMed
14.Kim, K.I. and Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1127 (2010).Google ScholarPubMed
15.Yang, J., Lin, Z., and Cohen, S.: Fast image super-resolution based on in-place example regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1059 (2013).CrossRefGoogle Scholar
16.LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning. Nature 521, 436 (2015).CrossRefGoogle ScholarPubMed
17.Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. IEEE Conference on Computer Vision and Pattern Recognition 1874 (2016).CrossRefGoogle Scholar
18.Krizhevsky, A., Sutskever, I., and Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 1097 (2012).Google Scholar
19.Chen, Y. and Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1256 (2017).CrossRefGoogle ScholarPubMed
20.Dong, C., Loy, C.C., He, K., and Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295 (2016).CrossRefGoogle ScholarPubMed
21.Heller, W.T., Cuneo, M., Debeer-Schmitt, L., Do, C., He, L., Heroux, L., Littrell, K., Pingali, S.V., Qian, S., Stanley, C., Urban, V.S., Wu, B., Bras, W., and IUCr: The suite of small-angle neutron scattering instruments at Oak Ridge National Laboratory. J. Appl. Crystallogr. 51, 242 (2018).CrossRefGoogle Scholar
22.Shelhamer, E., Long, J., and Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640 (2017).CrossRefGoogle ScholarPubMed
23.Shi, W., Caballero, J., Theis, L., Huszar, F., Aitken, A., Ledig, C., and Wang, Z.: Is the Deconvolution Layer the Same as a Convolutional Layer? (2016). arXiv:1609.07009 [Cs.CV].Google Scholar
24.Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A.: Automatic differentiation in PyTorch. In NIPS-W, Long Beach, USA (2017).Google Scholar
25.Zhao, J.K., Gao, C.Y., and Liu, D.: The extended Q-range small-angle neutron scattering diffractometer at the SNS. J. Appl. Cryst. 43, 1068 (2010).CrossRefGoogle Scholar
26.Castro-Roman, F., Porcar, L., Porte, G., and Ligoure, C.: Quantitative analysis of lyotropic lamellar phases SANS patterns in powder oriented samples. Eur. Phys. J. E 18, 259 (2005).CrossRefGoogle ScholarPubMed
27.Doe, C., Jang, H.-S., Kline, S.R., and Choi, S.-M.: Subdomain structures of lamellar and reverse hexagonal pluronic ternary systems investigated by small-angle neutron scattering. Macromolecules 42, 2645 (2009).CrossRefGoogle Scholar
28.Wang, Z., Iwashita, T., Porcar, L., Wang, Y., Liu, Y., Sanchez-Diaz, L.E., Wu, B., Egami, T., and Chen, W.-R.: Dynamically Correlated Region in Sheared Colloidal Glasses Revealed by Neutron Scattering (2017). arXiv:1709.07507.Google Scholar
29.López-Barrón, C.R., Zeng, Y., Schaefer, J.J., Eberle, A.P.R., Lodge, T.P., and Bates, F.S.: Molecular alignment in polyethylene during cold drawing using in-situ SANS and Raman spectroscopy. Macromolecules 50, 3627 (2017).CrossRefGoogle Scholar
30.Mortensen, K.: Structural studies of aqueous solutions of PEO—PPO—PEO triblock copolymers, their micellar aggregates and mesophases; a small-angle neutron scattering study. J. Phys. Condens. Matter 8, A103 (1996).CrossRefGoogle Scholar
31.Wang, Z., Lam, C.N., Chen, W.-R., Wang, W., Liu, J., Liu, Y., Porcar, L., Stanley, C.B., Zhao, Z., Hong, K., and Wang, Y.: Fingerprinting molecular relaxation in deformed polymers. Phys. Rev. X 7, 031003 (2017).Google Scholar
32.Huang, G.-R., Wang, Y., Wu, B., Wang, Z., Do, C., Smith, G.S., Bras, W., Porcar, L., Falus, P., and Chen, W.-R.: Reconstruction of three-dimensional anisotropic structure from small-angle scattering experiments. Phys. Rev. E 96, 022612 (2017).CrossRefGoogle ScholarPubMed