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Computational Reverse Engineering Analysis of Scattering Experiments Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)

Published 22 Jan 2024 in cond-mat.soft | (2401.12381v1)

Abstract: Characterization of structural diversity within soft materials is key for engineering new materials for various applications. Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales and typically outputs scattered intensity I(q) as a function of the scattered wavevector represented by its magnitude q and azimuthal angle {\theta}. While isotropic structures can be interpreted from azimuthally averaged 1D SAS profile, to understand anisotropic spatial arrangements, one has to interpret the 2D SAS profile, I(q,{\theta}). In this paper, we present a new method called CREASE-2D that interprets I(q,{\theta}) as is and outputs the relevant structural features. CREASE-2D is an extension of the 'computational reverse engineering analysis for scatting experiments' (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE uses a genetic algorithm for optimization and a surrogate ML model for fast calculation of 1D 'computed' scattering profiles that are then compared to the experimental 1D scattering profiles during optimization. In CREASE-2D, which goes beyond CREASE in interpretting 2D scattering profiles, we use XGBoost as the surrogate ML model to relate structural features to the I(q,{\theta}) profile. The CREASE-2D workflow identifies the structural features whose computed I(q,{\theta}) profiles match the input experimental I(q,{\theta}). We test the performance of CREASE-2D by using as input a variety of in silico 2D SAS profiles with known structural features and demonstrate that CREASE-2D converges towards their correct structural features. We expect this method will be valuable for materials' researchers who need direct interpretation of 2D scattering profiles to explore structural anisotropy.

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References (45)
  1. Data set of structural features and a few selected scattering profiles and structures prepared for crease-2d method. https://doi.org/10.5281/zenodo.10534943, 2024.
  2. Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry. Chem. Sci., 14:14003–14019, 2023.
  3. Computational reverse-engineering analysis for scattering experiments on amphiphilic block polymer solutions. Journal of the American Chemical Society, 141(37):14916–14930, 2019.
  4. Sasfit: a tool for small-angle scattering data analysis using a library of analytical expressions. Journal of applied crystallography, 48(5):1587–1598, 2015.
  5. Small-angle scattering of dense, polydisperse granular porous media: Computation free of size effects. Physical Review E, 87(1):013305, 2013.
  6. Diversity in genetic programming: An analysis of measures and correlation with fitness. IEEE Transactions on Evolutionary Computation, 8(1):47–62, 2004.
  7. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 785–794, New York, NY, USA, 2016. Association for Computing Machinery.
  8. Deok-Kee Choi. Data-driven materials modeling with xgboost algorithm and statistical inference analysis for prediction of fatigue strength of steels. International Journal of Precision Engineering and Manufacturing, 20:129–138, 2019.
  9. Three-dimensional protein structure prediction: Methods and computational strategies. Computational biology and chemistry, 53:251–276, 2014.
  10. Sasview version 4.1. Zenodo and http://www. sasview. org, 2017.
  11. Flow-sans and rheo-sans applied to soft matter. Current opinion in colloid & interface science, 17(1):33–43, 2012.
  12. Image generation: A review. Neural Processing Letters, 54(5):4609–4646, 2022.
  13. Machine learning methods for x-ray scattering data analysis from biomacromolecular solutions. Biophysical Journal, 114(11):2485–2492, 2018.
  14. Computational methods for high resolution prediction and refinement of protein structures. Current opinion in structural biology, 23(2):177–184, 2013.
  15. O. Glatter and O. Kratky. Small Angle X-ray Scattering. Academic Press, 1982.
  16. Xgboost model for electrocaloric temperature change prediction in ceramics. npj Computational Materials, 8(1):140, 2022.
  17. Structural and rheological aging in model attraction-driven glasses by rheo-sans. Soft Matter, 17(4):924–935, 2021.
  18. Structural modeling using solution small-angle x-ray scattering (saxs). Journal of molecular biology, 432(9):3078–3092, 2020.
  19. A. Guinier and G. Fournet. Small-angle Scattering of X-rays. Structure of matter series. Wiley, 1955.
  20. Computational approach for structure generation of anisotropic particles (casgap) with targeted distributions of particle design and orientational order. Nanoscale, 15:14958–14970, 2023.
  21. Boualem Hammouda. Probing nanoscale structures-the sans toolbox. National Institute of Standards and Technology, pages 1–717, 2008.
  22. Practical genetic algorithms. John Wiley & Sons, 2004.
  23. Computational reverse-engineering analysis for scattering experiments of assembled binary mixture of nanoparticles. ACS Materials Au, 1(2):140–156, 2021.
  24. Computational reverse-engineering analysis for scattering experiments for form factor and structure factor determination (“p (q) and s (q) crease”). JACS Au, 3(3):889–904, 2023.
  25. Computational reverse-engineering analysis for scattering experiments (crease) with machine learning enhancement to determine structure of nanoparticle mixtures and solutions. ACS Central Science, 8(7):996–1007, 2022.
  26. Small-angle x-ray and neutron scattering. Nature Reviews Methods Primers, 1(1):70, 2021.
  27. Hierarchical self-assembly of poly (d-glucose carbonate) amphiphilic block copolymers in mixed solvents. Macromolecules, 53(19):8581–8591, 2020.
  28. Structural characterization of biomaterials by means of small angle x-rays and neutron scattering (saxs and sans), and light scattering experiments. Molecules, 25(23):5624, 2020.
  29. Synergistic role of temperature and salinity in aggregation of nonionic surfactant-coated silica nanoparticles. Langmuir, 39(16):5917–5928, 2023.
  30. Stefano Marsili Libelli and P Alba. Adaptive mutation in genetic algorithms. Soft computing, 4:76–80, 2000.
  31. When do neural nets outperform boosted trees on tabular data?, 2023.
  32. Jan Skov Pedersen. Analysis of small-angle scattering data from colloids and polymer solutions: modeling and least-squares fitting. Advances in colloid and interface science, 70:171–210, 1997.
  33. Structural characterization of protein–polymer conjugates for biomedical applications with small-angle scattering. Current Opinion in Colloid & Interface Science, 42:157–168, 2019. X-Ray and Neutron Scattering.
  34. A strain-controlled rheosans instrument for the measurement of the microstructural, electrical, and mechanical properties of soft materials. Review of Scientific Instruments, 88(10), 2017.
  35. Increasing complexity in small-angle x-ray and neutron scattering experiments: from biological membrane mimics to live cells. Soft Matter, 17(2):222–232, 2021.
  36. Conditional image generation with pretrained generative model, 2023.
  37. A steel property optimization model based on the xgboost algorithm and improved pso. Computational Materials Science, 174:109472, 2020.
  38. Tree ensemble kernels for bayesian optimization with known constraints over mixed-feature spaces. Advances in Neural Information Processing Systems, 35:37401–37415, 2022.
  39. Characterizing polymer structure with small-angle neutron scattering: A tutorial. Journal of Applied Physics, 129(17), 2021.
  40. Computational reverse-engineering analysis of scattering experiments (crease) on amphiphilic block polymer solutions: cylindrical and fibrillar assembly. Macromolecules, 54(2):783–796, 2021.
  41. Machine learning enhanced computational reverse engineering analysis for scattering experiments (crease) to determine structures in amphiphilic polymer solutions. ACS Polymers Au, 1(3):153–164, 2021.
  42. arthijayaraman-lab/crease_ga: (v0.0.1)., 2021.
  43. Machine learning-enhanced computational reverse-engineering analysis for scattering experiments (crease) for analyzing fibrillar structures in polymer solutions. Macromolecules, 55(24):11076–11091, 2022.
  44. Computational reverse engineering analysis for scattering experiments (crease) on vesicles assembled from amphiphilic macromolecular solutions. JACS Au, 1(11):1925–1936, 2021.
  45. Application of xgboost algorithm in bearing fault diagnosis. In IOP Conference Series: Materials Science and Engineering, volume 490, page 072062. IOP Publishing, 2019.
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