Computational Reverse Engineering Analysis of Scattering Experiments Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929293260488704 |
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| author | Akepati, Sri Vishnuvardhan Reddy Gupta, Nitant Jayaraman, Arthi |
| author_facet | Akepati, Sri Vishnuvardhan Reddy Gupta, Nitant Jayaraman, Arthi |
| contents | 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 θ. 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,θ). In this paper, we present a new method called CREASE-2D that interprets I(q,θ) 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 machine learning (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,θ) profile. The CREASE-2D workflow identifies the structural features whose computed I(q,θ) profiles match the input experimental I(q,θ). 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computational Reverse Engineering Analysis of Scattering Experiments Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D) Akepati, Sri Vishnuvardhan Reddy Gupta, Nitant Jayaraman, Arthi Soft Condensed Matter 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 θ. 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,θ). In this paper, we present a new method called CREASE-2D that interprets I(q,θ) 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 machine learning (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,θ) profile. The CREASE-2D workflow identifies the structural features whose computed I(q,θ) profiles match the input experimental I(q,θ). 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. |
| title | Computational Reverse Engineering Analysis of Scattering Experiments Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D) |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2401.12381 |