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Inferring colloidal interaction from scattering by machine learning
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DOI：10.1016/j.cartre.2023.100252.png)
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摘要
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A machine learning solution for the potential inversion problem in elastic scattering is outlined. The inversion scheme consists of two major components, a generative network featuring a variational autoencoder which ex-tracts the targeted static two-point correlation functions from experimentally measured scattering cross sections, and a Gaussian process framework which probabilistically infers the relevant structural parameters from the in-verted correlation functions. Via a case study of charged colloidal suspensions, the feasibility of this approach for quantitative study of molecular interaction is critically benchmarked and its merit over existing deterministic approaches, in terms of numerical accuracy and computationally efficiency, is demonstrated.
Keyword:
Neutron scattering
Machine learning
Soft matter
Large-scale simulations
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3.9
论文数:
682
被引数:
1.3K
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A machine learning inversion scheme for determining interaction from scattering确定散射相互作用的机器学习反演方案
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