Compositional neural networks for multimodal X-ray and neutron scattering inversion

Key points

• Here we introduce a modular framework for probabilistic multi-measurement analysis based on prior-amortized flow matching.1
• Our single-observation demonstrations span X-ray, time-of-flight neutron and polarized neutron reflectometry.1
• In separate co-refinement studies, task-matched estimators enable homogeneous composition across multiple neutron contrasts and heterogeneous multimodal composition of X-ray and neutron measurements.1
• Together, these results establish compositional flow matching as a modular approach to uncertainty-aware inversion and co-refinement, with potential applicability to other scattering problems in which multiple observations constrain a common physical structure while retaining measurement-specific parameters.1

Sources (1)

• [1]Compositional neural networks for multimodal X-ray and neutron scattering inversion
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 07:54 AM

  X-ray and neutron scattering are indispensable for structural characterization across condensed-matter physics and materials science.

  Here we introduce a modular framework for probabilistic multi-measurement analysis based on prior-amortized flow matching.

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