Reconstructing the structure of thin films and multilayers from measurements of scattered X-rays or neutrons is key to progress in physics, chemistry, and biology. However, finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms, which typically results in unreliable analysis with only a single potential solution identified. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds, setting new standards in reflectometry. Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors that inform the inference network about known structural properties and controllable experimental conditions. PANPE networks support key scenarios such as high-throughput sample characterization, real-time monitoring of evolving structures, or the co-refinement of several experimental data sets, and can be adapted to provide fast, reliable, and flexible inference across many other inverse problems.

翻译：从散射X射线或中子测量中重建薄膜和多层膜结构是物理学、化学和生物学领域取得进展的关键。然而，对于标准算法而言，寻找所有与反射率数据兼容的结构在计算上是难以实现的，这通常导致不可靠的分析，仅能识别出单一潜在解。我们通过一种概率深度学习方法解决了这种可靠性不足的问题，该方法能在数秒内识别所有现实结构，为反射率测量设立了新标准。我们的方法——先验摊销神经后验估计（PANPE），将基于模拟的推理与新颖的自适应先验相结合，这些先验能够向推理网络传递已知结构特性和可控实验条件的信息。PANPE网络支持关键应用场景，如高通量样品表征、演化结构的实时监测或多个实验数据集的联合精修，并且可进行调整，为许多其他反问题提供快速、可靠且灵活的推理。