Fast and reliable probabilistic reflectometry inversion with prior-amortized neural posterior estimation

Area of Science:

• Materials Science and Condensed Matter Physics
• Computational Chemistry
• Structural Biology

Background:

• Accurate reconstruction of thin film and multilayer structures is crucial for advancements in physics, chemistry, and biology.
• Standard algorithms for analyzing reflectometry data are computationally intensive, often yielding unreliable results with only a single solution.

Purpose of the Study:

• To develop a reliable and computationally efficient method for reconstructing thin film and multilayer structures from reflectometry data.
• To address the limitations of existing algorithms in identifying all compatible structural solutions.

Main Methods:

• Implementation of a probabilistic deep learning approach named prior-amortized neural posterior estimation (PANPE).
• Integration of simulation-based inference with adaptive priors to incorporate known structural properties and experimental conditions.
• Development of PANPE networks for rapid and comprehensive structural analysis.

Main Results:

• PANPE identifies all realistic structures compatible with reflectometry data in seconds, significantly improving analysis speed.
• The method enhances the reliability of structural analysis, moving beyond single-solution limitations.
• Demonstrated adaptability for high-throughput characterization, real-time monitoring, and multi-dataset refinement.

Conclusions:

• PANPE redefines standards in reflectometry analysis by providing fast, reliable, and flexible inference.
• The probabilistic deep learning method offers a powerful tool for diverse inverse problems beyond reflectometry.
• This approach facilitates significant progress in materials characterization and scientific discovery.