Applying Bayesian Inference and Deterministic Anisotropy to Retrieve the Molecular Structure from Gas-Phase Diffraction Experiments
Published in Communications Physics, 6, 325 (2023), 2023
Recommended citation: K. Hegazy, V. Makhija, et al. (2023). "Applying Bayesian Inference and Deterministic Anisotropy to Retrieve the Molecular Structure from Gas-Phase Diffraction Experiments." Communications Physics (Nature), 6, 325. https://www.nature.com/articles/s42005-023-01448-x
A novel Bayesian inference framework (BIGR) for inverting gas-phase diffraction data to recover molecular-frame structure distributions.
- Improved 3D molecular imaging resolution by 100×, while removing the simulation bottleneck.
- Independently derived the Bayesian framework and the underlying physics theory.
- Addresses a 50-year-old inverse problem in ultrafast molecular imaging.