A Fisher-Geometric Approach to Supercell Pair Distribution Function Refinement

Abstract

Refining atomistic supercells against powder pair distribution functions (PDFs) requires optimizing many coupled atomic coordinates using a much smaller set of radial measurements. Reverse-mode differentiation makes gradients accessible for large systems, but standard first-order optimizers do not explicitly use coupled PDF sensitivities to precondition coordinate updates. We introduce a matrix-free, Fisher-geometric approach that pulls the information metric of the observation likelihood back onto atomic coordinates. The resulting update weights collective displacements according to their PDF response and experimental uncertainty. Under isotropic damping, the update is obtained by solving a damped linear system over $M$ PDF channels, using automatic differentiation to evaluate Jacobian--vector and vector--Jacobian products without constructing the full Jacobian or coordinate curvature matrix. For Gaussian measurement errors, the update recovers classical Levenberg--Marquardt refinement while avoiding dense coordinate-space factorization. The same computational framework accommodates correlated Gaussian errors and robust likelihood-based updates. This approach combines likelihood-aware collective updates with matrix-free backpropagation for direct second-order refinement of large atomistic supercells.