  The Computational Crystallography Toolbox (CCTBX) is open-source software
that allows for processing of crystallographic data, including from serial
femtosecond crystallography (SFX), for macromolecular structure determination.
We aim to use the modules in CCTBX to determine the oxidation state of
individual metal atoms in a macromolecule. Changes in oxidation state are
reflected in small shifts of the atom's X-ray absorption edge. These energy
shifts can be extracted from the diffraction images recorded in serial
femtosecond crystallography, given knowledge of a forward physics model.
However, as the diffraction changes only slightly due to the absorption edge
shift, inaccuracies in the forward physics model make it extremely challenging
to observe the oxidation state. In this work, we describe the potential impact
of using self-supervised deep learning to correct the scientific model in CCTBX
and provide uncertainty quantification. We provide code for forward model
simulation and data analysis, built from CCTBX modules, at
https://github.com/gigantocypris/SPREAD , which can be integrated with machine
learning. We describe open questions in algorithm development to help spur
advances through dialog between crystallographers and machine learning
researchers. New methods could help elucidate charge transfer processes in many
reactions, including key events in photosynthesis.
