Self-Supervised Deep Learning for Model Correction in the Computational Crystallography Toolbox

TL;DR

This paper explores using self-supervised deep learning to improve the accuracy of physical models in crystallography, specifically for detecting oxidation states of metal atoms via X-ray absorption shifts, enhancing scientific insights.

Contribution

It introduces a framework for applying self-supervised deep learning to correct forward physics models in CCTBX for crystallography applications.

Findings

Potential to accurately determine oxidation states from diffraction data

Provides open-source code for model correction and uncertainty quantification

Highlights open questions for further algorithm development

Abstract

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.