Finally, the machine learning model could be used to aid experimentalists in the area of X-ray crystal structure refinement. Refinement techniques typically involve iteratively improving the agreement between experimental reflection data and some structural model. Due to computational limitations, most structural models rely on crude approximations such as the isolated atom model, which we have discussed does not accurately represent a structure's true electron density (Fig. ). The isolated atom model works reasonably well at intermediate resolutions but starts to show deficiencies for high-resolution images (better than 1.0 Å). Because computational scaling is less of an issue for machine learning models, they can be efficiently used to replace crude models with more sophisticated ones. The need for more sophisticated models in refinement will only become more prevalent as experimental techniques continue to improve and highresolution images become the standard.