The lack of interfaces between crystallographic data and machine learning methods prevents the application of modern neural network frameworks to the crystal structure determination. Here we present \texttt{SFcalculator}, a differentiable pipeline to generate crystallographic data (structure factors) from atomic molecule structures with the bulk solvent model. This calculator fills the gap between the long-established crystallography field and the state-of-the-art deep learning algorithms. We discuss the correctness and performance of our \texttt{SFcalculator} by comparing with the current most-used tool \texttt{Phenix}. Finally, we demonstrate with an initial try that it makes possible the automated structure refinement in a well-regularized latent space defined by a deep generative model, which enables a more principled way to impose prior knowledge. We believe this tool paves the way towards a fully automated structure refinement and a possible end-to-end model, which is crucial for the next generation high throughput diffraction experiments.