AutoSolveX: a Python-based pipeline for automated structure solution, refinement, and guest identification from diffraction data

Three-dimensional electron diffraction (3D ED) is a powerful technique for determining the structures from nanosized crystals. During a typical 3D ED data experiment, dozens of datasets can be acquired per single session. While data collection can be automated, structure solution and refinement remain key bottlenecks in structure determination and require monotonous manual work. A structure determination pipeline, AutoSolveX, with corresponding graphical user interface, was developed that automates structure solution and refinement for small molecules, and allows the identification and refinement of ordered guest molecules in porous materials with theoretical candidates. AutoSolveX provides phase separation and Laue group suggestion, supports atom-type reassignment and HKL transformations consistent with an input space group, and automatically generates refinement summaries and statistics tables for cross-dataset comparison. We demonstrate the functionality of AutoSolveX by batch structure solution and refinement of seven datasets of MOF-74 and guest identification based on MOF-520.