On Pattern Matching of X-Ray Powder Diffraction Data

We introduce a novel pattern matching algorithm optimized for X-ray powder diffraction (XRPD) data and
useful for data from other types of analytical techniques (e.g., Raman, IR). The algorithm is based on
hierarchical clustering with a similarity metric that compares peak positions using the full peak profile. It
includes heuristics developed from years of experience manually matching XRPD data, and preprocessing
algorithms that reduce the effects of common problems associated with XRPD (e.g., preferred orientation
and poor particle statistics). This algorithm can find immediate application in automated polymorph screening
and salt selection, common tasks in the development of pharmaceuticals.