  Experimentally obtained X-ray diffraction (XRD) patterns can be difficult to
solve, precluding the full characterization of materials, pharmaceuticals, and
geological compounds. Herein, we propose a method based upon a multi-objective
evolutionary search that uses both a structure's enthalpy and similarity to a
reference XRD pattern (constituted by a list of peak positions and their
intensities) to facilitate structure solution of inorganic systems. Because the
similarity index is computed for locally optimized cells that are subsequently
distorted to find the best match with the reference, this process transcends
both computational (e.g. choice of theoretical method, and 0 K approximation)
and experimental (e.g. external stimuli, and metastability) limitations. We
illustrate how the proposed methodology can be employed to successfully uncover
complex crystal structures by applying it to a range of test cases, including
inorganic minerals, pure elements ramp-compressed to extreme conditions, and
molecular crystals. The results demonstrate that our approach not only improves
the accuracy of structure prediction but also significantly reduces the time
required to achieve reliable solutions, thus providing a powerful tool for the
advancement of materials science and related fields.
