Powder X-Ray Diffraction Assisted Evolutionary Algorithm for Crystal Structure Prediction

TL;DR

This paper introduces a multi-objective evolutionary algorithm that combines enthalpy and XRD pattern similarity to improve crystal structure prediction, overcoming computational and experimental limitations.

Contribution

It presents a novel method integrating XRD pattern similarity into evolutionary algorithms for more accurate and efficient crystal structure prediction.

Findings

Successfully applied to inorganic minerals and molecular crystals.

Improves accuracy of structure prediction over traditional methods.

Reduces computational time for reliable solutions.

Abstract

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.