Scientific Reports (Dec 2020)

Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

  • Yuta Suzuki,
  • Hideitsu Hino,
  • Takafumi Hawai,
  • Kotaro Saito,
  • Masato Kotsugi,
  • Kanta Ono

DOI
https://doi.org/10.1038/s41598-020-77474-4
Journal volume & issue
Vol. 10, no. 1
pp. 1 – 11

Abstract

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Abstract Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.