Journal
SCIENTIFIC REPORTS
Volume 10, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-020-77474-4
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Funding
- JST-Mirai Program [JPMJMI19G1]
- JST ACT-I [JPMJPR18UE]
- Elements Strategy Initiative Center for Magnetic Materials (ESICMM) through the Ministry of Education, Culture, Sports, Science and Technology (MEXT) [12016013]
- JST CREST [JPMJCR1761]
- European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant [701647]
- Toyota Motor Corporation
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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.
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