Unsupervised Machine Learning for Phase Identification and Characterization of High-Resolution STEM EELS in Novel Battery Materials

Zhen Yuan Yeo, Wenhui Lai, Jong Hak Lee, Deepan Balakrishnan, Barbaros Özyilmaz, N. Duane Loh

Keywords: Unsupervised Machine Learning, Phase Identification, High-Resolution STEM EELS, Novel Battery Materials

TL;DR: We used unsupervised ML to uncover hidden chemical phases in STEM-EELS battery data without reference databases, revealing noisy compositions (e.g. Si–C, Si–O). This can be extended to accelerate materials discovery with novel chemical compositions.

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Submission Number: 210