"Low-cost machine learning approach to the prediction of transition metal ..."

Gianmarco Terrones et al. (2022)

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DOI: 10.48550/ARXIV.2209.08595

access: open

type: Informal or Other Publication

metadata version: 2022-09-29

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  - https://dblp.org/rec/journals/corr/abs-2209-08595
Gianmarco Terrones, Chenru Duan, Aditya Nandy, Heather J. Kulik:
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties. CoRR abs/2209.08595 (2022)

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