Author Correction: Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics (npj Computational Materials, (2020), 6, 1, (9), 10.1038/s41524-020-0277-x)

Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41524-020-00349-9

Title:	Author Correction: Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics (npj Computational Materials, (2020), 6, 1, (9), 10.1038/s41524-020-0277-x)
Authors:	Ren, Z Oviedo, F Thway, M Tian, SIP Wang, Y Xue, H Perea, JD Layurova, M Heumueller, T Birgersson, E Aberle, AG Brabec, CJ Stangl, R Li, Q Sun, S Lin, F Peters, IM Buonassisi, T
Issue Date:	1-Dec-2020
Publisher:	Nature Research
Citation:	Ren, Z, Oviedo, F, Thway, M, Tian, SIP, Wang, Y, Xue, H, Perea, JD, Layurova, M, Heumueller, T, Birgersson, E, Aberle, AG, Brabec, CJ, Stangl, R, Li, Q, Sun, S, Lin, F, Peters, IM, Buonassisi, T (2020-12-01). Author Correction: Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics (npj Computational Materials, (2020), 6, 1, (9), 10.1038/s41524-020-0277-x). npj Computational Materials 6 (1). ScholarBank@NUS Repository. https://doi.org/10.1038/s41524-020-00349-9
Abstract:	© 2020, The Author(s). An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Source Title:	npj Computational Materials
URI:	https://scholarbank.nus.edu.sg/handle/10635/171573
ISSN:	20573960
DOI:	10.1038/s41524-020-00349-9
Appears in Collections:	Staff Publications Elements

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