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

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Embedding physics domain-published-open access.pdfPublished version1.74 MBAdobe PDF

OPEN

PublishedView/Download

Page view(s)

51
checked on Jan 15, 2021

Download(s)

1
checked on Jan 15, 2021

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.