Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films

Please use this identifier to cite or link to this item: https://doi.org/10.1039/d2dd00149g

Title:	Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films
Authors:	Tian, Siyu Isaac Parker Ren, Zekun Venkataraj, Selvaraj Cheng, Yuanhang Bash, Daniil Oviedo, Felipe Jayavelu, Senthilnath Chellappan, Vijila Lim, Yee-Fun Aberle, Armin Gerhard MacLeod, Benjamin P Parlane, Fraser GL Berlinguette, Curtis P Li, Qianxiao Buonassisi, Tonio Liu, Zhe
Issue Date:	13-Jul-2023
Publisher:	Royal Society of Chemistry (RSC)
Citation:	Tian, Siyu Isaac Parker, Ren, Zekun, Venkataraj, Selvaraj, Cheng, Yuanhang, Bash, Daniil, Oviedo, Felipe, Jayavelu, Senthilnath, Chellappan, Vijila, Lim, Yee-Fun, Aberle, Armin Gerhard, MacLeod, Benjamin P, Parlane, Fraser GL, Berlinguette, Curtis P, Li, Qianxiao, Buonassisi, Tonio, Liu, Zhe (2023-07-13). Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films. Digital Discovery. ScholarBank@NUS Repository. https://doi.org/10.1039/d2dd00149g
Abstract:	Transfer learning (TL) increasingly becomes an important tool in handling data scarcity, especially when applying machine learning (ML) to novel materials science problems. In autonomous workflows to optimize optoelectronic thin...
Source Title:	Digital Discovery
URI:	https://scholarbank.nus.edu.sg/handle/10635/243541
ISSN:	2635-098X
DOI:	10.1039/d2dd00149g
Appears in Collections:	Staff Publications Elements

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