Digital Discovery, 2023, 2,1334-1346
DOI: 10.1039/D2DD00149G, Paper
DOI: 10.1039/D2DD00149G, Paper
Open Access
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun Lim, Armin G. Aberle, Benjamin P. MacLeod, Fraser G. L. Parlane, Curtis P. Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu
thicknessML predicts film thickness from reflection and transmission spectra. Transfer learning enables thickness prediction of different materials with good performance. Transfer learning also bridges the gap between simulation and experiment.
The content of this RSS Feed (c) The Royal Society of Chemistry
thicknessML predicts film thickness from reflection and transmission spectra. Transfer learning enables thickness prediction of different materials with good performance. Transfer learning also bridges the gap between simulation and experiment.
The content of this RSS Feed (c) The Royal Society of Chemistry