@inproceedings{4a696277bc5c45d4b81ec05046f3e868,
title = "Predicting Molecular Properties Using Photonic Chip-Based Machine Learning Approach",
abstract = "The intensive neural network architecture for molecules resulted in exponential growth in computation cost. Photonic chip technology offers an alternative platform with faster processing. We apply an optical neural chip to predict multiple quantum mechanical properties of molecules.",
author = "J. Lau and H. Zhang and L. Wan and L. Shi and Lee, {C. K.} and Kwek, {L. C.} and Liu, {A. Q.}",
note = "Funding Information: This work was supported by the Singapore Ministry of Education (MOE) Tier 3 grant (MOE2017-T3-1-001), the Singapore National Research Foundation (NRF) National Natural Science Foundation of China (NSFC) joint grant (NRF2017NRF-NSFC002-014). Publisher Copyright: {\textcopyright} Optica Publishing Group 2022, {\textcopyright} 2022 The Author(s); 2022 Conference on Lasers and Electro-Optics, CLEO 2022 ; Conference date: 15-05-2022 Through 20-05-2022",
year = "2022",
month = may,
language = "English",
series = "2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings",
}