Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/24808
Title: 
Authors: 
Issue Date: 
2022
Citation: 
Zhang, H., Lau, J. W. Z., Wan, L., Shi, L., Shi, Y., Cai, H., Luo, X., Lo, G.-Q., Lee, C.-K., Kwek, L. C., & Liu, A. Q. (2022). Molecular property prediction with photonic chip-based machine learning. Laser and Photonics Reviews. Advance online publication. https://doi.org/10.1002/lpor.202200698
Journal: 
Laser and Photonics Reviews
Abstract: 
Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever-increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural networks with faster data processing and lower energy usage compared to digital computers. Photonics technology is naturally capable of implementing complex-valued neural networks at no additional hardware cost. Here, the capability of photonic neural networks for predicting the quantum mechanical properties of molecules is demonstrated. To the best of knowledge, this work is the first to harness photonic technology for machine learning applications in computational chemistry and molecular sciences, such as drug discovery and materials design. It is further shown that multiple properties can be learned simultaneously in a photonic chip via a multi-task regression learning algorithm, which is also the first of its kind as well, as most previous works focus on implementing a network in the classification task.
URI: 
ISSN: 
1863-8880 (print)
1863-8899 (online)
DOI: 
File Permission: 
Embargo_20240101
File Availability: 
With file
Appears in Collections:Journal Articles

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  Until 2024-01-01
13 MBAdobe PDFUnder embargo until Jan 01, 2024
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