Machine learning-based SERS chemical space for two-way prediction of structures and spectra of untrained molecules

Machine learning-based SERS chemical space for two-way prediction of structures and spectra of untrained molecules

Identifying unknown molecules beyond existing databases remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy. Conventional SERS analysis relies on matching experimental and cataloged spectra, limiting identification to known molecules in databases. With a vast chemical space...

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Bibliographic Details
Main Authors: Chen, Jaslyn Ru Ting, Tan, Emily Xi, Tang, Jingxiang, Leong, Shi Xuan, Hue, Sean Kai Xun, Pun, Chi Seng, Phang, In Yee, Ling, Xing Yi
Other Authors: School of Chemistry, Chemical Engineering and Biotechnology
Format: Journal Article
Language:English
Published: 2025
Subjects:
Chemistry
SERS
Chemical space
Untrained molecules
Online Access:https://hdl.handle.net/10356/182548

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