Paper collection about IR to molecule
Machine learning has significantly advanced the application of chemistry in recent years. Despite growing interest from machine learning experts, the adoption of these models into routine use in analytical chemistry remains limited. Infrared (IR) spectroscopy, one of the most cost-effective, straightforward, and widely accessible analytical tools for chemists, has primarily been used to identify specific functional groups with known vibrational frequencies. However, interpreting the majority of IR spectroscopy peaks still exceeds human capabilities.
Machine learning, particularly in the realm of language modeling, has demonstrated significant potential in chemistry. These models are applied across various tasks, from predicting retrosynthetic pathways and designing new drug candidates to facilitating the automation of experimental processes. In the area of IR spectroscopy, machine learning has further enhanced the analysis and processing of spectral data.
This repository is dedicated to curating a collection of research papers focused on IR2Molecule use machine learning
Contributions to this repository are welcome!
If you have come across relevant resources, feel free to open an issue or submit a pull request.
- (*conference|journal*) paper_name [[pdf](link)][[code](link)]
(MDPI 2023.8.7)Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning paper
(Chemical Science 10 Dec 2019) Spectral deep learning for prediction and prospective validation of functional groups paper code
(Anal. Chem June 30, 2021) Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models paper code
(Chemical Science 2022.10.25)Automatic materials characterization from infrared spectra using convolutional neural networks paper code
(ChemRxiv 2023.5.15) Leveraging Infrared Spectroscopy for Automated Structure Elucidationpaper code