Infrared spectroscopy data- and physics-driven machine learning …
Mar 23, 2020 · Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles...
Machine learning prediction of organic moieties from the IR …
Jan 31, 2024 · The integration of machine learning with infrared spectroscopy has shown promising results in addressing the challenges associated with interpreting spectroscopic data.
A Machine Learning Protocol for Predicting Protein Infrared …
Oct 30, 2020 · Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding.
Leveraging infrared spectroscopy for automated structure …
Nov 16, 2024 · We present a transformer model that enables chemists to leverage the complete information contained within an IR spectrum to directly predict the molecular structure. To cover a large chemical...
A Review of Machine Learning for Near-Infrared Spectroscopy
Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. First...
Exploring the Steps of Infrared (IR) Spectral Analysis: Pre …
Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample.
Infrared spectroscopy is a crucial analytical tool in organic chemistry, but interpreting IR data can be challenging. This study provides a comprehensive analysis of ve machine learning models: logistic regression, KNN (k-nearest neighbors), SVM
A Review of Machine Learning for Near-Infrared Spectroscopy
Dec 13, 2022 · Feature selection has been shown to be an effective and efficient data preprocessing technique for preparing data (especially high-dimensional data) for a variety of data mining and machine learning problems.
Predicting Infrared Spectra with Message Passing Neural Networks
May 28, 2021 · Infrared (IR) spectroscopy remains an important tool for chemical characterization and identification. Chemprop-IR has been developed as a software package for the prediction of IR spectra through the use of machine learning.
Enhancing spatial resolution in Fourier transform infrared spectral ...
Dec 20, 2023 · In this paper, we introduce an approach aimed at enhancing spatial resolution by mitigating noise present in spectral images acquired via FT-IR measurements, employing machine learning...