TITLE: A universal synthetic dataset for machine learning on spectroscopic data 

ABSTRACT: To assist in the development of machine learning methods for automated
classification of spectroscopic data, we have generated a universal synthetic
dataset that can be used for model validation. This dataset contains artificial
spectra designed to represent experimental measurements from techniques
including X-ray diffraction, nuclear magnetic resonance, and Raman
spectroscopy. The dataset generation process features customizable parameters,
such as scan length and peak count, which can be adjusted to fit the problem at
hand. As an initial benchmark, we simulated a dataset containing 35,000 spectra
based on 500 unique classes. To automate the classification of this data, eight
different machine learning architectures were evaluated. From the results, we
shed light on which factors are most critical to achieve optimal performance
for the classification task. The scripts used to generate synthetic spectra, as
well as our benchmark dataset and evaluation routines, are made publicly
available to aid in the development of improved machine learning models for
spectroscopic analysis.
To assist in the development of machine learning methods for automated
classification of spectroscopic data, we have generated a universal synthetic
dataset that can be used for model validation. This dataset contains artificial
spectra designed to represent experimental measurements from techniques
including X-ray diffraction, nuclear magnetic resonance, and Raman
spectroscopy. The dataset generation process features customizable parameters,
such as scan length and peak count, which can be adjusted to fit the problem at
hand. As an initial benchmark, we simulated a dataset containing 35,000 spectra
based on 500 unique classes. To automate the classification of this data, eight
different machine learning architectures were evaluated. From the results, we
shed light on which factors are most critical to achieve optimal performance
for the classification task. The scripts used to generate synthetic spectra, as
well as our benchmark dataset and evaluation routines, are made publicly
available to aid in the development of improved machine learning models for
spectroscopic analysis.
A universal synthetic dataset for machine learning on spectroscopic data
http://arxiv.org/abs/2206.06031v2