# A universal synthetic dataset for machine learning on spectroscopic data

arXiv: https://arxiv.org/abs/2206.06031 · HTML: https://mlsanity.com/paper/2206.06031/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution](https://mlsanity.com/paper/1708.09022/) (2017)
- [Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning](https://mlsanity.com/paper/1901.07666/) (2019)
- [Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy](https://mlsanity.com/paper/2001.04813/) (2020)
- [A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra](https://mlsanity.com/paper/2103.16664/) (2021)

## Built on by (most cited first, 3 of 3)

- [Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching](https://mlsanity.com/paper/2402.04924/) (2024)
- [Weak-signal extraction enabled by deep-neural-network denoising of diffraction data](https://mlsanity.com/paper/2209.09247/) (2022)
- [Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms](https://mlsanity.com/paper/2303.11699/) (2023)

## Abstract (arXiv)

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.