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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond

Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel, Yue Huang, Yujun Zhou, Mihir Surve, Zhichun Guo, Prayel Das, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang

TL;DR

The paper tackles the underexplored application of machine learning to spectroscopy, coalescing five major modalities (MS, NMR, IR, Raman, UV-Vis) under the umbrella of SpectraML. It proposes a unified framework that separates forward (molecule-to-spectrum) and inverse (spectrum-to-molecule) tasks, reviews data representations and neural architectures, and discusses the shift toward generative modeling and foundation models. Key contributions include a taxonomy of models (graph-based, transformer-based, and foundation-model driven), a synthesis of data preprocessing strategies, and an open-source repository of datasets and papers to promote reproducibility. The survey highlights synthetic data generation, large-scale pretraining, few-/zero-shot learning, and cross-modal integration as promising directions with potential to accelerate chemical discovery and materials design.

Abstract

The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data, referred to as Spectroscopy Machine Learning (SpectraML), remains relatively underexplored. Modern spectroscopic techniques (MS, NMR, IR, Raman, UV-Vis) generate an ever-growing volume of high-dimensional data, creating a pressing need for automated and intelligent analysis beyond traditional expert-based workflows. In this survey, we provide a unified review of SpectraML, systematically examining state-of-the-art approaches for both forward tasks (molecule-to-spectrum prediction) and inverse tasks (spectrum-to-molecule inference). We trace the historical evolution of ML in spectroscopy, from early pattern recognition to the latest foundation models capable of advanced reasoning, and offer a taxonomy of representative neural architectures, including graph-based and transformer-based methods. Addressing key challenges such as data quality, multimodal integration, and computational scalability, we highlight emerging directions such as synthetic data generation, large-scale pretraining, and few- or zero-shot learning. To foster reproducible research, we also release an open-source repository containing recent papers and their corresponding curated datasets (https://github.com/MINE-Lab-ND/SpectrumML_Survey_Papers). Our survey serves as a roadmap for researchers, guiding progress at the intersection of spectroscopy and AI.

Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond

TL;DR

The paper tackles the underexplored application of machine learning to spectroscopy, coalescing five major modalities (MS, NMR, IR, Raman, UV-Vis) under the umbrella of SpectraML. It proposes a unified framework that separates forward (molecule-to-spectrum) and inverse (spectrum-to-molecule) tasks, reviews data representations and neural architectures, and discusses the shift toward generative modeling and foundation models. Key contributions include a taxonomy of models (graph-based, transformer-based, and foundation-model driven), a synthesis of data preprocessing strategies, and an open-source repository of datasets and papers to promote reproducibility. The survey highlights synthetic data generation, large-scale pretraining, few-/zero-shot learning, and cross-modal integration as promising directions with potential to accelerate chemical discovery and materials design.

Abstract

The rapid advent of machine learning (ML) and artificial intelligence (AI) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data, referred to as Spectroscopy Machine Learning (SpectraML), remains relatively underexplored. Modern spectroscopic techniques (MS, NMR, IR, Raman, UV-Vis) generate an ever-growing volume of high-dimensional data, creating a pressing need for automated and intelligent analysis beyond traditional expert-based workflows. In this survey, we provide a unified review of SpectraML, systematically examining state-of-the-art approaches for both forward tasks (molecule-to-spectrum prediction) and inverse tasks (spectrum-to-molecule inference). We trace the historical evolution of ML in spectroscopy, from early pattern recognition to the latest foundation models capable of advanced reasoning, and offer a taxonomy of representative neural architectures, including graph-based and transformer-based methods. Addressing key challenges such as data quality, multimodal integration, and computational scalability, we highlight emerging directions such as synthetic data generation, large-scale pretraining, and few- or zero-shot learning. To foster reproducible research, we also release an open-source repository containing recent papers and their corresponding curated datasets (https://github.com/MINE-Lab-ND/SpectrumML_Survey_Papers). Our survey serves as a roadmap for researchers, guiding progress at the intersection of spectroscopy and AI.

Paper Structure

This paper contains 18 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Applications of Spectroscopy in Chemistry
  4. Roadmap of SpectraML
  5. SpectraML Methodologies Summary
  6. Data Representations and Preprocessing
  7. Forward Problem: Molecule-to-Spectrum
  8. Inverse Problem: Spectrum-to-Molecule
  9. Unified Frameworks and Cross-Modal Integration
  10. Challenges and Opportunities
  11. Data Quality, Scarcity, and Complexity
  12. Opportunities and Emerging Paradigms
  13. Conclusion
  14. Appendix
  15. Mass Spectrometry (MS)
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Timeline of ML progression and its application to spectroscopic studies. Left: Molecule to Spectrum, Right: Spectrum to Molecule
  • Figure 2: (Top) Overview of SpectraML, translating between Spectrum Space and Molecule Space. (Middle and Bottom) Illustration of key tasks in SpectraML, including their inputs, outputs, and the machine learning models used for mapping them, such as Random Forest, Feed Forward Networks (FFN), Variational Autoencoders (VAE), Transformers, Graph Neural Networks (GNN), and Foundation Models.