[Paper Review] Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond

Kehan Guo, Yili Shen+9 more|ArXiv.org|Feb 14, 2025

Various Chemistry Research Topics7 citations

TL;DR

A comprehensive survey of SpectraML across MS, NMR, IR, Raman, and UV-Vis, outlining forward and inverse tasks, architectures, challenges, and emerging directions including generative and foundation models with an open-source dataset repository.

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.

Motivation & Objective

• Provide a unified review of SpectraML across five major spectroscopic modalities (MS, NMR, IR, Raman, UV-Vis).
• Differentiate and organize forward (molecule-to-spectrum) and inverse (spectrum-to-molecule) tasks within spectroscopy ML.
• Identify key challenges (data quality, multimodal integration, scalability) and opportunities (foundation models, synthetic data, few-/zero-shot learning).
• Present a roadmap of historical evolution from pattern recognition to generative and reasoning frameworks.
• Offer an open-source repository of datasets and code to foster reproducible research.

Proposed method

• Survey and taxonomy of neural architectures used in forward and inverse spectroscopy tasks (GNNs, transformers, CNNs, RNNs, diffusion models, GANs).
• Description of data representations for spectra and molecular structures (vectors, sequences, graphs, SMILES, coordinates).
• Discussion of forward problem approaches (molecule-to-spectrum prediction) including encoding–prediction frameworks and output modalities (regression/classification/generation).
• Discussion of inverse problem approaches (spectrum-to-molecule inference) including encoder–decoder and encoder–predictor schemes, with examples of SMILES and graph outputs.
• Analysis of unified frameworks and cross-modal integration, including foundation models and physics-informed generative models.

Experimental results

Research questions

• RQ1 What are the dominant ML methodologies advancing forward (molecule-to-spectrum) and inverse (spectrum-to-molecule) problems across MS, NMR, IR, Raman, and UV-Vis?
• RQ2 How have data representations and preprocessing strategies evolved to handle high-dimensional spectral data?
• RQ3 What are the principal challenges in data quality, scarcity, and multimodal integration, and what emerging directions address these issues?
• RQ4 How can foundation models and synthetic data generation reshape SpectraML for few-/zero-shot learning and cross-modal tasks?
• RQ5 What open resources exist to support reproducible SpectraML research?

Key findings

• ML approaches have evolved from traditional pattern recognition to transformer-based and graph-based models across five spectroscopy modalities.
• Unified framing of forward and inverse problems clarifies methodological choices and evaluation in SpectraML.
• Data quality, scarcity, and cross-modal integration remain core challenges, driving interest in synthetic data, physics-informed methods, and large-scale pretraining.
• Foundation models and cross-modal fusion offer a path toward few-/zero-shot learning and more robust spectral reasoning.
• An open-source repository of datasets and code is provided to foster reproducible SpectraML research.