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

2025-02-14

AI Summary

Spectral and mass spectrometry data analysis has long relied heavily on expert knowledge, limiting scalability and hindering integration with molecular generation tasks. Method: This work introduces SpectraML—a unified framework systematically integrating both forward prediction (molecule → spectrum) and inverse inference (spectrum → molecular structure). It establishes the first taxonomy of spectroscopic AI models—spanning graph neural networks (GNNs), Transformers, and multimodal fusion architectures—and pioneers novel methodologies including synthetic data generation, large-model pretraining, and zero-shot learning. Contribution/Results: We release an open-source knowledge base comprising 100+ curated papers and annotated datasets, alongside standardized benchmarks and reproducible infrastructure. SpectraML advances chemical AI from spectral interpretation toward de novo molecular design, serving as a definitive roadmap for the spectroscopy–AI interdisciplinary field.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Automated analysis of spectroscopic data
• Forward and inverse tasks in SpectraML
• Challenges in data quality and scalability

Innovation

• Spectroscopy Machine Learning (SpectraML)
• Graph-based and transformer-based methods
• Synthetic data generation and pretraining