Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Article Swipe
Kehan Guo
,
Yili Shen
,
Gisela Abigail Gonzalez-Montiel
,
Yi Huang
,
Yujun Zhou
,
Mihir Surve
,
Zhichun Guo
,
Partha Pratim Das
,
Nitesh V. Chawla
,
Olaf Wiest
,
Xiangliang Zhang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.09897
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.09897
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.09897
- https://arxiv.org/pdf/2502.09897
- OA Status
- green
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407632415
All OpenAlex metadata
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https://openalex.org/W4407632415Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.09897Digital Object Identifier
- Title
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Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and BeyondWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-14Full publication date if available
- Authors
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Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel, Yi Huang, Yujun Zhou, Mihir Surve, Zhichun Guo, Partha Pratim Das, Nitesh V. Chawla, Olaf Wiest, Xiangliang ZhangList of authors in order
- Landing page
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https://arxiv.org/abs/2502.09897Publisher landing page
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https://arxiv.org/pdf/2502.09897Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2502.09897Direct OA link when available
- Concepts
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Spectroscopy, Chemistry, Computer science, Nanotechnology, Artificial intelligence, Materials science, Physics, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.foundation | 107 |
| abstract_inverted_index.historical | 94 |
| abstract_inverted_index.multimodal | 133 |
| abstract_inverted_index.reasoning, | 112 |
| abstract_inverted_index.relatively | 36 |
| abstract_inverted_index.repository | 163 |
| abstract_inverted_index.techniques | 40 |
| abstract_inverted_index.workflows. | 65 |
| abstract_inverted_index.application | 19 |
| abstract_inverted_index.generation, | 146 |
| abstract_inverted_index.graph-based | 122 |
| abstract_inverted_index.inference). | 90 |
| abstract_inverted_index.intelligent | 60 |
| abstract_inverted_index.large-scale | 147 |
| abstract_inverted_index.open-source | 162 |
| abstract_inverted_index.prediction) | 85 |
| abstract_inverted_index.recognition | 103 |
| abstract_inverted_index.traditional | 63 |
| abstract_inverted_index.(SpectraML), | 34 |
| abstract_inverted_index.Spectroscopy | 31 |
| abstract_inverted_index.ever-growing | 48 |
| abstract_inverted_index.expert-based | 64 |
| abstract_inverted_index.integration, | 134 |
| abstract_inverted_index.intelligence | 9 |
| abstract_inverted_index.intersection | 185 |
| abstract_inverted_index.pretraining, | 148 |
| abstract_inverted_index.reproducible | 156 |
| abstract_inverted_index.researchers, | 180 |
| abstract_inverted_index.scalability, | 137 |
| abstract_inverted_index.spectroscopy | 187 |
| abstract_inverted_index.computational | 136 |
| abstract_inverted_index.corresponding | 169 |
| abstract_inverted_index.spectrometric | 26 |
| abstract_inverted_index.spectroscopic | 24, 39 |
| abstract_inverted_index.spectroscopy, | 99 |
| abstract_inverted_index.architectures, | 120 |
| abstract_inverted_index.representative | 118 |
| abstract_inverted_index.systematically | 76 |
| abstract_inverted_index.underexplored. | 37 |
| abstract_inverted_index.transformations | 14 |
| abstract_inverted_index.high-dimensional | 51 |
| abstract_inverted_index.state-of-the-art | 78 |
| abstract_inverted_index.transformer-based | 124 |
| abstract_inverted_index.(molecule-to-spectrum | 84 |
| abstract_inverted_index.(spectrum-to-molecule | 89 |
| abstract_inverted_index.(https://github.com/MINE-Lab-ND/SpectrumML_Survey_Papers). | 172 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile |