ArticleProceedings of the National Academy of Sciences of the United States of America2023
Machine learning of spectra-property relationship for imperfect and small chemistry data.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 40 citations in OpenAlex.
- Unsupervised Hierarchical Symbolic Regression for Interpretable Property Modeling in Complex Multi-Variable Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Discovering physical laws with parallel symbolic enumeration.Nature computational science · 2026Article
- Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression.ACS nano · 2025Article
- CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration.PNAS nexus · 2024Article
- Predicting and understanding photocatalytic COPNAS nexus · 2024Article
- Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.Journal of the American Chemical Society · 2024Article
- A robotic AI-Chemist system for multi-modal AI-ready database.National science review · 2023Article
- Machine learning of spectra-property relationship for imperfect and small chemistry data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Machine learning (ML) is causing profound changes to chemical research through its powerful statistical and mathematical methodological capabilities. However, the nature of chemistry experiments often sets very high hurdles to collect high-quality data that are deficiency free, contradicting the need of ML to learn from big data. Even worse, the black-box nature of most ML methods requires more abundant data to ensure good transferability. Herein, we combine physics-based spectral descriptors with a symbolic regression method to establish interpretable spectra-property relationship. Using the machine-learned mathematical formulas, we have predicted the adsorption energy and charge transfer of the CO-adsorbed Cu-based MOF systems from their infrared and Raman spectra. The explicit prediction models are robust, allowing them to be transferrable to small and low-quality dataset containing partial errors. Surprisingly, they can be used to identify and clean error data, which are common data scenarios in real experiments. Such robust learning protocol will significantly enhance the applicability of machine-learned spectroscopy for chemical science.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.