Machine Learning Applications in Materials Chemistry: The Rombo AI Approach

It has been proven that NMR can become a central tool in analytical chemistry for industrial use, as advances in data analysis and automation unlock its full potential. While NMR is valued for its reproducibility and rich quantitative and structural information, analyzing mixtures of multiple chemical compounds remains a major challenge, as overlapping signals and chemical shift variations often make traditional analysis slow, dependent on multiple software, and highly dependent on expert interpretation. Machine learning and Neural Networks (like CNN, convolutional neural networks) can significantly improve compound identification in NMR spectra of mixtures.

From Spectra to Structure: Why Machine Learning Matters in NMR

NMR spectroscopy is a cornerstone of molecular characterization. It is non-destructive, highly reproducible, and rich in structural information. However, interpreting NMR spectra, especially for complex mixtures, remains challenging.

Why is that so?

Conventional NMR analysis methods typically rely on peak picking, spectral alignment, or similarity metrics. These approaches work reasonably well for pure compounds but struggle in real-world mixtures, where signals overlap and chemical shifts vary depending on the sample environment.

To compensate, analysts manually apply binning or alignment techniques, or resort to 2D NMR experiments and manual deconvolution. While effective in some cases, these methods are time-consuming and impractical for routine or large-scale analysis.

The shift towards ML to Handle Chemical Shift Variations

ML and NNs have shown strong performance in identifying compounds within mixtures directly from raw NMR spectra. Instead of relying on peak picking, alignment, or handcrafted features, these models learn relevant spectral representations end-to-end. This shift—from explicit spectral rules to learned representations—marks a fundamental change:

NMR data becomes a machine-interpretable signal rather than a bottleneck requiring expert intervention.

In production environments, ML is routinely applied to predict material properties such as viscosity, density, or boiling behavior, to support quality control and classification, to detect anomalies or contamination, and to monitor and optimize processes. Still, what matters most is obtaining reliable, decision-ready information quickly and consistently.

The ROMBO AI Material Intelligence Platform

Translating these advances into industry requires more than accurate models. It requires platforms that integrate acquisition, preprocessing, model governance, and validation around specific material families. Only in this way can ML-driven spectral analysis deliver outputs that are robust, comparable, and usable in production environments.

The Rombo AI Material Intelligence Platform combines low-field NMR instrumentation with material-specific AI models. At its core, the platform automates the extraction of physico-chemical properties, along other tailor-made properties, directly from NMR spectra. ML models learn the relationship between spectral patterns and material properties – such as viscosity and density (or for crude oil, distillation yields). These models are trained and validated on experimental datasets agreed upon with the customer, ensuring industrial relevance.

The platform supports:

1. End-to-end analysis, drastically reducing turnaround time
2. Automated signal analysis, tailored to the target material
3. Material-family models, avoiding unsafe generalization
4. Comparable outputs, suitable for quality control and decision-making

Conclusion

Machine learning is turning NMR from a descriptive technique into a predictive engine for industrial materials chemistry. As analytical demands become faster and more complex, platforms like Rombo AI bridge spectroscopy and AI to deliver reliable, decision-ready material insights at industrial speed. Explore how Rombo AI enables material intelligence beyond traditional analysis: contact@rombo.ai