Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples
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TL;DR - UltraIR is a 100M+ parameter foundation model pretrained on roughly 60 million simulated infrared spectra, then adapted to diverse chemical-analysis tasks. It enables data-efficient simulation-to-real transfer and cross-instrument zero-shot inference.
- Pretraining combines spectral reconstruction, molecular-fingerprint alignment, and functional-group prediction.
- Applications span molecular analysis, mixtures, bacteria, medicinal herbs, microplastics, and soil properties.
- UltraIR outperforms conventional machine-learning and task-specific deep-learning baselines across the reported tasks.
- It remains effective with limited labeled experimental data and transfers across spectrometers and laboratories.
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Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples
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TL;DR - UltraIR is a 100M+ parameter foundation model pretrained on roughly 60 million simulated infrared spectra, then adapted to diverse chemical-analysis tasks. It enables data-efficient simulation-to-real transfer and cross-instrument zero-shot inference.
- Pretraining combines spectral reconstruction, molecular-fingerprint alignment, and functional-group prediction.
- Applications span molecular analysis, mixtures, bacteria, medicinal herbs, microplastics, and soil properties.
- UltraIR outperforms conventional machine-learning and task-specific deep-learning baselines across the reported tasks.
- It remains effective with limited labeled experimental data and transfers across spectrometers and laboratories.