Yitian Kong
Infrared (IR) spectroscopy is widely used for functional group identification, but machine-learning models often struggle when spectra are transferred across physical phases. Here we establish a phase-separated 16-label benchmark for functional group recognition across gas, liquid, and solid IR spectra and show that direct gas-to-condensed transfer is unreliable, especially for solid spectra. Simply increasing model capacity or replacing the backbone provides only limited improvement. In contrast, the main two-stage gas-pretrained adaptation protocol substantially improves condensed-phase recognition over target-only training, raising solid Macro-F1 from 0.6902 to 0.7351 and liquid Macro-F1 from 0.7198 to 0.7792. The improvement is class-dependent, with strong recovery for labels such as nitrile, while alkene remains difficult because of weak and overlapping spectral evidence. Additional analyses show that the adaptation benefit is not explained by longer target-only training or class weighting, and that it transfers across multiple model families. To make the adapted model more suitable for resource-limited use, we applied segment max pooling under the two-stage gas-pretrained adaptation pipeline, reducing the model to 9.23% of the original Dual Head parameter count while retaining 99.04% of its Macro-F1 performance. Together, this work establishes a phase-aware adaptation framework for condensed-phase IR functional group recognition, showing that gas-phase spectra can be converted from an unreliable direct predictor into useful prior training data for solid and liquid spectra.