Tong Zhu

Predicting experimental vibrational spectra from molecular structure has been caught between theory and measurement: quantum chemistry is accurate but slow, while end-to-end neural networks are fast but bypass explicit vibrational information. HARMONY assembles harmonic spectra from atomic forces and spectroscopic tensors predicted directly by equivariant neural networks, then uses HarmoNet to map them to experimental IR and Raman spectra conditioned on molecular topology and measurement metadata. On the HarmoSpec test set, the full workflow reaches mean cosine similarities of 0.912 for infrared and 0.907 for Raman, with a mean of 2.12 seconds per molecule, roughly 600-fold faster than the evaluated DFT workflow.

Predicting experimental vibrational spectra from molecular structure has been caught between theory and measurement: quantum chemistry is accurate but slow, while end-to-end neural networks are fast but bypass explicit vibrational information. HARMONY assembles harmonic spectra from atomic forces and spectroscopic tensors predicted directly by equivariant neural networks, then uses HarmoNet to map them to experimental IR and Raman spectra conditioned on molecular topology and measurement metadata. On the HarmoSpec test set, the full workflow reaches mean cosine similarities of 0.912 for infrared and 0.907 for Raman, with a mean of 2.12 seconds per molecule, roughly 600-fold faster than the evaluated DFT workflow.