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As an ensemble average result, vibrational spectrum simulation can betime-consuming with high accuracy methods. We present a machine learningapproach based on the range-corrected deep potential (DPRc) model to improvecomputing efficiency. DPRc method divides the system into ``probe region'' and``solvent region''; ``solvent-solvent'' interactions are not counted in theneural network. We applied the approach to two systems: formic acid \ch{C=O}stretching and MeCN \ch{C+N} stretching vibrational frequency shifts in water.All data sets were prepared using Quantum Vibration Perturbation (QVP)approach. Effects of different region divisions, one-body correction,cut-range, and training data size were tested. The model with a single molecule``probe region'' showed stable accuracy; it ran roughly ten times faster thanregular DP and reduced the training time by about four. The approach isefficient, easy to apply, and extendable to calculating various spectra.

A Machine Learning Approach Based on Range Corrected Deep Potential Model for Efficient Vibrational Frequency Computation