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

As an ensemble average result, vibrational spectrum simulation
can be time-consuming with high accuracy methods. We present a machine
learning approach based on the range-corrected deep potential (DPRc)
model to improve the computing efficiency. The DPRc method divides
the system into “probe region” and “solvent region”;
“solvent–solvent” interactions are not counted
in the neural network. We applied the approach to two systems: formic
acid CO stretching and MeCN CN stretching vibrational
frequency shifts in water. All data sets were prepared using the quantum
vibration perturbation 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 10 times faster than regular deep
potential and reduced the training time by about four. The approach
is efficient, easy to apply, and extendable to calculating various
spectra.