Combining ab initio and machine learning method to improve prediction
performance of diatomic vibrational energies
Abstract
Through the comprehensive analysis of ab initio and experimental results
of a large number of diatomic systems, the systematic deviation of ab
initio method in vibrational energies prediction caused by
physical/mathematical simplification is located. A joint ab initio and
machine learning method based on information across molecules is
proposed to deal with the problem. Starting from an ab initio model, and
then systematically modifying it through machine learning, the
vibrational energies prediction of many diatomic systems (SiC, HBr, NO,
PC, N2, SiO, O2, ClF, etc.) have been
improved, and significantly surpassed the more complex ab initio model.
In addition to the improvement of accuracy, the new method also greatly
reduces the computational expense, and is applicable for the systems
without experimental data.