Achieving vibrational energies of diatomic systems with high quality by machine learning improved DFT method
Yang, ZZ (Yang, Zhangzhang); Wan, ZT (Wan, Zhitao); Liu, L (Liu, Li); Fu, J (Fu, Jia); Fan, QC (Fan, Qunchao); Xie, F (Xie, Feng); Zhang, Y (Zhang, Yi); Ma, J (Ma, Jie)
RSC Advances, 2022, Volume 12, pp. 35950-35958.
When using ab initio methods to obtain high-quality quantum behavior of molecules, it often involves a lot of trial-and-error work in algorithm design and parameter selection, which requires enormous time and computational resource costs. In the study of vibrational energies of diatomic molecules, we found that starting from a low-precision DFT model and then correcting the errors using the high-dimensional function modeling capabilities of machine learning, one can considerably reduce the computational burden and improve the prediction accuracy. Data-driven machine learning is able to capture subtle physical information that is missing from DFT approaches. The results of (CO)-C-12-O-16, (MgO)-Mg-24 and (NaCl)-Cl-35 show that, compared with CCSD(T)/cc-pV5Z calculation, this work improves the prediction accuracy by more than one order of magnitude, and reduces the computation cost by more than one order of magnitude. |
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