Achieving vibrational energies of diatomic systems with high quality by machine learning improved DFT method

Yang, Zhangzhang; Wan, Zhitao; Liu, Li; Fu, Jia; Fan, Qunchao; Xie, Feng; Zhang, Yi; Ma, Jie

doi:10.1039/d2ra07613f

Cited by 2 publications

(3 citation statements)

References 64 publications

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Section: Theory and Methodsmentioning

confidence: 88%

“…As shown in our recent work, machine learning algorithm performed great in predicting vibrational energy of diatomic molecular systems [30,31]. And the artificial neural network (ANN) [32,33] performed best [30]. Thus, it is used to get the ro‐vibrational energy, the molar heat capacity and the entropy in this paper.…”

Section: Theory and Methodsmentioning

confidence: 91%

“…And the artificial neural network (ANN) [32,33] performed best [30]. Thus, it is used to get the ro-vibrational energy, the molar heat capacity and the entropy in this paper.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Obtaining the molar heat capacities and entropies of HCl and HBr by combining density functional theory and machine learning algorithm

Yang

Wei

et al. 2023

Int J of Quantum Chemistry

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An integrated approach combing density functional theory (DFT) and machine learning algorithm (MLA) is proposed here to obtain the molar heat capacities and entropies of diatomic macroscopic gasses with high quality. The DFT approach takes care of the main physical effects, while machine learning takes care of the intricate details it leaves out. After machine learning algorithm correction, a complete set of accurate prediction of vibrational energy spectrum is obtained, which is better than the results of DFT methods in accuracy. And then it is used to replace the vibrational part in the ro‐vibrational energy calculated by DFT to obtain the rectified ro‐vibrational energy. Furthermore, through the quantum ensemble theory, the thermodynamic properties of the macroscopic gas are calculated by the predicted ro‐vibrational energy spectrum, and are modified again by the machine learning algorithm. The study of the HCl and HBr system show that, compared with CCSD(T)/cc‐pV5Z and the improved variational algebraical method, the macroscopic thermodynamic properties calculated by this work in the temperature range of 300–6000 K are the closest to the experimental values. The relative error is less than 1% at each temperature.

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Section: Theory and Methodsmentioning

confidence: 88%

Section: Theory and Methodsmentioning

confidence: 91%

See 1 more Smart Citation

Obtaining the molar heat capacities and entropies of HCl and HBr by combining density functional theory and machine learning algorithm

Yang

Wei

et al. 2023

Int J of Quantum Chemistry

Self Cite

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Analytical Development to Determine Vibrational Energy Levels and Dissociation Energy of Diatomic Molecules

Amila,

Fedoul,

Idrissi

et al. 2024

FirePhysChem

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Achieving vibrational energies of diatomic systems with high quality by machine learning improved DFT method

Abstract: By systematically correcting the calculation errors through machine learning, the accuracy of the diatomic vibrational energy prediction based on typical DFT methods has been improved by order of magnitude.

Cited by 2 publications

References 64 publications

Obtaining the molar heat capacities and entropies of HCl and HBr by combining density functional theory and machine learning algorithm

Obtaining the molar heat capacities and entropies of HCl and HBr by combining density functional theory and machine learning algorithm

Analytical Development to Determine Vibrational Energy Levels and Dissociation Energy of Diatomic Molecules

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