A Combined First-principles Calculation and Neural Networks Correction Approach for Evaluating Gibbs Energy of Formation

Abstract

Despite of their successes, the results of first-principles quantum mechanical calculations contain inherent numerical errors that are caused by inadequate treatment of electron correlation, incompleteness of basis sets, relativistic effects or approximated exchange-correlation functionals. In this work, we develop a combined density-functional theory and neural-network correction (DFT-NEURON) approach to reduce drastically these errors, and apply the resulting approach to determine the standard Gibbs energy of formation ΔG ⁰ at 298 K for small- and medium-sized organic molecules. The root mean square deviation of the calculated ΔG ⁰ for 180 molecules is reduced from 22.3 kcal · mol^-1 to 3.0 kcal · mol^-1 for B3LYP/6-311+G(d,p). We examine further the selection of physical descriptors for the neural network.

Keywords:

• DFT
• Neural network
• Gibbs energy of formation
• First-principles quantum mechanical methods

Acknowledgements

Support from the Hong Kong Research Grant Council (RGC) and the Committee for Research and Conference Grants (CRCG) of the University of Hong Kong is gratefully acknowledged.