Journal
MOLECULAR SIMULATION
Volume 30, Issue 1, Pages 9-15Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/08927020310001631098
Keywords
DFT; neural network; Gibbs energy of formation; first-principles quantum mechanical methods
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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 DeltaG(0) at 298 K for small- and medium-sized organic molecules. The root mean square deviation of the calculated DeltaG(0) for 180 molecules is reduced from 22.3 kcal.mol(-1) to 3.0kcal.mol(-1) for B3LYP/6-311+G(d,p). We examine further the selection of physical descriptors for the neural network.
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