期刊
CHEMPHYSCHEM
卷 11, 期 12, 页码 2561-2567出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/cphc.201000273
关键词
bond dissociation energy; density functional calculations; heat of formation; neural networks; thermochemistry
资金
- National Natural Science Foundation of China [10774126, 20973138, 20923004]
- Ministry of Science and Technology [2007C8815206]
Previously, we have put forward the X1 method that combines B3LYP with neural network correction for an accurate yet efficient prediction of thermochemistry. Without paying additional computational cost, X1 reduces B3LYP's mean absolute deviation (MAD) for a set of 92 bond dissociation energies (BDEs) from 5.5 to 2.4 kcal mol(-1). In this work, we extend X1 and propose the Xis method by including the spin change from molecules to atoms during atomization as an additional descriptor. Xis further reduces the MAD for BDEs to 1.4 kcal mol(-1), thus showing substantial improvement.
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