The X1s Method for Accurate Bond Dissociation Energies

Abstract

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 X1s method by including the spin change from molecules to atoms during atomization as an additional descriptor. X1s further reduces the MAD for BDEs to 1.4 kcal mol(-1), thus showing substantial improvement.

Gas‐Phase Thermodynamics as a Validation of Computational Catalysis on Surfaces: A Case Study of Fischer–Tropsch Synthesis

“…Indeed, as the chain length grows, a small error would eventually grow into a large error. Note that it would also be possible to adjust some of the PBE or B3LYP values [44,69,[73][74][75] to achieve the correct ther-modynamics of the net reaction. Table 7 lists the heats of reaction for Equation (1) to produce alkanes (C n H 2 n + 1 ) as n increases from 1 to 8.…”

“…After scaling, the error for this reaction is reduced from À1.51 to À0.06 kcal mol À1 . Note that it would also be possible to adjust some of the PBE or B3LYP values [44,69,[73][74][75] to achieve the correct ther-modynamics of the net reaction. However, the guidelines are such that the number of adjustments should as small as possible and the magnitude of the adjustment should be as small as possible.…”

Oxidative Addition of the CαCβ Bond in β‐O‐4 Linkage of Lignin to Transition Metals Using a Relativistic Pseudopotential‐Based ccCA‐ONIOM Method

“…[44][45][46] Despite its good accuracy in the prediction of geometric parameters, B3LYP has been shown to lead to large errors in the prediction of enthalpies of formation, bond dissociation energies and reaction barrier heights. [47] A methodical combination of the accuracy of composite approaches and the efficiency of DFT methods has the potential of broadening the applicability of quantitative theoretical methods to larger chemical systems.…”

The X1 family of methods that combines B3LYP with neural network corrections for an accurate yet efficient prediction of thermochemistry

“…Even though B3LYP has enjoyed a remarkable performance over a wide range of systems, there is growing evidence showing that B3LYP (1) accumulates errors on heats of formation (HOFs, ) as the system size is increased, (2) yields too low bond dissociation enthalpies (BDEs), displaying, in particular, increasing errors on CX bond energies with increased alkylation, (3) fails to give reliable energy ordering for heats of isomerization (HOIs), (4) underestimates reaction barrier heights, and (5) breaks down for nonbonded interaction systems, and so forth. To develop a functional that performs uniformly better than B3LYP remains a great challenge …”

“…Some representatives are bond/group additivity corrections, parametrization of atomic energies, corrections considering spin multiplicities and charges, and corrections based on neural network (NN), , and so forth. We proposed the X1 method which is a NN correction procedure to diminish the B3LYP errors . The X1 family of methods are based on HOFs of 223 molecules in the G3/99 set, plus those of 170 additional molecules (the X1‐1 set) with a more diverse chemical environment.…”