Benchmark Comparison of Dual-basis Double-hybrid Density Functional Theory and a Neural-network-optimized Method for Intermolecular Interactions
Document Type
Article
Publication Date
12-30-2020
Abstract
We present a computationally efficient implementation of double-hybrid density functional theory (DH-DFT) leveraging the dual basis methods of Head-Gordon and co-workers and the resolution-of-the-identity second-order Møller-Plesset (RI-MP2) theory. The B2PLYP, B2GP-PLYP, DSD-BLYP and DSD-PBEP86 density functionals are applied to assess the performance of dual-basis methods on several benchmark test cases, including the CONF set of conformational energy differences in C4-C7 alkanes, the S22 set of noncovalent interaction energies, and the RGC10 noble-gas dimer dissociation curves. The dual-basis DH-DFT approach is shown to give results in excellent agreement with conventional methods at a reduced computational cost. For noncovalent interaction energies, DH-DFT is compared against a leading neural-network-based approach, namely the SNS-MP2 method of McGibbon and coworkers (McGibbon et al., 2017). The DH-DFT and SNS-MP2 methods are shown to produce similar accuracies when compared to the established benchmark values.
Source Publication
Journal of Molecular Spectroscopy (ISSN 0022-2852 | eISSN 1096-083X)
Recommended Citation
Lutz, J. J., Byrd, J. N., & Montgomery, J. A. (2021). Benchmark comparison of dual-basis double-hybrid density functional theory and a neural-network-optimized method for intermolecular interactions. Journal of Molecular Spectroscopy, 376, 111406. https://doi.org/10.1016/j.jms.2020.111406
Comments
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The published version of the article is accessible by subscription in volume 376 of Journal of Molecular Spectroscopy (February 2021) as cited on this page, accessible through the DOI link below.