Data and Code for "Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies" ...

• Main Authors: Ruth, Marcel, Justus Liebig University Giessen
• Format: Dataset
• Language: English
• Published: Universitätsbibliothek Gießen 2023
• Subjects: ddc540; Orca
• Online Access: https://dx.doi.org/10.22029/jlupub-9418; https://jlupub.ub.uni-giessen.de//handle/jlupub/10034

The datasets, models, and scripts were created to achieve an accurate prediction of the increment of single-point energies between density functional theory (DFT) and wavefunction-based methods, which led to our submitted article: 'A Machine Learning Approach for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies'. We used the ORCA quantum chemical package to compute the geometries of each species at the B3LYP-D3(BJ)/cc-pVTZ level of theory. The optimized structure was subsequently employed for single-point (SP) computations at the DLPNO-CCSD(T)/cc-pVTZ and CCSD(T)/cc-pVTZ levels of theory. All data were extracted from the calculations and compiled in the provided .csv files. With the datasets and prediction scripts, it is possible to forecast the differences in single-point (SP) energies between the B3LYP-D3(BJ)/cc-pVTZ and DLPNO-CCSD(T)/cc-pVTZ (for monomers and dimers) levels of theory, as well as to the CCSD(T)/cc-pVTZ level of theory for monomers. The datasets can be opened ...