A source of quantum chemical energy calculations for organic molecules computed at the high-level DLPNO-CCSD(T) level of theory. It features paired results using both def2-SVP and def2-TZVP basis sets to facilitate the training of machine learning models for Δ-CCSD(T) energy prediction.
Use Cases
- Train a regression model to predict Δ-CCSD(T) corrections using the energy differences between the def2-SVP and def2-TZVP columns.
- Benchmark the performance of density functional theory (DFT) approximations against these high-fidelity coupled-cluster reference values.
- Develop neural network potentials for organic chemistry by training on the provided DLPNO-CCSD(T) energy labels.
Strengths
- Features energy labels computed via the DLPNO-CCSD(T) method, a high-accuracy coupled-cluster approximation.
- Provides comparative data across two distinct basis sets: def2-SVP and def2-TZVP.
- Focuses specifically on organic molecules to support chemical space exploration and drug discovery applications.