H2CO Dataset - Käser, Silvan; Koner, Debasish; Christensen, Anders S.; von Lilienfeld, O. Anatole; Meuwly, Markus (Zenodo, 2020)
The deposited data sets were used to compare three state-of-the art machine learning (ML) approaches to obtain representations of potential energy surfaces (PESs). The comparison is meant to be representative as it examines a purely kernel-based approach (reproducing kernel Hilbert space plus forces (RKHS+F))[1], a purely neural network based approach (PhysNet)[2] and includes the FCHL representation [3] within kernel ridge regression. Formaldehyde, H2CO, is used as a benchmark system. H2CO is a small molecule for which PESs can be calculated at different levels of theory and, thus, suitable for an in-depth theoretical study. Also, very high-level calculations have already been presented (see e.g. Ref. [4]) and experimental reference data is available to compare with [5]. Using reference data calculated at three different levels of quantum chemical theory (B3LYP/cc-pVDZ, MP2/aug-cc-pVTZ and CCSD(T)-F12/aug-cc-pVTZ-F12) ML models are trained using the different ML methods. The performance of the models is then examined by considering energy and force learning curves, harmonic frequencies and IR spectra from finite-Temperature molecular dynamics (MD) simulations. The data sets contain different geometries for the H2CO molecule generated using the normal mode sampling approach [6] performed at different temperatures. Four data sets are deposited: i) "h2co_B3LYP_cc-pVDZ_4001.npz": 4001 geometries of H2CO generated using normal mode sampling and calculated using ORCA [7] (B3LYP/cc-pVDZ). ii) "h2co_mp2_avtz_4001.npz": 4001 geometries of H2CO generated using normal mode sampling and calculated using MOLPRO 2019 [8] (MP2/aug-cc-pVTZ). iii) "h2co_ccsdt_avtz_4001.npz": 4001 geometries of H2CO generated using normal mode sampling and calculated using MOLPRO 2019 [8] (CCSD(T)-F12/aug-cc-pVTZ-F12). iv) "h2co_ccsdt_avtz_2500_extrapol.npz": 2500 geometries of H2CO generated using normal mode sampling and calculated using MOLPRO 2019 [8] (CCSD(T)-F12/aug-cc-pVTZ-F12). This sampling was carried out at higher temperature (5000 K ...