H2CO Dataset

• Main Authors: Käser, Silvan, Koner, Debasish, Christensen, Anders S., von Lilienfeld, O. Anatole, Meuwly, Markus
• Format: Dataset
• Language: English
• Published: Zenodo 2020
• Subjects: Machine Learning; Formaldehyde; Neural Network; Quantum Chemistry; Potential Energy Surface; Christensen; Harding; Knowles; Orca
• Online Access: https://dx.doi.org/10.5281/zenodo.3923823; https://zenodo.org/record/3923823

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...