H2CO Dataset

• Main Authors: Käser, Silvan (10505887), Koner, Debasish (10505890), Christensen, Anders S. (10505893), von Lilienfeld, O. Anatole (10505896), Meuwly, Markus (6505646)
• Format: Dataset
• Language: unknown
• Published: 2020
• Subjects: Biotechnology; Ecology; Science Policy; Environmental Sciences not elsewhere classified; Biological Sciences not elsewhere classified; Chemical Sciences not elsewhere classified; Machine Learning; Formaldehyde; Neural Network; Quantum Chemistry; Potential Energy Surface; Christensen; Harding; Knowles; Orca
• Online Access: https://doi.org/10.5281/zenodo.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...