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This data set was used to examine isomerization (to vinylalcohol, VA) and decomposition reactions (to either CO+CH4 or H2+H2CCO) of acetaldehyde (AA) relevant to atmospheric processes. Therefor, the data is used to train a neural network which serves as a fully dimensional, reactive potential energy surface and is used to run molecular dynamics simulations. The data set contains different geometries for the mentioned molecules and complexes as well as for some substructures (Amons [1]). In total the data set contains 432'399 geometries with reference energies, forces and dipole moments calculated at the MP2/aug-cc-pVTZ level of theory calculated using the MOLPRO [2]. For more details, see https://arxiv.org/abs/2003.08171 [1] Huang, B.; von Lilienfeld, O.A., arXiv:1707.04146 [2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schütz, M.; et al. https://www.molpro.net
Machine Learning, Atmospheric Chemistry, Neural Network, Acetaldehyde, Potential Energy Surface, Quantum Chemistry
Machine Learning, Atmospheric Chemistry, Neural Network, Acetaldehyde, Potential Energy Surface, Quantum Chemistry
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