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This data set was used to examine the reactive dynamics and spectroscopic properties of three beta-diketones: malonaldehyde (MA, propandial), acetoacetaldehyde (AAA, 3- oxobutanal) and acetylacetone (AcAc, pentan-2,4-dion), in a neural network based manner. The data set contains different geometries for the three molecules as well as for substructures (Amons [1]). In total the data set contains 71208 geometries with reference energies, forces and dipole moments calculated at the MP2/aug-cc-pVTZ level of theory calculated using the MOLPRO 2018.2.0 [2]. For more details, see https://arxiv.org/abs/1911.09475 [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, 2018.2.0
Machine Learning, Malonaldehyde, Neural Network, Quantum Chemistry, beta-diketone, potential energy surface
Machine Learning, Malonaldehyde, Neural Network, Quantum Chemistry, beta-diketone, potential energy surface
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