where pe and pf are the weight for the energy and force terms, respectively. N represents the number of atoms in the structure. Similar to a classical neural network, the DP scheme trains the model by computing the gradient of the loss function using the backpropagation algorithm . The NN model is trained for 1.0 × 10 6 iterations with an exponentially decaying learning rate from 1.0 × 10 -3 to 5.0× 10 -8 . The performance of NN potential highly depends on the quality of the training dataset . Although NNs are good at interpolating between training points, they cannot predict the energies and forces of configurations distant from those of the training set. Therefore, it is critical to ensure that the dataset covers the PES of interest. The formation of molecular soot precursors starts from the fuel pyrolysis process, where acetylene and propargyl are the key intermediates. These intermediates would further react to form the first aromatic ring -benzene. Subsequent reactions lead to the formation of larger PAHs via the HACA mechanism. This paper constructs an ab initio database to cover the entire evolution from acetylene to large PAH molecules. In Fig. , there are 47 subsystems in the database, including alkanes, alkenes, alkynes, gaseous radicals, PAH molecules, PAH radicals, and carbon materials. Each subsystem consists of MD trajectories of a specific molecule. Details about the molecular configuration of each system are listed in Table . The trajectories are generated from ReaxFF MD simulation under an NVT ensemble at temperatures of 300, 1000, 2000, 3000, 4000, 5000, and 6000 K.