A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Calculations

Files contained in Reaction-QM The Reaction-QM dataset consists of three datasets: GFN2-RXN, B3LYP-RXN, and B3LYP-IRC, which are organized as follows: GFN2-RXN GFN2_reaction_info.csv: Summarizes reactions with reaction SMILES (SMARTS) and reaction properties calculated at the GFN2-xTB level. GFN2_xTB_X_Y.h5: Contains the fully optimized geometries of reactants, products, and transition states, along with their corresponding electronic energies calculated at the GFN2-xTB level. Here, X and Y denote the indices of the grouped reactions within the dataset. B3LYP-RXN B3LYPD3_TZVP_reaction_info.csv: Summarizes reactions with reaction SMILES (SMARTS) and reaction properties calculated at the B3LYP-D3/TZVP level. B3LYPD3_TZVP.h5: Contains the fully optimized geometries of reactants, products, and transition states, along with their corresponding electronic energies calculated at the B3LYP-D3/TZVP level. B3LYP-IRC B3LYPD3_TZVP_IRC_X_Y.h5: Contains IRC trajectories of the reactions included in B3LYP-RXN, calculated at the B3LYP-D3/TZVP level using Gaussian 16. Here, X and Y denote the minimum and maximum number of atoms contained in the trajectories within each file. The following script is a simple example for reading GFN2_xTB_X_Y.h5 file or B3LYPD3_TZVP.h5 file: import h5py h5file = h5py.File('Path/To/File.h5', 'r') for rxn_name_key, reaction_point_dict in h5file.items(): # Main file = { Reaction name : Reaction data dictionary } reactants_data_dict = reaction_point_dict.get('R') # Reaction data dictionary = { 'R': Reactant data, products_data_dict = reaction_point_dict.get('P') # 'P' : Product data, ts_data_dict = reaction_point_dict.get('TS') # 'TS' : Transition state data } for species_key, species_data in reactants_data_dict.items(): # Reactant data = { Species key : Species data } # Species data = A dictionary containing the information of one molecule E_H_G_tuple = species_data['EHG'][()] atomic_numbers_array = species_data['atomic_number'][()] atomic_coordinates_array = species_data['coords'][()] The following script is another example code for reading B3LYPD3_TZVP_IRC_X_Y.h5 file: import h5py h5file = h5py.File('Path/To/File.h5', 'r') for num_atoms_key, TS_dict in h5file.items(): # Main file = { Number of atoms : Dictionary of TS data } for TS_name_key, TS_info_dict in TS_dict.items(): # Dictionary of TS data = { TS ID : Information dictionary of each reaction } # Information dictionary of each reaction contains full IRC trajectory atomic_numbers = TS_info_dict['numbers'][()] atomic_coordinates = TS_info_dict['coords'][()] atomic_energies = TS_info_dict['energies'][()] atomic_forces = TS_info_dict['forces'][()]