Databases for all data related to the article: "Challenges for Kinetics Predictions via Neural Network Potentials: a Wilkinson’s catalyst case" Each dataset was created with ASE db, and can be explored with: import ase.db with ase.db.connect(db_path) as db: for row in db.select(): atoms = row.toatoms() # ASE Atoms object data = row.data # Diverse information (energy, gradients and dipole, at DFT, xTB [and NNP or NNP(+xTB)], geometry type, reaction path network connection, ...) Data labels: data['energy']: DFT energy [eV] data['gradients']: DFT gradients [eV/A] data['dipole']: DFT dipole [Debye] data['xTB']['GFN2-xTB']['energy']: xTB energy [eV] (when available) data['xTB']['GFN2-xTB']['gradients']: xTB gradients [eV/A] (when available) data['xTB']['GFN2-xTB']['dipole']: xTB dipole [Debye] (when available) data['E_pred']: Prediction energy [eV] (NNP, NNP(+xTB), xTB, depending on the dataset), if available data['grad_pred']: Prediction gradients [eV/A] data['dipole_pred']: Prediction gradients [Debye] data['geo_type']: Type of geometry ('EQ': Equilibrium state, 'TS': Transition state, 'NODE': intermediary geometry, 'TSEQ': barrier-less TS [both path top and path endpoint]) data['EQ_id']: GRRM EQ number (sort of exploration timestamp on EQs), when available data['TS_id']: GRRM path number (exploration timestamp on paths), when available data['node_id']: Position in path, when available Datasets: WilkinsonAFIRdb.db: DFT-powered AFIR-based search data (including the single geometry with failed xTB convergence) pureNNP_20%_dataset.zip: train/val/test data from NNP model trained on the first 20% of DFT paths explored pureNNP_50%_dataset.zip: train/val/test data from NNP model trained on the first 50% of DFT paths explored pureNNP_80%_dataset.zip: train/val/test data from NNP model trained on the first 80% of DFT paths explored pureNNP_20%_localSearch.db: local NNP-powered AFIR-based search data, using NNP model trained on the first 20% of DFT paths explored pureNNP_50%_localSearch.db: local NNP-powered AFIR-based search data, using NNP model trained on the first 50% of DFT paths explored pureNNP_80%_localSearch.db: local NNP-powered AFIR-based search data, using NNP model trained on the first 80% of DFT paths explored NNPxTB_20%_localSearch: local NNP-powered AFIR-based search data, using NNP(+xTB) model trained on the first 20% of DFT paths explored NNPxTB_50%_localSearch: local NNP-powered AFIR-based search data, using NNP(+xTB) model trained on the first 50% of DFT paths explored NNPxTB_80%_localSearch: local NNP-powered AFIR-based search data, using NNP(+xTB) model trained on the first 80% of DFT paths explored xTB_localSearch: xTB-powered AFIR-based search data NNPxTB_20%_globalSearch: global/full NNP-powered AFIR-based search data, using NNP(+xTB) model trained on the first 20% of DFT paths explored (EQ and TS only) NNPxTB_50%_globalSearch: global/full NNP-powered AFIR-based search data, using NNP(+xTB) model trained on the first 50% of DFT paths explored (EQ and TS only) Note: DFT level of theory is RωB97X-D/Def2-SVP

Additional data related to the full NNP(+xTB)-powered searches will be added in a future version