WilkinsonAFIRdb and related

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