AI2EMD with Hierarchical Active Learning Enables Accurate and Generalizable Liquid Electrolyte Characterization: Neural network potentials training data

The deepmd_data dataset comprises energy data for 375,781 structures and force data for more than 45 million atoms, generated from six hierarchical active learning iterations. The init directory stores the non-periodic molecular configurations established at the initialization stage. The iter directories contain structures labeled by both AIMD and DPMD-FP methods for each iteration. Each folder is named according to the scheme of solvent molecule index followed by lithium salt designation. The finetuned_data folder contains SEI reaction simulation data used for fine-tuning the MLFFs. Comprehensive details regarding the solvent molecules are available in the solvent_with_smiles.xlsx file.