Data supporting "Learning Pairwise Interaction for Extrapolative and Interpretable Machine Learning Interatomic Potentials with Physics-informed Neural Network"

Data supporting "Learning Pairwise Interaction for Extrapolative and Interpretable Machine Learning Interatomic Potentials with Physics-informed Neural Network"This archive contains training data used for models in the manuscript, which are created from the authors or modified/subsampled from public datasets. All molecular geometries are saved as extended xyz (extxyz) format.Dataset file descriptionEthanol`ethanol/ethanol_dissociation.xyz`: The geometries for calculating PES of ethanol dissociation.`ethanol/ethanol_optimized.xyz`: The optimized geometry used for computing pairwise interaction energies and intrinsic bond strength index (IBSI).Extrapolation dataThe files listed in `extrapolation_data` are the dataset for Figure 2 of the manuscript, which are splitted to train and test dataset as described in the manuscript.SN2 reaction`sn2_reaction/sn2_reaction_I_Br.xyz`: The subset of publicly available SN2 reaction of methyl halide[1], only containing I and Br as halogen species.`sn2_reaction/sn2_I_Br_subsampled.xyz`: Normal mode sampled (NMS) images & AIMD trajectory of the reaction CH3I + Br- CH3Br + I-.`sn2_reaction/sn2_I_Br_noTS.xyz`: NMS-only dataset for the same reaction.Liquid waterClassical MD trajectory of liquid water. The original dataset is created from the authors of [2]. We subsampled the dataset with 500 geometries.`water/water_0.5k_train.xyz`: 450 geometries for training the model`water/water_0.5k_val.xyz`: 50 geometries for validation steps.References[1] Unke, O. T.; Meuwly, M. PhysNet: A neural network for predict-ing energies, forces, dipole moments, and partial charges. Journal of chemical theory and computation 2019, 15 (6), 3678-3693[2] Fu, X.; Wu, Z.; Wang, W.; Xie, T.; Keten, S.; Gomez-Bombarelli, R.; Jaakkola, T. Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations. 2023; https://arxiv.org/abs/2210.07237.