Datasets and ML/MM models for simulating uracil, N-methylacetamide and alanine dipeptide

The same types of files are provided for the three molecules studied: uracil, N-methylacetamide, and alanine dipeptide. Datasets These archives contain the training and test sets for each molecule and each level of theory. The first artificial dataset was generated using normal-mode displacements combined with random charges placed on external points around the molecule. The validation test sets were extracted from ML/MM simulations in solvent obtained with the trained models. The two reference levels of theory are ωB97XD/6-31G(d) and B2PLYP-D3/cc-pVTZ. For each molecule we provide datasets_*.tar.gz, which includes: Artificial training and test sets training-set_wB97XD_6-31G(d).npz - 1000 samples; training-set_B2PLYPD3_cc-pVTZ.npz - 400 samples; test-set_wB97XD_6-31G(d).npz - 200 samples; test-set_B2PLYPD3_cc-pVTZ.npz - 200 samples; Test sets extracted from ML/MM simulations test-set_dynamics_wB97XD_6-31G(d).npz - 500 samples; test-set_dynamics_B2PLYPD3_cc-pVTZ.npz - 250 samples. Each dataset contains QM and MM coordinates, MM charges, vacuum energies and forces, vacuum dipole moments, QM/MM energies and forces, and QM/MM dipole moments. Dipoles from dynamics These archives contain the dipole moments used for IR spectra, extracted from ML/MM simulations. Uracil - dipoles_from_dynamics_uracil.tar.gz: gas-phase and water ML/MM simulations of 1 ns using the base-models (Mbase_vacuum.npy, Mbase_water.npy); gas-phase and water ML/MM simulations of 1 ns using the Δ-learning correction only for the vacuum (Mdelta_vacuum.npy, Mdelta_water.npy); water ML/MM simulations of 1 ns using the Δ-learning correction for both vacuum and environment (Mdelta_deltaEnv_water.npy); short simulation of 10 ps of aqueous uracil using ML/MM and QM/MM (short_Mbase_MLMM.npy, short_QMMM.npy); N-methylacetamide - dipoles_from_dynamics_nmethylacetamide.tar.gz: gas-phase and water ML/MM simulations of 1 ns using the base-models (Mbase_vacuum.npy, Mbase_water.npy); gas-phase and water ML/MM simulations of 1 ns using the Δ-learning correction only for the vacuum (Mdelta_vacuum.npy, Mdelta_water.npy); Alanine dipeptide - dipoles_from_dynamics_alanine_dipeptide.tar.gz: water and DMSO ML/MM simulations of 100 ps starting from αR and PII conformations. For αR we report only the replicas that remained in this conformation, whereas for PII we considered all the replicas that stayed in the PII or β conformation. These simulations were run with both the base-models (Mbase_water_*.npy, Mbase_DMSO_*.npy) and correcting with Δ-learning the vacuum part (Mdelta_water_*.npy, Mdelta_DMSO_*.npy). Models These archives contain all parameters for the machine learning models trained with GPX (permut_symm branch). For the base-models the reference level of theory is ωB97XD/6-31G(d) and for the Δ-learning ones is B2PLYP-D3/cc-pVTZ. models_uracil.tar.gz: vaccum and environment base-models (modelvacgs.npz, modelenvgs.npz), and vacuum and environment Δ-learning models (modelvacgsdelta.npz, modelenvgsdelta.npz); models_nmethylacetamide.tar.gz: vaccum and environment base-models (modelvacgs.npz, modelenvgs.npz), and vacuum Δ-learning model (modelvacgsdelta.npz); models_alanine_dipeptide.tar.gz: vaccum and environment base-models (modelvacgs.npz, modelenvgs.npz), and vacuum Δ-learning model (modelvacgsdelta.npz). These models can be directly used in ML-server to run ML/MM simulations. Scripts for dataset generation The archive scripts_for_datagen.tar.gzincludes two python scripts for generating geometries of isolated molecules and artificial environment configurations: nm_displacement.py: reads the high-precision normal modes computed by Gaussian and the equilibrium geometry to compute normal-mode displacements; gen_charges.py: reads the isolated molecule geometry from Gaussian input files and the Mulliken charges from Gaussian output files, calculates a layered grid of points around the molecule, and selects a subset on which random external charges are placed.