The datasets and final machine learning model files for the manuscript "Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates". Citation should refer directly to the manuscript: Chung, Y.; Green, W. H. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. Chemical Science 2024, doi: 10.1039/D3SC05353A To use the machine learning models, please refer to the sample files and instructions on https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML. Detailed information can be found in README.md file. Details on the files In the pretraining and finetuning set csv files, each column represents: rxn_smiles: atom-mapped reaction SMILES solvent_smiles: solvent SMILES ddGsolv: solvation free energy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target) ddHsolv: solvation enthalpy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target) dGsolv_reactant: solvation free energy of reactant(s) at 298K in kcal/mol (additional feature) dGsolv_product: solvation free energy of product(s) at 298K in kcal/mol (additional feature) dHsolv_reactant: solvation enthalpy of reactant(s) at 298K in kcal/mol (additional feature) dHsolv_product: solvation enthalpy of product(s) at 298K in kcal/mol (additional feature) Data sets under 'RxnSolvKSE_dataset_v1.1.zip' pretraining_set: contains the dataset used for pre-training all_data: contains all calculated data pretraining_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: contains both main prediction targets and additional feature for reaction-solvent pairs pretraining_solvent_info.csv: list of all solvents pretraining_unique_rxn.csv: list of all reactions, both forward and reverse directions chosen_500k_data: contains the chosen 500k data pretraining_rxn_solvent_ddGsolv_ddHsolv_500k.csv: contains main prediction targets for reaction-solvent pairs pretraining_features_react_prod_dGsolv_dHsolv_500k.csv: contains additional features for reaction-solvent pairs train_test_split: contains the 5-fold random split training and test sets. finetuning_set: contains the dataset used for fine-tuning all_data: contains all calculated data finetuning_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: constains both main prediction targets and additional features for reaction-solvent pairs. The rxn_key column indicates whether the reaction is bimolecular hydrogen abstraction (bihabs), unimolecular hydrogen migration (intrahabs), or radical addition to a multiple bond (raddition). The 'fwd' and 'rev' each indicate forward and reverse reactions. finetuning_solvent_info.csv: list of all solvents finetuning_unique_rxn.csv: list of all reactions, both forward and reverse directions chosen_data: contains chosen data finetuning_rxn_solvent_ddGsolv_ddHsolv_chosen.csv: contains main prediction targets for reaction-solvent pairs finetuning_features_react_prod_dGsolv_dHsolv_chosen.csv: contains additional features for reaction-solvent pairs experimental_set: contains the experimental rate constant data used to test the model. The original experimental data can be found at https://zenodo.org/record/7747557. expt_rxn_atom_mapped_smiles.csv: contains the atom-mapped reaction SMILES used for the experimental data. expt_data_collected.xlsx: contains all experimental data and detailed information expt_rxn_solv_smiles_with_features_all.csv: contains the computed additional features for the experimental reaction-solvent pairs. Machine learning model files under 'RxnSolvKSE_ML_model_files.zip' Contains the Chemprop machine learning model files for predicting ddGsolv and ddHsolv for a reaction-solvent pair. It takes atom-mapped reaction SMILES and solvent SMILES as inputs. To use these ML models, please refer to the sample files and instructions on https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML