This file is automatically generated. It contains the descriptions of each uploaded file as provided by the user.

• README.md: Explanation of the content of the repository
• environment.yml: YAML file to create a conda environment with all the required software to reproduce the results of the associated manuscript
• train_multiple_equivariants_qm7.zip: Data and scripts to train an equivariant model for dipole moments, polarizabilities, and hyperpolarizabilities of a subset of the QM7 dataset
• comparison_with_lambda_soap.zip: Data and scripts to obtain learning curves comparing the performances of the proposed machine learning model and an equivariant linear model
• water_ir_spectrum.zip: Data and scripts to compute the infrared spectrum of liquid water and compare the machine learning predictions with those of a classical force field.
• per_atom_equivariants_co2.zip: Data and scripts to compute the dataset and train ML models for Born effective charges and Raman tensors of CO2 to test the predictions of the model for per-atom properties