Supporting information for a multifidelity neural network formulation for molecular potential energy surfaces

This is a supplementary information for our paper titled "Multifidelity neural network formulations for prediction of quantum chemistry potential energy surfaces" Supplemental information includes two data files corresponding to the complete sets of low and high fidelity training data used in numerical experiments. Format is JavaScript Object Notation (JSON). 1. low_fidelity_training_data.json contains 74000 records 2. high_fidelity_training_data.json contains 36988 records Each record consists of a numerical id ("id"), (x,y,z) position tuples ("geometry") for C5H5 ordered as 5 carbon atoms followed by 5 hydrogen atoms, and corresponding potential energy ("energy"). Source: normal mode sampling around 2 wells, 1 transition state, and a set of IRCs as depicted in Figure 2. Usage: subsets of this data were used as needed to define different data amounts and different subset randomizations in Figures 5 through 8.