Using Machine Learning Models to Predict Higher-Level Quantities from Energy Models

Machine learning methods are now used more and more as a substitute for Density Functional Theory calculations due to their low computational costs. However, in some cases, relevant datasets are not available, and the effort that would be necessary to generate this data suppresses the advantages of using machine learning to speed up the calculations. Furthermore, the process of training a reliable model is not trivial and can also be expensive. For those reasons, we are developing a python package named ML_Calc_Driver¹, which goal is to allow to use portable trained models to make predictions easily. Energy datasets are numerous and, through the use of implemented finite difference workflows, can be used to predict higher-level quantities such as forces, phonon energies, and infrared intensities. As of now, the package is interfaced to use SchNetPack² trained models, and more model types can easily be added as our workflows are independent of the actual calculators.

1. https://github.com/OMalenfantThuot/ML_Calc_Driver
2. Schütt, K. T. And al. https://doi.org/10.1021/acs.jctc.8b00908.