astuke/HyperTune: Optimization of machine learning hyperparameters in chemical physics

Python code for the optimization of hyperparameters in machine learning (ML), applied to a problem in chemical physics. The ML model at hand is based on kernel ridge regression (KRR) and predicts molecular orbital energies. The hyperparameters in this setup stem from two sources: the KRR method itself and the descriptors for the atomic structure of molecules, resulting in the simultaneous optimization of up two 6 hyperparameters. This repository includes code for three different optimization methods: Bayesian optimization (BO), grid search and random search.

The title and description of this software correspond to the situation when the software metadata was imported to ACRIS. The most recent version of metadata is available in the original repository.