Alternative approaches to traditional force fields have recently been growing in popularity, where highly flexible potentials are optimized entirely on known data under the umbrella term of machine learning . Machine learning in chemistry has grown rapidly in the last decade , with successful results in potential energy surface prediction , energy corrections between levels of theory , predicting chemical properties , and promising indications of predicting chemical phenomena that are not explicitly defined in the dataset .