Machine learning modeling for the prediction of materials energy

Machine learning (ML) is a fast-evolving field of artificial intelligence that has been applied in many domains due to the increasing availability of computerized databases, including materials science; for instance, validating crystal descriptors for energy prediction poses difficult problems. This work investigates machine learning models to substitute the laboratory crystal energy prediction using two- and three-body distribution functions as structural and atomic descriptors. To achieve this, ML algorithms were used notably ElasticNet, Bayesian Ridge, Random Forest, Support Vector Machine, and Deep Neural Networks to model structural descriptors. Moreover, a non-conventional Deep Neural Networks topology was developed and implemented to model atomic descriptors. Five-fold cross-validation procedure was performed on each model; quality assessment metrics were else used for testing and evaluation in order to identify the most robust descriptors. Finally, the best result of energy prediction was achieved by combining both two- and three-body atomic distribution functions.