General machine learning models for materials prediction

Machine learning tools applied to problems in materials science are transforming the way we predict properties of materials. These tools enable us to compute properties of materials with the accuracy of quantum mechanics at a fraction of the time. We present five general machine learning based models which were used to simultaneously predict formation energies of 10 different materials (AgCu, AlFe, AlMg, AlNi, AlTi, CoNi, CuFe, CuNi, FeV, NbNi). We show that the results of using machine learning for materials prediction are independent of the particular model used. Prediction errors of all five models were found to qualitatively agree, with errors of the order of 1, meV/atom.