Materials prediction using machine learning: comparing MBTR, MTP and deep learning

With the advancement of supercomputers and electronic structure methods such as density functional theory, material scientists have developed huge computational databases of materials over the last two decades. The rate at which the material repositories increase their database determines the rate at which we can invent new materials. This necessitates faster surrogate models to replace the expensive methodology of density functional theory. In this regard the materials community have come up with quantum mechanics machine learning models, which are fast and accurate to describe the materials space of solids. We tested three different (MBTR[1], MTP[2] and Deep learning) machine learning models for predicting the ground state energies of solids. The database is generated using standard interatomic potentials. We present a comparison between performance of three different machine learning models.

[1] Huo, Haoyan, and Matthias Rupp. arXiv preprint arXiv:1704.06439 (2017).
[2] Shapeev, Alexander V. Multiscale Modeling and Simulation 14.3 (2016): 1153-1173.