Abstract by Chandramouli Nyshadham
Physics and Astronomy
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, MTP and Deep learning) machine learning models for predicting the ground state energies of solids. The database is generated using first-principles calculations. We present a comparison between performance of three different machine learning models.
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*Funding from ONR (MURI N00014-13-1-0635)