Modeling glasses from first-principles using random structure sampling

Abstract

We present an approach to approximating static properties of glasses without experimental inputs rooted in the first-principles random structure sampling. In our approach, the glassy system is represented by a collection (composite) of periodic, small-cell (few 10s of atoms) local minima on the potential energy surface. These are obtained by generating a set of periodic structures with random lattice parameters and random atomic positions, which are then relaxed to their closest local minima on the potential energy surface using the first-principles methods. Using vitreous SiO2 as an example, we illustrate and discuss how well various atomic and electronic structure properties calculated as averages over the set of such local minima reproduce experimental data. The practical benefit of our approach, which can be rigorously thought of as representing an infinitely quickly quenched liquid, is in that it transfers the computational burden to linearly scaling and easy to converge averages of properties computed on small-cell structures, rather than simulation cells with 100s if not 1000s of atoms while retaining a good overall predictive accuracy. Because of this it enables the future use of high-cost/high-accuracy electronic structure methods thereby bringing modeling of glasses and amorphous phases closer to the state of modeling of crystalline solids.