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Atomistic simulations of $({\mathrm{Na}}_{2}\mathrm{O}{)}_{x}({\mathrm{SiO}}_{2}{)}_{1\ensuremath{-}x}$ glasses are carried out using the building-block method that uses copies of low-energy units, ``building blocks,'' to build large realistic structural models. The calculated pair-correlation functions show that the local structure of these glasses is in good agreement with diffraction experiments. The electronic density of states for the doped models reveals defects in the band gap close to the conduction tail that are localized around undercoordinated Na atoms. Thermal properties for the systems, including the thermal expansion coefficient, are studied within the quasiharmonic approximation, and compare favorably with experiment. The elastic properties of the glasses are studied by calculating bulk and shear modulus.

The behavior of an atom in a molecule, liquid or solid is governed by the force it experiences. If the dependence of this vectorial force on the atomic chemical environment can be $learned$ efficiently with high-fidelity from benchmark reference results-using "big data" techniques, i.e., without resorting to actual functional forms-then this capability can be harnessed to enormously speed up $in \ silico$ materials simulations. The present contribution provides several examples of how such a $force$ field for Al can be used to go far beyond the length-scale and time-scale regimes accessible presently using quantum mechanical methods. It is argued that pathways are available to systematically and continuously improve the predictive capability of such a learned force field in an adaptive manner, and that this concept can be generalized to include multiple elements., Comment: 5pages, 3 figures