  Gaussian process regression (GPR) is a useful technique to predict
composition--property relationships in glasses as the method inherently
provides the standard deviation of the predictions. However, the technique
remains restricted to small datasets due to the substantial computational cost
associated with it. Here, using a scalable GPR algorithm, namely, kernel
interpolation for scalable structured Gaussian processes (KISS-GP) along with
massively scalable GP (MSGP), we develop composition--property models for
inorganic glasses based on a large dataset with more than 100,000 glass
compositions, 37 components, and nine important properties, namely, density,
Young's, shear, and bulk moduli, thermal expansion coefficient, Vickers'
hardness, refractive index, glass transition temperature, and liquidus
temperature. Finally, to accelerate glass design, the models developed here are
shared publicly as part of a package, namely, Python for Glass Genomics
(PyGGi).
