  Despite the extensive usage of oxide glasses for a few millennia, the
composition-property relationships in these materials still remain poorly
understood. While empirical and physics-based models have been used to predict
properties, these remain limited to a few select compositions or a series of
glasses. Designing new glasses requires a priori knowledge of how the
composition of a glass dictates its properties such as stiffness, density, or
processability. Thus, accelerated design of glasses for targeted applications
remain impeded due to the lack of universal composition-property models.
Herein, using deep learning, we present a methodology for the rational design
of oxide glasses. Exploiting a large dataset of glasses comprising of up to 37
oxide components and more than 100,000 glass compositions, we develop
high-fidelity deep neural networks for the prediction of eight properties that
enable the design of glasses, namely, density, Young's modulus, shear modulus,
hardness, glass transition temperature, thermal expansion coefficient, liquidus
temperature, and refractive index. These models are by far the most extensive
models developed as they cover the entire range of human-made glass
compositions. We demonstrate that the models developed here exhibit excellent
predictability, ensuring close agreement with experimental observations. Using
these models, we develop a series of new design charts, termed as glass
selection charts. These charts enable the rational design of functional glasses
for targeted applications by identifying unique compositions that satisfy two
or more constraints, on both compositions and properties, simultaneously. The
generic design approach presented herein could catalyze machine-learning
assisted materials design and discovery for a large class of materials
including metals, ceramics, and proteins.
