  Amorphous materials exhibit unique properties that make them suitable for
various applications in science and technology, ranging from optical and
electronic devices and solid-state batteries to protective coatings. However,
data-driven exploration and design of amorphous materials is hampered by the
absence of a comprehensive database covering a broad chemical space. In this
work, we present the largest computed amorphous materials database to date,
generated from systematic and accurate \textit{ab initio} molecular dynamics
(AIMD) calculations. We also show how the database can be used in simple
machine-learning models to connect properties to composition and structure,
here specifically targeting ionic conductivity. These models predict the Li-ion
diffusivity with speed and accuracy, offering a cost-effective alternative to
expensive density functional theory (DFT) calculations. Furthermore, the
process of computational quenching amorphous materials provides a unique
sampling of out-of-equilibrium structures, energies, and force landscape, and
we anticipate that the corresponding trajectories will inform future work in
universal machine learning potentials, impacting design beyond that of
non-crystalline materials.
