The ab initio amorphous materials database: Empowering machine learning to decode diffusivity

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 ex-
ploration and design of amorphous materials is hampered by the absence of a com-
prehensive database covering a broad chemical space. In this work, we present
the largest computed amorphous materials database to date, generated from sys-
tematic and accurate 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 conductiv-
ity. 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 uni-
versal machine learning potentials, impacting design beyond that of non-crystalline
materials.