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

TL;DR

This paper introduces the largest ab initio amorphous materials database and demonstrates how machine learning models can accurately predict ionic diffusivity, facilitating efficient materials discovery and understanding.

Contribution

The work provides a comprehensive amorphous materials database generated from ab initio molecular dynamics and shows its application in machine learning for property prediction, especially ionic conductivity.

Findings

Database enables rapid diffusivity predictions

Machine learning models achieve high accuracy

Out-of-equilibrium structures inform future potentials

Abstract

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.