Synthetic accessibility and stability rules of NASICONs

TL;DR

This paper develops a machine learning-based stability rule for NASICON materials, enabling efficient prediction of stable compounds with complex compositions and atomic structures.

Contribution

It introduces a simple two-dimensional descriptor, the 'tolerance factor', derived from machine learning, to predict NASICON stability, advancing materials discovery methods.

Findings

High accuracy in separating stable from unstable NASICONs

Successful experimental validation of predicted stable compositions

Demonstrates an efficient paradigm for discovering complex materials

Abstract

In this paper we develop the stability rules for NASICON structured materials, as an example of compounds with complex bond topology and composition. By applying machine learning to the ab-initio computed phase stability of 3881 potential NASICONs we can extract a simple two-dimensional descriptor that is extremely good at separating stable from unstable NASICONS. This machine-learned "tolerance factor" contains information on the Na content, the radii and electronegativities of the elements, and the Madelung energy. We test the predictive capability of this approach by selecting six predicted NASICON compositions. Five out of the six resulted in a phase pure NASICON while the sixth composition led to a NASICON that coexisted with other phases, validating the efficacy of this approach. This work not only provide tools to understand synthetic accessibility of NASICON-type materials, but also demonstrate an efficient paradigm for discovering new materials with complicate composition and atomic structure.