Synthetic accessibility and stability rules of NASICONs
arXiv:2102.03627 · doi:10.1038/s41467-021-26006-3
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.
References in corpus (7)
- New Tolerance Factor to Predict the Stability of Perovskite Oxides and Halides
- Big Data of Materials Science - Critical Role of the Descriptor
- SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
- A Map of the Inorganic Ternary Metal Nitrides
- A critical examination of compound stability predictions from machine-learned formation energies
- The electronic-structure origin of cation disorder in transition-metal oxides
- The role of decomposition reactions in assessing first-principles predictions of solid stability