Ouyang Bin, Wang Jingyang, He Tanjin, Bartel Christopher J, Huo Haoyan, Wang Yan, Lacivita Valentina, Kim Haegyeom, Ceder Gerbrand
Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Department of Materials Science and Engineering, University of California, Berkeley, CA, USA.
Nat Commun. 2021 Oct 1;12(1):5752. doi: 10.1038/s41467-021-26006-3.
In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility. This machine-learned tolerance factor is based on the Na content, elemental radii and electronegativities, and the Madelung energy and can offer reasonable accuracy for separating stable and unstable NASICONs. This work will not only provide tools to understand the synthetic accessibility of NASICON-type materials, but also demonstrates an efficient paradigm for discovering new materials with complicated composition and atomic structure.
在本文中，我们开发了NASICON结构材料的稳定性规则，以此作为具有复杂键拓扑结构和组成的化合物的示例。通过对3881种潜在NASICON相进行第一性原理高通量计算，我们制定了NASICON的稳定性指导规则，并通过合成六种尝试材料验证了从头预测能力，其中五种成功合成。通过确定独立筛选和机器学习排序提取了一个用于预测NASICON稳定性的简单二维描述符，该描述符根据其合成可及性对NASICON相进行分类。这个机器学习的容忍因子基于钠含量、元素半径和电负性以及马德隆能量，能够为区分稳定和不稳定的NASICON提供合理的准确性。这项工作不仅将提供工具来理解NASICON型材料的合成可及性，还展示了一种发现具有复杂组成和原子结构的新材料的有效范例。
