Chen Zhiwei, Liu Wei, Shan Bing, Pei Yanzhong
Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Tongji University, Shanghai 201804, China.
Natl Sci Rev. 2024 Aug 1;11(9):nwae269. doi: 10.1093/nsr/nwae269. eCollection 2024 Sep.
Crystalline matters with periodically arranged atoms found wide applications in modern science and technology. To facilitate the design of new materials and the advancement of existing ones, accurate and efficient models without relying too much on known inputs for predicting the functionalities are essential. Here, we propose an analytical approach for such a purpose, with only the knowledge of the structural chemistry of crystals. Based on the electrostatic interaction between periodically arranged atoms, the 1st, 2nd and 3rd derivatives of interatomic potential, respectively, enable a prediction of ten kinds in total of mechanical, acoustical and thermal properties. Over a thousand measurements are collected from ∼500 literatures, this results in the symmetric mean percentage error (SMPE) within ±25% and the symmetric mean absolute percentage error (SMAPE) ranging from 22%∼74% across all properties predicted, which further enables a revelation of bond characteristics as the most important but implicit origin for functionalities.
具有周期性排列原子的晶体物质在现代科学技术中有着广泛的应用。为了促进新材料的设计和现有材料的改进,不依赖太多已知输入来预测功能的准确高效模型至关重要。在此,我们为此目的提出一种分析方法,仅需晶体结构化学知识。基于周期性排列原子间的静电相互作用,原子间势的一阶、二阶和三阶导数分别能够预测总共十种机械、声学和热学性质。从约500篇文献中收集了超过一千次测量数据,在所有预测性质中,对称平均百分比误差(SMPE)在±25%以内,对称平均绝对百分比误差(SMAPE)在22%至74%之间,这进一步揭示了键特征是功能最重要但隐含的起源。