An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K_0.5Na_0.5)NbO₃-based ceramics

Bowen Ma, Xiao Wu, Chunlin Zhao, Cong Lin, Min Gao, Baisheng Sa & Zhimei Sun

npj Computational Materials 9: 229 (2023); Published online: 22 December 2023

Editorial Summary

Interpretable KNN ceramic high piezoelectric coefficients: Fast and good?

People have invested a lot of time and energy in making piezoelectric ceramics lead-free. Through cumbersome and complicated composition control, the piezoelectric coefficient of KNN-based ceramics has been breaking through year by year. On the one hand, the traditional empirical trial-and-error research paradigm has the limitation of time-consuming consumables. On the other hand, the literature data related to the piezoelectric coefficient (d₃₃) of KNN-based ceramics accumulated in the past is extremely valuable to explore. This study constructed a d₃₃ descriptor development framework and proposed a descriptor containing only four easily accessible parameters: is used to predict d₃₃. This descriptor can also explain the high d₃₃ mechanism caused by the coexistence of multi-phase KNN-based ceramics. The team of Prof. Wu Xiao and Prof. Sa Baisheng from Fuzhou University and Prof. Sun Zhimei from Beihang University established a regression mapping of the chemical composition of KNN-based ceramics with d₃₃ from 1113 data points in 244 published articles by coupling feature engineering, machine learning regression and the SISSO algorithm. This study constructed global and local features based on the element position configuration of ABO₃-type perovskites, and then used methods such as Pearson correlation screening, feature importance, and feature exhaustion to screen key features. The optimal extreme random tree regression model has a leave-one-out cross-validation error as low as ±49pC/N.The author used the optimal feature set for the SISSO descriptor search and obtained a descriptor that has an intuitive changing trend with d₃₃.At the same time, the descriptor value also has a mapping relationship with the KNN-based ceramic phase boundary, that is, chemical compositions with descriptor values in a smaller range are more likely to obtain high d₃₃.This method has been verified in the newly published 63 KNN-based ceramic high piezoelectric coefficient compositions. This study established a mathematical mapping model of the d₃₃-composition-phase boundary in KNN-based ceramics, providing a highly intuitive and instructive way to improve the performance of perovskites, and overcoming the low reliability and difficult interpretation problems of traditional machine learning models. 

编辑概述

可解释KNN陶瓷高压电系数的机器学习：又快又好？

针对压电陶瓷的无铅化人们投入了大量的时间和精力，通过繁琐和复杂的组分调控，(K, Na)NbO₃(KNN)基陶瓷压电系数逐年突破。一方面，传统经验试错的研究范式存在耗时耗材的局限性，另一方面，过去积累的KNN基陶瓷压电系数(d₃₃)相关的文献数据极具挖掘价值。该研究构建了一个d₃₃描述符开发框架，提出一个仅包含4个易于获取参数的描述符：用于预测d₃₃，该描述符还能够解释KNN基陶瓷多相共存引发的高d₃₃机理。来自福州大学的吴啸副教授、萨百晟教授和北京航空航天大学的孙志梅教授团队通过耦合特征工程、机器学习回归和SISSO算法，从244篇已发表的1113个数据点中建立了KNN基陶瓷化学成分与d₃₃的回归映射。该研究根据ABO₃型钙钛矿的元素位置构型，分别从全局和局部进行特征构造，然后利用Pearson相关性筛选、特征重要性和特征穷尽等方法对关键特征进行筛选，最优的极端随机树回归模型的留一交叉验证误差最低至±49 pC/N。作者将最优的特征集用于SISSO描述符搜索，得到了一个和d₃₃有直观变化趋势的描述符，同时该描述符数值还与KNN基陶瓷相界具备映射关系，即描述符值在较小区间的化学成分更容易获得高压电系数。这一方法在最新发表的63个KNN基陶瓷高压电系数组分中得到验证。该研究在KNN基陶瓷中建立了d₃₃-组分-相界三者的数学映射模型，为提高钙钛矿的性能提供了一种高度直观和指导性的途径，克服了传统机器学习模型低可信度和难解释的问题。