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高性能压电陶瓷是现代电子设备中至关重要的一类材料。为追求可持续发展，以PbZr1−xTixO3 (d33 ≈ 200-1500 pC/N)为主的传统铅基压电材料正在逐步过渡到(K, Na)NbO3 (KNN)基等无铅压电陶瓷。经过数十年的努力，KNN基陶瓷压电系数(d33)不断突破(d33 ≈ 100-650 pC/N)，同时也积攒了大量的文献数据有待进一步挖掘，但基于传统经验试错法的研究范式难以拜托耗时耗力的局限性。近年来，伴随着人工智能和材料信息学的发展，机器学习在挖掘材料结构/组分-性能方面展现出强大的潜力。同时，符号回归、确定独立性筛选和稀疏化算子（SISSO）等兼具性能和可解释性的机器学习方法也应运而生，为非计算机领域的研究人员提供了更直观和易于理解的新研究途径。
Fig. 1 Feature-assisted SISSO ML framework for d33 prediction and explicit descriptor mining
福州大学吴啸副教授、萨百晟教授联合北京航空航天大学孙志梅教授团队，通过耦合特征工程、机器学习回归和SISSO算法，构建了一种d33描述符开发框架，提出一个仅包含4种易于获取参数的描述符：
用于预测KNN基陶瓷的d33。
Fig. 2 ML model training.
该研究从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。作者根据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。最后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。
Fig. 3 Descriptor generation and performance.
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。
Fig. 4 Mapping analysis of D4simp and phase boundaries.
该文近期发表于npj Computational Materials 9：229(2023)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。
An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K0.5Na0.5)NbO3-based ceramics
Bowen Ma, Xiao Wu, Chunlin Zhao, Cong Lin, Min Gao, Baisheng Sa & Zhimei Sun
Perovskite-type lead-free piezoelectric ceramics allow access to illustrious piezoelectric coefficients (d33) through intricate composition design and experimental modulation. Developing a swift and accurate technology for identifying (K, Na)NbO3 (KNN)-based ceramic compositions with high d33 in exceedingly large “compositional” space will establish an innovative research paradigm surpassing the traditional empirical trial-and-error method. Herein, we demonstrate an interpretable machine learning (ML) framework for quick evaluation of KNN-based ceramics with high d33 based on data from published literature. Specifically, a thorough feature construction was carried out from the global and local dimensions to establish tree regression models with d33 as the target property. Subsequently, the feature-property mapping rules of KNN-based piezoelectric ceramics are further optimized through feature screening. To intuitively understand the correlation mechanisms between ML regression targets and features, the sure independence screening and sparsifying operator (SISSO) method was employed to extract the essential descriptors to explain d33. A straightforward descriptor, "e" ^("(" 〖"NM" 〗_"B" -〖"MV" 〗_"B"  ")" ) "⋅ST/(I" "D" _"A"  ")" ^"2" , consisting of only four easily accessible parameters, can accelerate the evaluation of a series of novel KNN-based ceramics with high d33 while exhibiting strong theoretical interpretability. This work not only provides a tool for the rapid discovery of high piezoelectric performance in KNN-based ceramics but also offers a data-driven route for the design of property descriptors in perovskites.
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