Accelerated Discovery of Multifunctional K_0.5Na_0.5NbO₃-Based Ceramics via Integrated High-Throughput Computation and Machine Learning

Abstract

Potassium sodium niobate (KNN)-based ceramics have attracted significant interest due to their strong piezoelectric response, distinct photochromic, and photoluminescent behaviors, demonstrating great potential for applications in medical devices and optical securities. Recent breakthroughs in artificial intelligence have facilitated the use of machine learning (ML) in KNN-based ceramics. However, conventional global ML modeling approaches tend to overlook the decisive role of local atomic environments, especially those introduced by dopants, which critically govern the ceramic properties. Herein, a robust database containing 300 entries for key properties of KNN-based ceramics is constructed through high-throughput density functional theory calculations, whose reliability is benchmarked against experimental data. Furthermore, we implement an ML approach that specifically emphasizes the features describing the local coordination of dopants to map the relationships between doping behaviors and the structural stability and electronic structure of KNN-based ceramics. Moreover, the analysis of feature importance yields physically meaningful design rules that directly link atomic scale to functional performance. This work accelerates the development of KNN-based ceramics with electro-optical multifunctional coupling by establishing the critical influence of local structure on macroscopic properties.

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