Active learning assisted piezoelectric materials synthesis on the basis of composite decision-making

Abstract

The synthesis and development of novel materials for soft electronics, health monitoring, etc., have become a research hotspot. Traditional laboratory synthesis is significantly time-and-resource-consuming. Machine learning therefore becomes an ideal approach for expediting experimental process, constructing a virtual and automated closed-loop material synthesis and evaluation approach. In this work, we combined piezoelectric materials’ synthesis with machine learning to achieve automatic design optimization. 300 samples with different material recipes were used to train the initial active learning model. Thereafter, more samples were fabricated based on the recommended feasible recipes for each learning loop and then proceeded to the next round of learning. Through 10 active learning loops, 105 piezoelectric samples were stagewise fabricated. Moreover, a reverse design model based on Bayesian optimization is demonstrated, and Spearman's rank correlation coefficient and p values revealed the rules for the synthesis of piezoelectric materials. Finally, according to the setup model, we fabricate optimized piezoelectric materials, and demonstrate the application in cycling monitoring. We anticipate this work establishes an essential approach to accelerate the development of new materials.