This paper proposes a universal framework integrating high-throughput experiments, chemical knowledge, and machine learning (ML) techniques to guide the synthesis of 2D silver/bismuth organic-inorganic hybrid perovskites. The framework uses subgroup discovery and support vector machines to uncover structure-property relationships and screen for materials with high synthesis feasibility. Applying this approach increased the synthesis success rate fourfold compared to traditional methods, offering a practical route for accelerating material synthesis with limited resources.
Publisher
Nature Communications
Published On
Jan 02, 2024
Authors
Yilei Wu, Chang-Feng Wang, Ming-Gang Ju, Qiangqiang Jia, Qionghua Zhou, Shuaihua Lu, Xinying Gao, Yi Zhang, Jinlan Wang
Tags
machine learning
material synthesis
2D perovskites
structure-property relationship
high-throughput experiments
support vector machines
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