Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory

Yilei Wu, Chang-Feng Wang, Ming-Gang Ju (), Qiangqiang Jia, Qionghua Zhou, Shuaihua Lu, Xinying Gao, Yi Zhang () and Jinlan Wang ()
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Yilei Wu: Southeast University
Chang-Feng Wang: Zhejiang Normal University
Ming-Gang Ju: Southeast University
Qiangqiang Jia: Zhejiang Normal University
Qionghua Zhou: Southeast University
Shuaihua Lu: Southeast University
Xinying Gao: Southeast University
Yi Zhang: Zhejiang Normal University
Jinlan Wang: Southeast University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.

Date: 2024
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DOI: 10.1038/s41467-023-44236-5

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