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Machine Learning Accelerated Insights of Perovskite Materials

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Artificial Intelligence for Materials Science

Part of the book series: Springer Series in Materials Science ((SSMATERIALS,volume 312))

Abstract

In recent years, lead-halide perovskite (LHP) have made tremendous progress in photovoltaic and optoelectronic fields. However, stability and toxicity still are obstacles for commercial application. These challenges have motivated significant efforts to search nontoxic and stable alternatives which could achieve comparable high performance with low-cost and facile fabrication methods. With continuing increasing computation powers, first-principles modeling combining with machine learning (ML) has made significant advances in the discovery of new perovskite materials. This provides invaluable insights into the physical origin for the high performance of perovskites in photovoltaic field, thereby facilitating the discovery of good absorber perovskite materials. This chapter aims to give a brief review of ML-guided design and discovery of perovskite materials for photovoltaics. Specifically, this chapter will introduce well-established ML models widely used in perovskite-related studies from both the construction of data and material representation aspects. The approaches of data sets will be discussed including the high-throughput (HT) computations and experimentations. The material representation will cover descriptors and feature engineering of perovskites in photovoltaic field. Then, we will give a general introduction of recent progress for ML models applications in perovskite solar cells. Conclusion and outlook will be given in the end.

Shuaihua Lu and Yilei Wu contributed equally with all other contributors.

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Acknowledgments

This work is supported by the National Key Research and Development Program of China (2017YFA0204800), Natural Science Foundation of China (21525311, 21773027, 22033002), the National Natural Science Foundation of Jiangsu (BK20180353), the Fundamental Research Funds for the Central Universities of China, and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX20_0075) in China.

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    1. School of Physics, Southeast University, Nanjing, China

      Shuaihua Lu, Yilei Wu, Ming-Gang Ju & Jinlan Wang

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    1. Shuaihua Lu
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    2. Yilei Wu
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    Correspondence to Ming-Gang Ju or Jinlan Wang .

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    1. Monash Suzhou Research Institute, Suzhou, China

      Yuan Cheng

    2. Institute of Artificial Intelligence, Beihang University, Beijing, China

      Tian Wang

    3. Institute of High Performance Computing, Singapore, Singapore

      Gang Zhang

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    Lu, S., Wu, Y., Ju, MG., Wang, J. (2021). Machine Learning Accelerated Insights of Perovskite Materials. In: Cheng, Y., Wang, T., Zhang, G. (eds) Artificial Intelligence for Materials Science. Springer Series in Materials Science, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-68310-8_8

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