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A Data-Driven Platform for Two-Dimensional Hybrid Lead-Halide Perovskites.

An Chen1, Zhilong Wang1, Jing Gao1

  • 1Key Laboratory for Thin Film and Microfabrication of Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China.

ACS Nano
|July 5, 2023
PubMed
Summary

A new classification method using inorganic structure factors (SF) effectively explores two-dimensional hybrid organic-inorganic lead-halide perovskites (2D HOIPs). This enables the discovery of novel 2D HOIPs for optoelectronics and solar energy applications.

Keywords:
data-driven platformdatabaseshybrid organic−inorganic lead-halide perovskitesmachine learningtwo-dimensional materials

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Solid-State Chemistry

Background:

  • Two-dimensional hybrid organic-inorganic lead-halide perovskites (2D HOIPs) exhibit exceptional optoelectronic properties, driving their use in solar energy conversion.
  • The vast structural diversity of 2D HOIPs necessitates advanced methods for performance optimization.
  • Existing classification methods, like RP-DJ, are insufficient for correlating structure with electronic properties in 2D HOIPs.

Purpose of the Study:

  • To develop a novel classification descriptor for 2D HOIPs that accurately reflects structural influences on electronic properties.
  • To create a comprehensive database of 2D HOIPs and their properties using machine learning.
  • To establish an integrated platform for exploring and discovering new 2D HOIP materials.

Main Methods:

  • Utilized inorganic structure factors (SF) as a descriptor, accounting for inorganic layer distortion in 2D HOIPs.
  • Investigated the relationship between SF, physicochemical features, and band gaps.
  • Employed machine learning to generate a large-scale database (304,920 2D HOIPs) and an exploration platform.

Main Results:

  • Successfully correlated inorganic structure factors with the electronic properties (band gaps) of 2D HOIPs.
  • Generated a database of 304,920 2D HOIPs, including many previously unexplored materials.
  • Developed a user-friendly platform for searching, downloading, analyzing, and predicting 2D HOIP properties.

Conclusions:

  • Inorganic structure factors provide an effective means to classify and explore the performance of 2D HOIPs.
  • The developed database and platform significantly accelerate the discovery of novel 2D HOIP materials.
  • This work offers a powerful tool for advancing optoelectronics and solar energy conversion through rational material design.