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Updated: Sep 16, 2025

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Data-driven microstructural optimization of Ag-Bi-I perovskite-inspired materials.

Kshithij Mysore Nandishwara1, Shuan Cheng1, Pengjun Liu2

  • 1Department of Mechanical Engineering, University of Washington, Seattle, WA USA.

Npj Computational Materials
|July 7, 2025
PubMed
Summary

The Daisy Visual Intelligence Framework (Daisy) uses AI to accelerate the discovery of new semiconductor materials by optimizing microstructural design. This AI framework significantly speeds up image analysis and synthesis planning for thin-film applications.

Keywords:
Materials for devicesTheory and computation

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Semiconductor Physics

Background:

  • Microstructural control in thin-film semiconductors is vital for photovoltaics and optoelectronics but remains a significant challenge.
  • Developing new materials is hindered by the difficulty in achieving desired microstructures.

Purpose of the Study:

  • To introduce the Daisy Visual Intelligence Framework (Daisy), an AI-driven system for optimizing thin-film semiconductor microstructures.
  • To accelerate the discovery and development of novel thin-film materials for electronic applications.

Main Methods:

  • Daisy integrates an image interpreter for extracting microstructural statistics (grain size, defects) and a reinforcement-learning planner for optimizing synthesis conditions.
  • The framework was tested using Ag-Bi-I perovskite-inspired materials as a case study.

Main Results:

  • Daisy demonstrated over 120x acceleration in image analysis and 87x in synthesis planning compared to manual methods.
  • Optimized AgBiI4 films were produced within 3.5 minutes from over 1700 conditions, exhibiting no pinholes and 14.5% larger average grain size.

Conclusions:

  • The Daisy framework significantly accelerates AI-driven microstructure development for emerging thin-film materials.
  • This work advances computational frameworks for self-driving laboratories, enabling faster material innovation.