Scholarlink

The implementation of machine learning (ML) techniques in materials discovery has been a revolutionary approach in identifying novel and promising materials with desired properties and has deepened our understanding of the relationships between materials and their properties. This chapter explores the application of ML in the identification and optimization of lead-free perovskite materials, with a focus on their photocatalytic applications. It also provides an overview of the general stages of ML processes from data collection and preparation to model evaluation and application. To highlight the advantages and the limitations of ML in materials discovery compared to the experimental methods, three case studies are thoroughly reviewed.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Computational Approaches to Guide the Synthesis of Lead-Free Perovskite Photocatalysts

  • 章节

  • 发表日期：

    2025年

查看全文
  • Amir Mahboud,
  • Haoxin Mai,
  • Tu C. Le,
  • Rachel A. Caruso

摘要

The implementation of machine learning (ML) techniques in materials discovery has been a revolutionary approach in identifying novel and promising materials with desired properties and has deepened our understanding of the relationships between materials and their properties. This chapter explores the application of ML in the identification and optimization of lead-free perovskite materials, with a focus on their photocatalytic applications. It also provides an overview of the general stages of ML processes from data collection and preparation to model evaluation and application. To highlight the advantages and the limitations of ML in materials discovery compared to the experimental methods, three case studies are thoroughly reviewed.