Scholarlink

<p>Reticular chemistry is the science of connecting molecular building units into crystalline extended structures such as metal–organic frameworks and covalent organic frameworks. Large language models (LLMs), a type of generative artificial intelligence system, can augment laboratory research in reticular chemistry by helping scientists to&#xa0;extract knowledge from literature, design materials and collect and interpret experimental data — ultimately accelerating scientific discovery. In this Perspective, we explore the concepts and methods used to apply LLMs in research, including prompt engineering, knowledge and tool augmentation and fine-tuning. We discuss how ‘chemistry-aware’ models can be tailored to specific tasks and integrated into existing practices of reticular chemistry, transforming the traditional ‘make, characterize, use’ protocol driven by empirical knowledge into a discovery cycle based on finding synthesis–structure–property–performance relationships. Furthermore, we explore how modular LLM agents can be integrated into multi-agent laboratory systems, such as self-driving robotic laboratories, to streamline labour-intensive tasks and collaborate with chemists and how LLMs can lower the barriers to applying generative artificial intelligence and data-driven workflows to such challenging research questions as crystallization. This contribution equips both computational and experimental chemists with the insights necessary to harness LLMs for materials discovery in reticular chemistry and, more broadly, materials science.</p>

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

Large language models for reticular chemistry

  • 期刊论文

  • 发表日期：

    2025年2月1日

查看全文
  • Zhiling Zheng,
  • Nakul Rampal,
  • Theo Jaffrelot Inizan,
  • Christian Borgs,
  • Jennifer T. Chayes,
  • Omar M. Yaghi

摘要

Reticular chemistry is the science of connecting molecular building units into crystalline extended structures such as metal–organic frameworks and covalent organic frameworks. Large language models (LLMs), a type of generative artificial intelligence system, can augment laboratory research in reticular chemistry by helping scientists to extract knowledge from literature, design materials and collect and interpret experimental data — ultimately accelerating scientific discovery. In this Perspective, we explore the concepts and methods used to apply LLMs in research, including prompt engineering, knowledge and tool augmentation and fine-tuning. We discuss how ‘chemistry-aware’ models can be tailored to specific tasks and integrated into existing practices of reticular chemistry, transforming the traditional ‘make, characterize, use’ protocol driven by empirical knowledge into a discovery cycle based on finding synthesis–structure–property–performance relationships. Furthermore, we explore how modular LLM agents can be integrated into multi-agent laboratory systems, such as self-driving robotic laboratories, to streamline labour-intensive tasks and collaborate with chemists and how LLMs can lower the barriers to applying generative artificial intelligence and data-driven workflows to such challenging research questions as crystallization. This contribution equips both computational and experimental chemists with the insights necessary to harness LLMs for materials discovery in reticular chemistry and, more broadly, materials science.