多相催化的大型语言模型

IF 27 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Yiwen Yao, Jinbo Zhu, Yan Liu, Guanpeng Ren, Xiao-Yan Li, Pengfei Ou
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引用次数: 0

摘要

多相催化在化工制造和可持续技术中有着广泛的应用。它使用固体催化来实现有效的化学转化。传统的活性位点和反应机理研究很大程度上依赖于实验和计算方法,如密度泛函理论计算。然而,科学文献和数据的数量正在快速增长。这种快速的增长使得系统地捕捉、处理和对新兴见解采取行动变得越来越困难。近年来,大型语言模型(llm)已成为支持催化研究各个阶段的强大工具。他们理解和生成自然语言的能力帮助他们从大量的文本中提取有用的信息,协助催化剂设计,帮助规划实验,并澄清复杂的描述符。在这篇高级综述中,我们首先分析了法学硕士在多相催化领域的最新进展,重点关注四个关键领域:文献挖掘和知识提取,催化剂设计和筛选,实验自动化和工作流程优化,以及高维描述符的解释。然后,我们强调了该领域的挑战,尽管取得了这些进步,最值得注意的是需要特定领域的微调和分子表征的改进。最后,我们讨论了将llm与互补的机器学习方法和专家在环系统相结合的未来机会,以加速下一代催化剂的合理发现。本文分类如下:
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Large Language Models for Heterogeneous Catalysis

Large Language Models for Heterogeneous Catalysis

Large Language Models for Heterogeneous Catalysis

Large Language Models for Heterogeneous Catalysis

Heterogeneous catalysis has a wide range of applications in chemical manufacturing and sustainable technologies. It uses solid catalysis to enable efficient chemical transformations. Traditional research on active sites and reaction mechanisms relies heavily on experiments and computational methods, such as density functional theory calculations. However, the volume of scientific literature and data is growing fast. This rapid growth has made it increasingly difficult to capture, process, and act on emerging insights systematically. Recently, large language models (LLMs) have emerged as powerful tools to support various stages in catalysis research. Their ability to understand and generate natural language helps them extract useful information from vast amounts of text, assist in catalyst design, aid in planning experiments, and clarify complex descriptors. In this advanced review, we first analyze recent progress in applying LLMs to heterogeneous catalysis, focusing on four key areas: literature mining and knowledge extraction, catalyst design and screening, experiment automation and workflow optimization, and the interpretation of high-dimensional descriptors. We then highlight the challenges in this field despite these advances, most notably the need for domain-specific fine-tuning and the improvement of molecular representation. We conclude by discussing future opportunities for integrating LLMs with complementary machine learning approaches and expert-in-the-loop systems, toward accelerating the rational discovery of next-generation catalysts.

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来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
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