大学化学

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机器学习在化学领域中的应用

张思源, 张志成, 李荣金   

  1. 天津大学理学院化学系,有机集成电路教育部重点实验室,天津市分子光电科学重点实验室,天津 300072
  • 收稿日期:2024-04-25 录用日期:2024-06-05
  • 通讯作者: 李荣金 E-mail:lirj@tju.edu.cn
  • 基金资助:
    国家自然科学基金面上项目(52073206, 52273193)

Applications of Machine Learning in Chemistry

Siyuan Zhang, Zhicheng Zhang, Rongjin Li   

  1. Key Laboratory of Organic Integrated Circuit, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin 300072, China
  • Received:2024-04-25 Accepted:2024-06-05
  • Contact: Rongjin Li E-mail:lirj@tju.edu.cn

摘要: 在计算机科学和技术的推动下,机器学习已成为化学研究的强大工具。本文首先介绍了机器学习的基本概念,随后探讨了其在化学领域的四个关键应用:在有机全合成中预测合成路径;在原子模拟中对势能面进行高效采样和重建;在异相催化设计中用于反应路径揭示和催化剂筛选;以及在核磁共振谱学中的信号处理和解析。本文还介绍了移动机器人化学家的概念,展示了机器学习与自动化技术结合的潜力。最后,本文对机器学习在未来化学研究中的发展前景进行了展望,指出了其可能带来的变革性影响。

关键词: 机器学习, 深度学习, 机器人化学家

Abstract: Driven by advancements in computer science and technology, machine learning has emerged as a powerful tool in chemical research. This paper begins by introducing the basic concepts of machine learning, followed by an exploration of its four key applications in the field of chemistry: predicting synthetic pathways in organic total synthesis; conducting efficient sampling and reconstruction of potential energy surfaces in atomic simulations; revealing reaction pathways and screening catalysts in heterogeneous catalysis design; and processing and interpreting signals in nuclear magnetic resonance spectroscopy. Additionally, the concept of the robotic chemist is introduced, illustrating the potential of integrating machine learning with automation technologies. Finally, the paper discusses the future prospects of machine learning in chemical research, highlighting its potential transformative impacts.

Key words: Machine learning, Deep learning, Robot chemist