通过人工智能增强计算催化和化学反应
Konstantinos D Vogiatzis1, Clémence Corminboeuf2, Ainara Nova3,4
1Department of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States.
Journal of the American Chemical Society
|February 20, 2026
概括
人工智能 (AI) 和机器学习 (ML) 正在为更快的催化剂发现彻底改变计算化学. 将人工智能与人类专业知识相结合,将加速化学洞察力和催化剂设计.
科学领域:
- 计算化学计算化学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在计算化学方面越来越有影响力.
- 这些技术提供了新的方法来加速催化剂的发现,并提高对化学反应的理解.
研究的目的:
- 突出新兴的人工智能/ML方法转变计算催化剂.
- 讨论将人工智能应用于催化剂设计的挑战和机遇.
- 强调人类直觉和人工智能驱动的方法之间的协同作用.
主要方法:
- 机器学习的潜力 机器学习的潜力
- 强化学习是一种强化学习.
- 生成性AI是一种人工智能.
- 大型语言模型.
- 对于反应性结果的数据集构造.
主要成果:
- 人工智能/ML方法已经准备好改变计算催化剂.
- 挑战包括开发过渡金属复合体的分子表征,并与人工智能建立机制理解的桥梁.
- 捕捉成功和失败反应的可靠数据集至关重要.
结论:
- 计算催化剂的未来涉及到平衡人类直觉与算法力量.
- 人工智能应该被视为加速器,而不是替代化学洞察力和催化剂设计.
- 将人工智能与实验和计算专业知识相结合是关键.
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