阐明区块化学和数据科学的接口:通过ML引导的发现和数字分子制造器最大化功能
Nolan M Green1, Rachel I Hammond1, James Planey2
1Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States.
Journal of chemical education
|February 16, 2026
概括
这个实验室向学生介绍了用于分子创新的自动化块化学和人工智能. 学习者探索合成平台和人工智能工具,以设计和优化新型分子,用于有机激光和光伏等应用.
科学领域:
- 化学 化学 化学
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 自动合成和人工智能 (AI) 的融合正在彻底改变分子发现.
- 区块化学,利用代的碳-碳键形成,是这一领域的关键启用技术.
- 本科教育需要纳入这些先进的计算和合成方法.
研究的目的:
- 为学生介绍自动化块化学和人工智能的交叉点的原则和工具.
- 培养学生分析分子结构的技能,以实现代合成的兼容性.
- 为功能性材料提供人工智能驱动的分子设计和优化实践经验.
主要方法:
- 学生学习四个自动化合成平台,并分析分子结构的冗余,双功能构建块兼容的债券.
- 案例研究涉及使用区块化学与人工智能来优化有机激光和光伏候选者的性能.
- 一个新的在线平台,数字分子制造器 (DMM),用于多目标优化,然后是手动的区块化学实践.
主要成果:
- 学生熟悉自动合成平台和人工智能驱动的分子设计概念.
- 该课程有助于确定适合目标分子的构建块和合成平台.
- 实践活动加强了人工智能驱动的分子发现所必不可少的基础块化学原理.
结论:
- 这种本科实验室序列有效地弥合了自动化块化学和人工智能的功能分子发现.
- 学生在计算化学和与新兴研究领域相关的自动化合成方面培养实用技能.
- 该课程为学生准备在AI和化学合成的接口上进行高级研究.
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