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快速而准确的环应变能量预测,使用机器学习和应用于应变促进反应.
Zhen Liu1, Jessica Vinskus1, Yue Fu2
1Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.
JACS Au
|October 31, 2025
概括
我们开发了一种快速机器学习 (ML) 方法来预测环张力能量 (RSE),这对于化学反应至关重要. 这种方法绕过了缓慢的量子力学计算,使得RSE易于用于药物发现和材料科学.
科学领域:
- 计算化学的计算化学
- 机器学习在化学中的应用
- 预测化学反应性的预测.
背景情况:
- 环应变能量 (RSE) 对于预测各种化学学科的分子反应性至关重要.
- 测定RSE的实验和量子力学 (QM) 方法在计算上昂贵且耗时.
- 由于RSE的可访问性有限,因此阻碍了其在反应设计和发现中的广泛应用.
研究的目的:
- 开发一个快速而准确的机器学习 (ML) 工作流来预测RSE,克服传统方法的局限性.
- 为了实现对各种化学系统的RSE的高效和大规模计算.
- 为研究RSE对化学过程的影响的研究人员创造一个有价值的资源.
主要方法:
- 利用AIMNet2机器学习原子间潜力和Auto3D进行符合性识别和RSE计算.
- 开发了一种基于ML的工作流程,可以绕过用于RSE预测的传统QM计算.
- 在准确性和速度方面将ML工作流与高级QM方法 (ωB97M-D4/Def2-TZVPP) 进行了对比.
主要成果:
- 实现了高预测准确度,R平方为0.997和平均绝对误差 (MAE) 为0.896kcal/mol.
- 显示了显著的速度改进,ML工作流比DFT计算快了数量级.
- 成功地应用了工作流来区分在点击化学和聚合反应中的反应性和非反应性分子.
- 编制了RSE Atlas,一个包含16,905个单环分子的数据库,作为公共资源.
结论:
- 机器学习工作流提供了一个可靠和高效的RSE计算替代方案,将其转化为可访问的属性.
- 开发的方法有助于将RSE考虑纳入反应设计,药物发现和材料科学.
- 该RSE Atlas作为一个全面的资源,用于探索与环应变相关的结构-属性关系.
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