高维配置空间的有效探索,用于生成化学数据集
Xuewen Xiao1, Abdul-Rahman Allouche2, Emmanuel Dartois1
1Université Paris-Saclay, CNRS, Institut des Sciences Moléculaires d'Orsay, 91405 Orsay, France.
Journal of chemical information and modeling
|October 9, 2025
概括
我们开发了一种自动化方法来探索化学空间的反应保存原子,像机械化学. 这种方法有效地绘制复杂的分子景观,并为机器学习生成化学数据库.
科学领域:
- 计算化学的计算化学
- 化学物理 化学物理
- 天体化学是天体化学.
背景情况:
- 探索高维化学空间是计算密集的.
- 了解分子构造和反应途径对于化学发现至关重要.
- 机械化学反应,保存原子,提出了独特的探索挑战.
研究的目的:
- 为探索大型化学空间引入一种自动化,高效和具有成本效益的方法.
- 专注于保存原子数的过程,特别是机械化学反应.
- 为机器学习模型培训生成注释化学数据库.
主要方法:
- 结合了物理动机的随机全球景观探索 (机械化学扭曲) 来克服热带障碍.
- 采用局部勘探阶段,使用分子动力学和图形理论进行盆地采样.
- 使用 (vdW-) 过渡状态搜索与化学动力学模拟.
主要成果:
- 成功探索了C60H10-碳集群的结构格局,识别了过渡状态和最小能量路径.
- 从单个种子几何形状生成了一系列212个机械化学相关的适配体.
- 仅从3个符合者中获得了超过13,000个最小值,完全描述了化学网络.
结论:
- 开发的方法极大地加速了复杂化学空间的探索.
- 这种方法对于生成大型的注释化学数据库是有效的.
- 证明了在化学中训练数据饥渴的深度学习模型的潜力,特别是用于天体化学应用.
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