加快过渡状态搜索和干选用于有机金属催化与反应机器学习潜力的反应性机器学习潜力
Kun Tang1, Yujing Zhao1,2, Lei Zhang1,3
1State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
Journal of chemical theory and computation
|September 10, 2025
概括
一个新的反应性机器学习潜力 (RMLP) 模型加速了有机金属催化剂的设计. 这种计算工具快速选连接体并优化过渡状态,大大加快了催化剂的开发.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 化学工程是化学工程的组成部分.
背景情况:
- 有机金属催化对于工业化工生产至关重要.
- 设计高效的催化剂需要准确的过渡状态识别和连接体选.
- 目前用于这些任务的量子化学方法在计算上昂贵.
研究的目的:
- 开发一个反应性机器学习潜力 (RMLP) 模型来加速有机金属催化剂设计.
- 为了实现快速的过渡状态优化和连接体选.
- 为传统量子化学方法提供一个计算效率高的替代方案.
主要方法:
- 开发了一个反应式机器学习潜力 (RMLP) 模型.
- 使用自动化过渡状态数据库构建方法.
- 采用了传递神经网络的更高阶等价值信息.
- 验证了对乙烯化反应的模型.
主要成果:
- RMLP的速度比严格的量子化学计算快了近3个数量级.
- 该模型保持了高精度,在过渡状态几何学上,根-平方平均偏差为0.307 Å.
- 对反应障碍物实现了0.871 kJ·mol-1的平均绝对误差,超过了半实证方法.
结论:
- RMLP模型提供了一种快速而准确的方法,用于在有机金属催化中优化过渡状态.
- 这有助于对新型有机金属催化剂配体进行高通量选.
- 为加速发现工业应用的先进催化剂铺平了道路.
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