神经网络的原子潜力用于全球能量最小值在碳集群中搜索
Nikolay V Tkachenko1, Anastasiia A Tkachenko2, Benjamin Nebgen3
1Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322-0300, USA. nikolay.tkachenko95@gmail.com.
Physical chemistry chemical physics : PCCP
|July 25, 2023
概括
神经网络潜能,如ANI-1ccx和ANI-nr,可以有效地找到碳集群的全球最小结构. 这些潜能作为强大的预取样器,有助于优化化学和物理性质评估的能源.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 化学物理 化学物理
背景情况:
- 全球能源优化对于确定化合物特性至关重要,特别是在原子集群中.
- 能源景观的复杂性随着集群规模的增加而呈指数级增加,需要有效的搜索方法.
- 神经网络 (NN) 潜能为分子结构优化提供了一种计算效率高的方法.
研究的目的:
- 评估ANI-1ccx和ANI-nr NN潜力的适用性,以优化碳集群中的全球最小值.
- 确定在特定数据集上训练的NN潜力是否可以准确地代表相关领域的潜在能量表面 (PES).
- 评估NN潜力的稳定性,以导航碳集群复杂的能源景观.
主要方法:
- 在碳集群 (Cn,n=3-10) 上测试ANI-1ccx和ANI-nr NN潜在的全球最小值搜索.
- 整合集群连接限制以指导优化过程.
- 使用密度函数理论 (DFT) 或与NN潜力结合的ab initio计算.
主要成果:
- ANI-1ccx和ANI-nr潜力在识别小型碳集群的全球最小结构方面表现出有效性.
- 当与连接约束和进一步计算相结合时,NN潜力被证明能够捕捉像C20这样的大集群的全球最小值.
- 这些NN潜能作为潜在能量表面的可靠预取样器.
结论:
- ANI-1ccx和ANI-nr NN潜力是碳集群全球最小值优化中潜在能量表面预采样的强大工具.
- 整合连接限制提高了复杂系统NN潜力的可靠性.
- NN潜力显示出在加速发现稳定的原子集群结构方面具有显著的前景.
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