同质的水蒸气凝结与深度神经网络潜力模型
Shenghui Zhong1, Zheyu Shi1,2, Bin Zhang1
1International Innovation Institute, Beihang University, Hangzhou 311115, China.
The Journal of chemical physics
|March 22, 2024
概括
分子模拟揭示了对水凝结核形成的新见解. 深度神经网络潜力模型准确地预测核化速率,提供比传统理论更可靠的理解.
科学领域:
- 物理化学 物理化学
- 计算物理 计算物理
- 材料科学 材料科学 材料科学
背景情况:
- 由于复杂的相互作用和罕见的事件发生,分子级核形成的理解很差.
- 准确的核化建模对于理解各种系统中的相位过渡至关重要.
研究的目的:
- 在分子水平上研究同质的水蒸气凝结.
- 通过深度神经网络 (DNN) 潜力预测的核化动态与既有模型和理论进行比较.
主要方法:
- 使用了分子动力学 (MD) 模拟.
- 纳入了基于第一原则的深度神经网络 (DNN) 潜在模型.
- 将DNN模型的预测与SPC/E,TIP4P,OPC模型和经典核化理论 (CNT) 进行了比较.
主要成果:
- DNN模型的核化速率与OPC模型保持一致,超过SPC/E和TIP4P.
- 在SPC/E和TIP4P模型中,因过高估计的散装免费能源而导致的差异.
- MD模拟显示了关于表面和散装自由能量以及集群属性的CNT偏差.
结论:
- DNN潜能模型提供了更准确的水核化动态的表示.
- 经典核化理论的假设不能完全捕捉到分子层次的凝结现象.
- 胚胎集群形成涉及分子几何学和密度概况的重大变化.
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