学习尊重顺序对称性的集体变量
Jiaxin Yuan1, Shashank Sule1, Yeuk Yin Lam2
1Department of Mathematics, University of Maryland, College Park, Maryland 20742, USA.
The Journal of chemical physics
|September 22, 2025
概括
本研究引入了一个数值框架,用于学习 permutational 对称的系统的集体变量,准确估计粒子集群的过渡速率和停留时间. 该方法确保了对称性保护,用于增强粗粒度建模.
科学领域:
- 统计力学就是统计力学.
- 计算物理学的计算物理.
- 材料科学是一种材料科学.
背景情况:
- 粗粒度模型对于理解具有相同相互作用粒子的系统至关重要.
- 这些模型有助于识别元稳定状态,描述动态和估计转变速率.
- 随着转换和旋转对称性,变对称性是这样的系统的一个关键特征.
研究的目的:
- 开发一个数值框架来学习集体变量,本质上尊重转换,旋转和变换对称性.
- 准确估计具有相同相互作用粒子的系统中的过渡速率和停留时间.
- 为复杂粒子系统的粗粒度建模提供强大的方法.
主要方法:
- 一个新的框架,结合了基于排序的特色化和居住多重学习.
- 使用带有正交关系损失函数的自编码器来学习对称集体变量.
- 使用缩小模型的提交器作为前向流量采样和过渡路径采样控制的反应坐标.
主要成果:
- 拟议的框架成功地学习了保留系统对称性的集体变量.
- 使用缩小模型计算的过渡率和停留时间与粗暴强力方法有很好的一致性.
- 通过对Lennard-Jones-7 (2D) 和Lennard-Jones-8 (3D) 系统的案例研究证明了有效性.
结论:
- 开发的数值框架提供了一个准确而高效的方法,用于粗粒度模型的系统与换对称性.
- 这种方法增强了对粒子集群中超稳态,动态和过渡途径的理解.
- 该方法为复杂的相互作用粒子系统的计算技术提供了重大进步.
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