用深度最小行动方法揭示活跃模型B的相位图和反应路径
Ruben Zakine1,2,3, Eric Simonnet4, Eric Vanden-Eijnden1
1<a href="https://ror.org/037tm7f56">Courant Institute</a>, <a href="https://ror.org/0190ak572">New York University</a>, 251 Mercer Street, New York, New York 10012, USA.
Physical review letters
|August 2, 2024
概括
本研究使用深度神经网络方法分析了活跃模型B中的非平衡阶段过渡. 它揭示了非传统的相分离路径和机制,这对于理解活性物质系统至关重要.
科学领域:
- 统计物理 统计物理
- 活体物质系统是什么
- 阶段过渡 阶段过渡 阶段过渡
背景情况:
- 由于复杂的动态和未知的稳定状态分布,不平衡相位过渡具有挑战性.
- 在这些系统中,时间逆向对称性往往被打破.
研究的目的:
- 要计算活跃模型B的相位图.
- 揭示非传统的反应路径和核化机制在第1,2,3维.
- 分析系统在均和不均阶段之间的切换.
主要方法:
- 深度神经网络实现几何最小动作方法 (gMAM).
- 对活跃模型B的相位图的计算.
主要成果:
- 逃离相位分离的平均时间在系统大小L时是很长的,但在1D时是不单调的.
- 逃离同质状态的平均时间是有限的.
- 活性项增强同质相稳定性,可能破坏有限系统中的相分离.
结论:
- 该研究提供了关于活性物质系统的动态的见解,特别是关于有限大小的影响.
- 结果与具有有限组成部分数量的系统 (约为10^7) 有关.
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