Contour forward flux sampling: Sampling rare events along multiple collective variables

Making rare events typical in Markovian open quantum systems

Large dynamical fluctuations - atypical realizations of the dynamics sustained over long periods of time - can play a fundamental role in determining the properties of collective behavior of both classical and quantum non-equilibrium systems. Rare dynamical fluctuations, however, occur with a probability that often decays exponentially in their time extent, thus making them difficult to be directly observed and exploited in experiments. Here, using methods from dynamical large deviations, we explain how rare dynamics of a given (Markovian) open quantum system can always be obtained from the ty…

The role of rare events in the pinning problem

Type II superconductors exhibit a fascinating phenomenology that is determined by the dynamical properties of the vortex matter hosted by the material. A crucial element in this phenomenology is vortex pinning by material defects, e.g., immobilizing vortices at small drives and thereby guaranteeing dissipation-free current flow. Pinning models for vortices and other topological defects, such as domain walls in magnets or dislocations in crystals, come in two standard variants: i) weak collective pinning, where individual weak defects are unable to pin, while the random accumulation of many for…

Learning nonequilibrium control forces to characterize dynamical phase transitions

Sampling the collective, dynamical fluctuations that lead to nonequilibrium pattern formation requires probing rare regions of trajectory space. Recent approaches to this problem based on importance sampling, cloning, and spectral approximations, have yielded significant insight into nonequilibrium systems, but tend to scale poorly with the size of the system, especially near dynamical phase transitions. Here we propose a machine learning algorithm that samples rare trajectories and estimates the associated large deviation functions using a many-body control force by leveraging the flexible fu…

从转变路径系综获取深度学习集体变量

Deep Learning Collective Variables from Transition Path Ensemble

本文提出将转变路径系综数据加入 Deep Targeted Discriminant Analysis 集体变量训练，以提升分子动力学稀有转变采样的准确性与收敛速度。

基于无监督归一化流的稀有事件高效采样

Efficient Rare Event Sampling with Unsupervised Normalising Flows

本文提出 FlowRES 框架，利用无监督归一化流生成高质量非局域蒙特卡洛提议以高效采样稀有事件，无需先验数据或集体变量，且能处理多路径系统。

everywhere everywhere all at once: 一种基于概率的增强采样方法用于稀有事件

Everything everywhere all at once: a probability-based enhanced sampling approach to rare events

本文提出了一种基于概率的增强采样方法，用于高效研究稀有事件的自由能面和过渡态。

Enhancing entropy and enthalpy fluctuations to drive crystallization in atomistic simulations

Crystallization is a process of great practical relevance in which rare but crucial fluctuations lead to the formation of a solid phase starting from the liquid. Like in all first order first transitions there is an interplay between enthalpy and entropy. Based on this idea, to drive crystallization in molecular simulations, we introduce two collective variables, one enthalpic and the other entropic. Defined in this way, these collective variables do not prejudge the structure the system is going to crystallize into. We show the usefulness of this approach by studying the case of sodium and al…

Simulating rare events using a Weighted Ensemble-based string method

We introduce an extension to the Weighted Ensemble (WE) path sampling method to restrict sampling to a one dimensional path through a high dimensional phase space. Our method, which is based on the finite-temperature string method, permits efficient sampling of both equilibrium and non-equilibrium systems. Sampling obtained from the WE method guides the adaptive refinement of a Voronoi tessellation of order parameter space, whose generating points, upon convergence, coincide with the principle reaction pathway. We demonstrate the application of this method to several simple, two-dimensional mo…

可微自由能面：一种变分方法，用于通过生成深度学习模型直接观察稀有事件

Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models

本文提出变分自由能面（VaFES），通过生成深度学习模型直接建模自由能面，无需数据集，实现稀有事件的快速识别和生成，适用于复杂多体系统的研究。