Ghimenti Federico, Berthier Ludovic, van Wijland Frédéric
<a href="https://ror.org/032w6q449">Laboratoire Matière et Systèmes Complexes (MSC)</a>, Université Paris Cité & CNRS (UMR 7057), 75013 Paris, France.
<a href="https://ror.org/02ftce284">Laboratoire Charles Coulomb (L2C)</a>, Université de Montpellier & CNRS (UMR 5221), 34095 Montpellier, France.
Phys Rev Lett. 2024 Jul 12;133(2):028202. doi: 10.1103/PhysRevLett.133.028202.
Equilibrium sampling of the configuration space in disordered systems requires algorithms that bypass the glassy slowing down of the physical dynamics. Irreversible Monte Carlo algorithms breaking detailed balance successfully accelerate sampling in some systems. We first implement an irreversible event-chain Monte Carlo algorithm in a model of continuously polydisperse hard disks. The effect of collective translational moves marginally affects the dynamics and results in a modest speedup that decreases with density. We then propose an irreversible algorithm performing collective particle swaps which outperforms all known Monte Carlo algorithms. We show that these collective swaps can also be used to prepare very dense jammed packings of disks.
对无序系统中的构型空间进行平衡采样需要能绕过物理动力学中玻璃态慢化现象的算法。打破细致平衡的不可逆蒙特卡罗算法在某些系统中成功加速了采样。我们首先在连续多分散硬磁盘模型中实现了一种不可逆事件链蒙特卡罗算法。集体平移移动的影响对动力学的影响微乎其微，导致适度加速，且加速效果随密度降低。然后我们提出了一种执行集体粒子交换的不可逆算法，该算法优于所有已知的蒙特卡罗算法。我们表明，这些集体交换还可用于制备非常致密的磁盘堵塞堆积。
