论文标题
复杂能量景观的强大和记忆效率的过渡状态搜索方法
A robust and memory-efficient transition state search method for complex energy landscapes
论文作者
论文摘要
定位过渡状态对于研究从原子体到宏观系统的广泛现象中的过渡机制至关重要。但是，现有的方法可能会遇到许多自由度，自适应的自适应重新粘贴和粗粒的问题，以及本地平坦或不连续的能量景观。为了解决这些挑战，我们引入了一种新的双层方法，即二进制图像过渡状态搜索（BITSS）。它仅使用两种收敛到过渡状态的状态，从而产生了快速，灵活和记忆效率的方法。我们还表明，与仅使用两种状态的现有括号方法相比，它更强大。我们通过将位置应用于三个截然不同的问题：Lennard-Jones簇，壳屈曲和多相相期模型来证明其多功能性。
Locating transition states is crucial for investigating transition mechanisms in wide-ranging phenomena, from atomistic to macroscale systems. Existing methods, however, can struggle in problems with a large number of degrees of freedom, on-the-fly adaptive remeshing and coarse-graining, and energy landscapes that are locally flat or discontinuous. To resolve these challenges, we introduce a new double-ended method, the Binary-Image Transition State Search (BITSS). It uses just two states that converge to the transition state, resulting in a fast, flexible, and memory-efficient method. We also show it is more robust compared to existing bracketing methods that use only two states. We demonstrate its versatility by applying BITSS to three very different classes of problems: Lennard-Jones clusters, shell buckling, and multiphase phase-field models.
