Atomistic Simulations, Italian Institute of Technology, Via Enrico Melen 83, Genoa GE 16153, Italy.
Department of Materials Science, Università di Milano-Bicocca, Milano 20126, Italy.
J Chem Phys. 2023 May 28;158(20). doi: 10.1063/5.0148872.
The study of the rare transitions that take place between long lived metastable states is a major challenge in molecular dynamics simulations. Many of the methods suggested to address this problem rely on the identification of the slow modes of the system, which are referred to as collective variables. Recently, machine learning methods have been used to learn the collective variables as functions of a large number of physical descriptors. Among many such methods, Deep Targeted Discriminant Analysis has proven to be useful. This collective variable is built from data harvested from short unbiased simulations in the metastable basins. Here, we enrich the set of data on which the Deep Targeted Discriminant Analysis collective variable is built by adding data from the transition path ensemble. These are collected from a number of reactive trajectories obtained using the On-the-fly Probability Enhanced Sampling flooding method. The collective variables thus trained lead to more accurate sampling and faster convergence. The performance of these new collective variables is tested on a number of representative examples.
研究长寿命亚稳态之间发生的罕见转变是分子动力学模拟中的一个主要挑战。许多旨在解决这个问题的方法都依赖于识别系统的慢模式，这些慢模式被称为集体变量。最近，机器学习方法已被用于学习作为大量物理描述符函数的集体变量。在众多此类方法中，深度靶向判别分析已被证明是有用的。这种集体变量是从亚稳盆地中短的无偏模拟中收集的数据构建而成的。在这里，我们通过添加来自过渡路径集合的数据来丰富构建深度靶向判别分析集体变量的数据集合。这些数据是使用实时概率增强采样淹没法从许多反应轨迹中收集的。经过训练的这些新集体变量可实现更准确的采样和更快的收敛。这些新集体变量的性能在一些代表性示例中进行了测试。
