原子环境空间中的元数据采样，用于收集机器学习潜力的训练数据
Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials
摘要：
机器学习势（MLP）的通用数学形式将原子间势发展的核心转移到收集适当的训练数据上。理想情况下，训练集应该包含不同的局部原子环境，但传统方法倾向于重复采样类似的配置，这主要是由于玻尔兹曼统计。因此，实践者手动挑选大量不同的配置，大大延长了开发周期。在这里，我们提出了一种新的采样方法，优化收集不同的，但相关的配置半自动。这是通过将元数据与局部原子环境的描述符一起作为集合变量来应用来实现的。因此，模拟自动转向未访问的局部环境空间，使得每个原子经历不同的化学环境而没有冗余。我们将所提出的亚自洽采样应用于H：Pt（111）、GeTe和Si系统。在整个示例中，少量的元自适应轨迹可以提供训练高保真MLP所需的参考结构。通过提出一种针对MLP的半自动采样方法，本工作为MLP在许多具有挑战性的应用中的更广泛应用铺平了道路。
英文摘要：
The universal mathematical form of machine-learning potentials (MLPs) shifts the core of development of interatomic potentials to collecting proper training data. Ideally, the training set should encompass diverse local atomic environments but the conventional approach is prone to sampling similar configurations repeatedly, mainly due to the Boltzmann statistics. As such, practitioners handpick a large pool of distinct configurations manually, stretching the development period significantly. Herein, we suggest a novel sampling method optimized for gathering diverse yet relevant configurations semi-automatically. This is achieved by applying the metadynamics with the descriptor for the local atomic environment as a collective variable. As a result, the simulation is automatically steered toward unvisited local environment space such that each atom experiences diverse chemical environments without redundancy. We apply the proposed metadynamics sampling to H:Pt(111), GeTe, and Si systems. Throughout the examples, a small number of metadynamics trajectories can provide reference structures necessary for training high-fidelity MLPs. By proposing a semi-automatic sampling method tuned for MLPs, the present work paves the way to wider applications of MLPs to many challenging applications.
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