论文标题
原子环境空间中的元动力学抽样，用于收集机器学习潜力的培训数据
Metadynamics sampling in atomic environment space for collecting training data for machine learning potentials
论文作者
论文摘要
机器学习潜力（MLP）的通用数学形式将原子间潜力发展的核心转移到收集适当的训练数据上。理想情况下，训练集应涵盖各种本地原子环境，但常规的方法很容易反复对类似的配置进行采样，这主要是由于Boltzmann统计数字。因此，从业者手动手动将大量不同的配置手动销售，从而大大扩展了开发期。在此，我们建议一种优化的新型抽样方法，用于半自动地收集多样化但相关的配置。这是通过将元动力学应用于局部原子环境的描述符作为集体变量来实现的。结果，模拟会自动转向未访问的本地环境空间，以使每个原子在没有冗余的情况下都会经历各种化学环境。我们将提出的元动力学采样应用于H：PT（111），Gete和Si系统。在整个示例中，少量的元动力轨迹可以提供训练高保真MLP所需的参考结构。通过提出针对MLP调整的半自动抽样方法，目前的工作为MLP在许多具有挑战性的应用程序中的更广泛应用铺平了道路。
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.
