Chen Xin, Chen Dong, Weng Mouyi, Jiang Yi, Wei Guo-Wei, Pan Feng
School of Advanced Materials, Shenzhen Graduate School, Peking University, Shenzhen 518055, People's Republic of China.
Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States.
J Phys Chem Lett. 2020 Jun 4;11(11):4392-4401. doi: 10.1021/acs.jpclett.0c00974. Epub 2020 May 21.
In cluster physics, the determination of the ground-state structure of medium-sized and large-sized clusters is a challenge due to the number of local minimal values on the potential energy surface growing exponentially with cluster size. Although machine learning approaches have had much success in materials sciences, their applications in clusters are often hindered by the geometric complexity clusters. Persistent homology provides a new topological strategy to simplify geometric complexity while retaining important chemical and physical information without having to "downgrade" the original data. We further propose persistent pairwise independence (PPI) to enhance the predictive power of persistent homology. We construct topology-based machine learning models to reveal hidden structure-energy relationships in lithium (Li) clusters. We integrate the topology-based machine learning models, a particle swarm optimization algorithm, and density functional theory calculations to accelerate the search of the globally stable structure of clusters.
在团簇物理学中，由于势能面上局部极小值的数量随团簇尺寸呈指数增长，确定中型和大型团簇的基态结构是一项挑战。尽管机器学习方法在材料科学中取得了很大成功，但其在团簇中的应用常常受到团簇几何复杂性的阻碍。持久同调提供了一种新的拓扑策略，可简化几何复杂性，同时保留重要的化学和物理信息，而无需“降维”原始数据。我们进一步提出持久成对独立性（PPI）以增强持久同调的预测能力。我们构建基于拓扑的机器学习模型，以揭示锂（Li）团簇中隐藏的结构 - 能量关系。我们将基于拓扑的机器学习模型、粒子群优化算法和密度泛函理论计算相结合，以加速寻找团簇的全局稳定结构。
