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# Topology-Based Machine Learning Strategy for Cluster Structure Prediction

journal contribution

posted on 2020-05-21, 16:26 authored by Xin Chen, Dong Chen, Mouyi Weng, Yi Jiang, Guo-Wei Wei, Feng PanIn
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.