Topology-Based Machine Learning Strategy for Cluster Structure Prediction.

ABSTRACT: 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-

SUBMITTER: Chen X 

PROVIDER: S-EPMC7351018 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature