Topology-Based Machine Learning Strategy for Cluster Structure Prediction
| Autor: | Guo-Wei Wei, Xin Chen, Feng Pan, Dong Chen, Yi Jiang, Mouyi Weng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Structure (category theory) Topology Machine learning computer.software_genre 01 natural sciences Article 03 medical and health sciences Physical information 0103 physical sciences Cluster (physics) General Materials Science Physical and Theoretical Chemistry 010306 general physics Topology (chemistry) 030304 developmental biology Pairwise independence 0303 health sciences Persistent homology business.industry Particle swarm optimization Construct (python library) ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business computer |
| Zdroj: | J Phys Chem Lett |
| ISSN: | 1948-7185 |
| DOI: | 10.1021/acs.jpclett.0c00974 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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