Identifying crystal defects is vital for unraveling the origins of many physical phenomena.Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for longmolecular d...Identifying crystal defects is vital for unraveling the origins of many physical phenomena.Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for longmolecular dynamics simulations.Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations.We compare the performance of three such algorithms(PCA,UMAP,and PaCMAP)on silicon and water systems.Initially,we evaluate the algorithms for recognizing phases,including crystal polymorphs and the melt,followed by an extension of our analysis to identify interstitials,vacancies,and interfaces.While PCA is found unsuitable for effective classification,it has been shown to be a suitable initialization for UMAP and PaCMAP.Both UMAP and PaCMAP show promising results overall,with PaCMAP proving more robust in classification,except in cases of significant class imbalance,where UMAP performs better.Notably,both algorithms successfully identify nuclei in supercooled water,demonstrating their applicability to ice nucleation in water.展开更多
基金support by the Doctoral College Advanced Functional Materials-Hierarchical Design of Hybrid Systems DOC 85 doc.funds and SFB-TACO 10.55776/F81 funded by the Austrian Science Fund(FWF)by the Vienna Doctoral School in Physics(VDSP).The computational results presentedhave beenpartly achievedusing the Vienna Scientific Cluster(VSC).
文摘Identifying crystal defects is vital for unraveling the origins of many physical phenomena.Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for longmolecular dynamics simulations.Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations.We compare the performance of three such algorithms(PCA,UMAP,and PaCMAP)on silicon and water systems.Initially,we evaluate the algorithms for recognizing phases,including crystal polymorphs and the melt,followed by an extension of our analysis to identify interstitials,vacancies,and interfaces.While PCA is found unsuitable for effective classification,it has been shown to be a suitable initialization for UMAP and PaCMAP.Both UMAP and PaCMAP show promising results overall,with PaCMAP proving more robust in classification,except in cases of significant class imbalance,where UMAP performs better.Notably,both algorithms successfully identify nuclei in supercooled water,demonstrating their applicability to ice nucleation in water.
