BEGIN:VCALENDAR
METHOD:PUBLISH
PRODID:www-matsim-tf-fau-de//Events//EN
VERSION:2.0
BEGIN:VEVENT
SUMMARY:Uncovering Hidden Structures in Materials Data: A Study of Two
  Clustering Algorithms with Dimensionality Reduction - Yan Mei
UID:794cfbe3-1a79-4aa5-a256-c23f6e19d30a
DESCRIPTION:Yan Mei WW8\, FAU 6. Mai 2025\, 17:00 WW8\, Fürth This st
 udy applies two clustering methods to high-dimensional materials datas
 ets from the NOMAD and Matminer. Principal component analysis (PCA) an
 d t-SNE are used for dimensionality reduction and visualization\, enab
 ling direct comparisons of cluster assignments. Outlier detection and 
 Jaccard index for clusters(including outlier overlap) are employed to 
 evaluate differences in how the algorithms group and label data. In ad
 dition\, space group\, atomic density\, and bulk modulus descriptors a
 re introduced to examine possible connections between material propert
 ies and cluster structures. The results indicate that while both algor
 ithms can reveal structural patterns\, they define clusters in differe
 nt ways\, suggesting the importance of algorithm choice and feature se
 lection in materials data analysis.
DTSTART:20250506T150000Z
DTEND:20250506T160000Z
LOCATION:WW8\, Room 2.018-2\, Dr.-Mack-Str. 77\, Fürth
DTSTAMP:20260427T173623Z
END:VEVENT
END:VCALENDAR