k-Means Clustering in Fingerprint-Based Configuration Selection for Fitting Interatomic Potentials.
Sign Up to like & getrecommendations! Published in 2024 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.4c01225
Abstract: In this study, we present a method for selecting an arbitrary number of distinct configurations from a larger data set by applying k-means clustering to atomistic configuration fingerprints based on the CrystalNN model and radial… read more here.
Keywords: configuration; means clustering; interatomic potentials; clustering fingerprint ... See more keywords
Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-01994-0
Abstract: In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnetic Resonance Imaging (MRI) using the physical properties of the nanoparticles and the MRI machine. The parameters considered include the size… read more here.
Keywords: machine learning; signal; machine; random selection ... See more keywords
Random selection of factors preserves the correlation structure in a linear factor model to a high degree
Sign Up to like & getrecommendations! Published in 2018 at "PLoS ONE"
DOI: 10.1371/journal.pone.0206551
Abstract: In a very high-dimensional vector space, two randomly-chosen vectors are almost orthogonal with high probability. Starting from this observation, we develop a statistical factor model, the random factor model, in which factors are chosen stochastically… read more here.
Keywords: factor model; random selection; correlation; linear factor ... See more keywords