Fig. 6: Utilization of t-distributed stochastic neighbor embedding (t-SNE) plots to understand the expected underlying mechanism.
From: An unsupervised machine learning based approach to identify efficient spin-orbit torque materials
Different colored regions correspond to the data clusters for the target words “topological insulator”, “topological semimetal”, “spin Hall effect”, “Rashba”, “ferromagnet”, and “antiferromagnet”. The black scatter points are the new SOT materials identified by the word embedding model, shown into four groups for visual clarity: a \(0.4\,<\, {\xi }_{{\rm{SOT}}}^{{\rm{NN}}}\le\)50, b \(0.2\,<\, {\xi }_{{\rm{SOT}}}^{{\rm{NN}}}\le\)0.4, c \(0.1\,<\, {\xi }_{{\rm{SOT}}}^{{\rm{NN}}}\le\)0.2, and d \({\xi }_{{\rm{SOT}}}^{{\rm{NN}}}\le\)0.1. The blue scatter points are a few known SOT materials for benchmarking purposes.
