Fig. 5: Model prediction of new SOT materials.
From: An unsupervised machine learning based approach to identify efficient spin-orbit torque materials
a Distribution of the neural network (NN) projected strength of the spin–orbit torque (SOT) efficiency factors, \({\xi }_{\,\text{SOT}}^{\text{NN}\,}\), for the predicted materials. b \({\xi }_{\,\text{SOT}}^{\text{NN}\,}\) as a function of their corresponding Γ for the 16 high SOT (\({\xi }_{\,\text{SOT}}^{\text{NN}\,}\ge 1\)) candidates. c Timeline analysis of the prediction on \({\xi }_{\,\text{SOT}}^{\text{NN}\,}\) for the 16 high SOT candidates as more data is added to the training dataset.
