Our research dives into the performance comparison of two popular machine learning approaches: the support vector machine (SVM) and the more recent deep learning-based stacked autoencoder (SAE). We ...

Additionally, utilizing stacked autoencoder architecture with layer-wise pre-training and hierarchical structures, GMScaleSAE effectively fuses multiscale features, ensuring stable training and ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...