Deep kernel methods learn better: from cards to process optimization

The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article

Effect of arc suppression on the physical properties of low temperature dc magnetron sputtered tantalum thin films

Arcing is a common phenomenon in the sputtering process. Arcs and glow discharges emit electrons which may influence the physical properties of films. This article reports the properties of tantalum (Ta) thin films prepared by continuous dc magnetron sputtering in normal and arc-suppression modes. The substrate temperature was varied in the range of 300–673 K. The tantalum films were ∼ 1.8 μm thick and have good adherence to 316 stainless steel and single-crystal silicon substrates. The phase of the Ta thin film determines the electrical and tribological properties. The films deposited at 300 K using both methods were crystallized in a tetragonal structure (β phase) with a smooth surface (grain size of ∼ 10 nm) and exhibited an electrical resistivity of ∼ 194 μΩ cm and a hardness of ∼ 20 GPa. When the substrate temperature was 473 K and higher, the arc-suppression mode appears to influence the films to crystallize in the α phase with a grain size of ∼ 40 nm, whereas the normal power mode gave mixed phases β and α beyond 473 K, the arc-suppression mode yields larger grain sizes in the Ta thin films and the hardness decreases. These changes in the physical properties in arc-suppression mode are attributed to either the change in plasma characteristics or the energetic particle bombardment onto the substrate, or both