Technological advancements have substantially increased computational power and data
availability, enabling the application of powerful machine-learning (ML) techniques across …

Unsupervised and semi-supervised ML methods such as variational autoencoders (VAE)
have become widely adopted across multiple areas of physics, chemistry, and materials …

Topological analysis is widely adopted in various research fields to unveil intricate features
and structural relationships implied in geometrical objects. Especially, in the fields of data …

We construct a deep neural network to enhance the resolution of spin structure images
formed by spontaneous symmetry breaking in the magnetic systems. Through the deep …

A ventilated acoustic resonator (VAR), a type of acoustic metamaterial (AM) has emerged as
a promising solution for mitigating urban noise pollution and traffic noise which …

Recently, deep generative models using machine intelligence are widely utilized to
investigate scientific systems by generating scientific data. In this study, we experiment with …

Gradient-free optimizers allow for tackling problems regardless of the smoothness or
differentiability of their objective function, but they require many more iterations to converge …

Si-based materials have gained attention as negative electrode materials for lithium-ion
batteries. However, it is still difficult to model and predict their degradation phenomena using …

We classify the magnetic ground states of a 2D lattice of localized magnetic moments, which
are coupled to a superconducting substrate with Rashba spin-orbit coupling. We discover a …

Gradient-free optimizers allow for tackling problems regardless of the smoothness or
differentiability of their objective function, but they require many more iterations to converge …