Topological spin textures in magnetic materials and arrangements of electric dipoles in
ferroelectrics are considered to be promising candidates for next-generation information …

Orbitronics is based on the use of orbital currents as information carriers. Orbital currents
can be generated from the conversion of charge or spin currents, and inversely, they could …

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the
growing energy costs of machine learning. In software-based machine learning, computing …

With the advent of Big Data, traditional digital computing is struggling to cope with intricate
tasks related to data classification or pattern recognition. To mitigate this limitation, software …

Memristor-based physical reservoir computing holds significant potential for efficiently
processing complex spatiotemporal data, which is crucial for advancing artificial intelligence …

Antiferromagnets (AFMs) are ideal materials to boost neuromorphic computing toward the
ultrahigh speed and ultracompact integration regime. However, developing a suitable AFM …

The effect of changing magnetic parameters on the size and density of skyrmions in a
hexagonal lattice is investigated using micromagnetic simulations. Achieving control of the …

Physical reservoir computing (RC) is a beyond von-Neumann computing paradigm that
harnesses the dynamical properties of a complex physical system (reservoir) to process …

Using atomistic simulations, we show that new types of skyrmion states called skyrmion
molecular crystals and skyrmion superlattices can be realized on triangular substrates when …

Skyrmions are increasingly favored in developing various spintronic devices as efficient
information carriers. The proposed voltage-controlled Dzyaloshinskii–Moriya interaction …