Estimating the effective fields of spin configurations using a deep learning technique

The properties of complicated magnetic domain structures induced by various spin–spin interactions in magnetic systems have been extensively investigated in recent years. To understand the statistical and dynamic properties of complex magnetic structures, it is crucial to obtain information on the effective field distribution over the structure, which is not directly provided by magnetization. In this study, we use a deep learning technique to estimate the effective fields of spin configurations. We construct a deep neural network and train it with spin configuration datasets generated by Monte Carlo simulation. We show that the trained network can successfully estimate the magnetic effective field even though we do not offer explicit Hamiltonian parameter values. The estimated effective field information is highly applicable; it is utilized to reduce noise, correct defects in the magnetization data, generate spin configurations, estimate external field responses, and interpret experimental images.

Non-collinear ground state and stable bimerons from four-spin chiral interactions in D3h magnet

We demonstrate that four-spin interactions in crystals with D3h point group of symmetry can cause a phase transition from a collinear state to a non-collinear magnetic ground state (such as magnetic vortices or magnetic skyrmions), while all anti-symmetric chiral terms are forbidden by symmetry in such crystals. Moreover, D3h point group rather common among two dimensional magnets. Taking into account possible four-spin chiral exchange interactions is important for understanding noncollinear magnetic order in these systems. We also address a possible stabilization of bimerons by the same contribution.

Atomic-precision advanced manufacturing for Si quantum computing

A materials synthesis method that we call atomic-precision advanced manufacturing (APAM), which is the only known route to tailor silicon nanoelectronics with full 3D atomic precision, is making an impact as a powerful prototyping tool for quantum computing. Quantum computing schemes using atomic (31P) spin qubits are compelling for future scale-up owing to long dephasing times, one- and two-qubit gates nearing high-fidelity thresholds for fault-tolerant quantum error correction, and emerging routes to manufacturing via proven Si foundry techniques. Multiqubit devices are challenging to fabricate by conventional means owing to tight interqubit pitches forced by short-range spin interactions, and APAM offers the required (Å-scale) precision to systematically investigate solutions. However, applying APAM to fabricate circuitry with increasing numbers of qubits will require significant technique development. Here, we provide a tutorial on APAM techniques and materials and highlight its impacts in quantum computing research. Finally, we describe challenges on the path to multiqubit architectures and opportunities for APAM technique development. Graphic Abstract

Atomically resolved single-molecule triplet quenching

The nonequilibrium triplet state of molecules plays an important role in photocatalysis, organic photovoltaics, and photodynamic therapy. We report the direct measurement of the triplet lifetime of an individual pentacene molecule on an insulating surface with atomic resolution by introducing an electronic pump-probe method in atomic force microscopy. Strong quenching of the triplet lifetime is observed if oxygen molecules are coadsorbed in close proximity. By means of single-molecule manipulation techniques, different arrangements with oxygen molecules were created and characterized with atomic precision, allowing for the direct correlation of molecular arrangements with the lifetime of the quenched triplet. Such electrical addressing of long-lived triplets of single molecules, combined with atomic-scale manipulation, offers previously unexplored routes to control and study local spin-spin interactions.