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Abstract The investigation of exotic properties in two-dimensional (2D) topological superconductors has garnered increasing attention in condensed matter physics, particularly for applications in topological qubits. Despite this interest, a reliable way of fabricating topological Josephson junctions (JJs) utilizing topological superconductors has yet to be demonstrated. Controllable structural phase transition presents a unique approach to achieving topological JJs in atomically thin 2D topological superconductors. In this work, we report the pioneering demonstration of a structural phase transition from the superconducting to the semiconducting phase in the 2D topological superconductor 2M-WS₂. We reveal that the metastable 2M phase of WS₂remains stable in ambient conditions but transitions to the 2H phase when subjected to temperatures above 150 °C. We further locally induced the 2H phase within 2M-WS₂nanolayers using laser irradiation. Notably, the 2H phase region exhibits a hexagonal shape, and scanning tunneling microscopy uncovers an atomically sharp crystal structural transition between the 2H and 2M phase regions. Moreover, the 2M to 2H phase transition can be induced at the nanometer scale by a 200 kV electron beam. The electrical transport measurements further confirmed the superconductivity of the pristine 2M-WS₂and the semiconducting behavior of the laser-irradiated 2M-WS₂. Our results establish a novel approach for controllable topological phase change in 2D topological superconductors, significantly impacting the development of atomically scaled planar topological JJs. 

Abstract Effective control of magnetic phases in two-dimensional magnets would constitute crucial progress in spintronics, holding great potential for future computing technologies. Here, we report a new approach of leveraging tunneling current as a tool for controlling spin states in CrI₃. We reveal that a tunneling current can deterministically switch between spin-parallel and spin-antiparallel states in few-layer CrI₃, depending on the polarity and amplitude of the current. We propose a mechanism involving nonequilibrium spin accumulation in the graphene electrodes in contact with the CrI₃layers. We further demonstrate tunneling current-tunable stochastic switching between multiple spin states of the CrI₃tunnel devices, which goes beyond conventional bi-stable stochastic magnetic tunnel junctions and has not been documented in two-dimensional magnets. Our findings not only address the existing knowledge gap concerning the influence of tunneling currents in controlling the magnetism in two-dimensional magnets, but also unlock possibilities for energy-efficient probabilistic and neuromorphic computing. 

Abstract Materials in crystalline form possess translational symmetry (TS) when the unit cell is repeated in real space with long‐ and short‐range orders. The periodic potential in the crystal regulates the electron wave function and results in unique band structures, which further define the physical properties of the materials. Amorphous materials lack TS due to the randomization of distances and arrangements between atoms, causing the electron wave function to lack a well‐defined momentum. High entropy materials provide another way to break the TS by randomizing the potential strength at periodic atomic sites. The local elemental distribution has a great impact on physical properties in high entropy materials. It is critical to distinguish elements at the sub‐nanometer scale to uncover the correlations between the elemental distribution and the material properties. Here, the use of synchrotron X‐ray scanning tunneling microscopy (SX‐STM) with sub‐nm scale resolution in identifying elements on a high entropy alloy (HEA) surface is demonstrated. By examining the elementally sensitive X‐ray absorption spectra with an STM tip to enhance the spatial resolution, the elemental distribution on an HEA's surface at a sub‐nm scale is extracted. These results open a pathway towards quantitatively understanding high entropy materials and their material properties. 

Abstract In this study, we propose a scalable batch sampling scheme for optimization of simulation models with spatially varying noise. The proposed scheme has two primary advantages: (i) reduced simulation cost by recommending batches of samples at carefully selected spatial locations and (ii) improved scalability by actively considering replicating at previously observed sampling locations. Replication improves the scalability of the proposed sampling scheme as the computational cost of adaptive sampling schemes grow cubicly with the number of unique sampling locations. Our main consideration for the allocation of computational resources is the minimization of the uncertainty in the optimal design. We analytically derive the relationship between the “exploration versus replication decision” and the posterior variance of the spatial random process used to approximate the simulation model’s mean response. Leveraging this reformulation in a novel objective-driven adaptive sampling scheme, we show that we can identify batches of samples that minimize the prediction uncertainty only in the regions of the design space expected to contain the global optimum. Finally, the proposed sampling scheme adopts a modified preposterior analysis that uses a zeroth-order interpolation of the spatially varying simulation noise to identify sampling batches. Through the optimization of three numerical test functions and one engineering problem, we demonstrate (i) the efficacy and of the proposed sampling scheme to deal with a wide array of stochastic functions, (ii) the superior performance of the proposed method on all test functions compared to existing methods, (iii) the empirical validity of using a zeroth-order approximation for the allocation of sampling batches, and (iv) its applicability to molecular dynamics simulations by optimizing the performance of an organic photovoltaic cell as a function of its processing settings.