There has been growing excitement over the possibility of employing artificial neural networks (ANNs) to gain new theoretical insight into the physics of quantum many-body problems. "Interpretability"remains a concern: can we understand the basis for the ANN's decision-making criteria in order to inform our theoretical understanding? "Interpretable"machine learning in quantum matter has to date been restricted to linear models, such as support vector machines, due to the greater difficulty of interpreting nonlinear ANNs.

In many unconventional superconductors, nematic quantum fluctuations are strongest where the critical temperature is highest, inviting the conjecture that nematicity plays an important role in the pairing mechanism. Recently, Ba1-xSrxNi2As2 has been identified as a tunable nematic system that provides an ideal testing ground for this proposition. We therefore propose several sharp empirical tests, supported by quantitative calculations in a simple model of Ba1-xSrxNi2As2.

We investigated the photoluminescence (PL) characteristics of MoS2–Au hybrid nanostructures, fabricated by nanosphere lithography and wet-transfer techniques. Two kinds of Au nanostructures - such as nanotriangles (NTs) and nanoholes (NHs) - were fabricated for comparison. MoS2 monolayers on both NT and NH arrays exhibited enhanced PL intensity, compared with those on SiO2/Si substrates and flat Au thin films. Numerical simulations revealed clear distinction in the electric field intensity distributions in the NT and NH arrays at the PL excitation wavelength.

In conventional 3D heterostructures, a gradual potential gradient in constituent layers and an abrupt potential discontinuity at heterointerfaces can appear. Studies of the electrostatic potential in 2D heterostructures require careful characterizations and analyses because the 2D materials have distinct physical characteristics compared with their 3D counterparts. Herein, three kinds of samples are prepared using sulfurization of metal layers on single-crystalline sapphire substrates: WS2, MoS2, and WS2/MoS2.

Metal and transition-metal dichalcogenide (TMD) hybrid systems have been attracting growing research attention because exciton-plasmon coupling is a desirable means of tuning the physical properties of TMD materials. Competing effects of metal nanostructures, such as the local electromagnetic field enhancement and luminescence quenching, affect the photoluminescence (PL) characteristics of metal/TMD nanostructures.

Recent experimental observations of apparently hydrodynamic electronic transport have generated much excitement. However, the understanding of the observed nonlocal transport (whirlpool) effects and parabolic (Poiseuille-like) current profiles has largely been motivated by a phenomenological analogy to classical fluids. This is due to difficulty in incorporating strong correlations in quantum mechanical calculation of transport, which has been the primary angle for interpreting the apparently hydrodynamic transport.

Recent developments in twisted and lattice-mismatched bilayers have revealed a rich phase space of van der Waals systems and generated excitement. Among these systems are heterobilayers, which can offer new opportunities to control van der Waals systems with strong in plane correlations such as spin-orbit-assisted Mott insulator α-RuCl3. Nevertheless, a theoretical ab initio framework for mismatched heterobilayers without even approximate periodicity is sorely lacking.

We developed perovskite solar cells (PSCs) with a ZnO electron-Transporting layer (ETL) of which the surface was passivated with methoxybenzoic acid self-Assembled monolayers (SAMs). The self-Assembled monolayer (SAM) simultaneously improved the photovoltaic performance and device stability.

Designing an efficient and stable hole transport layer (HTL) material is one of the essential ways to improve the performance of organic-inorganic perovskite solar cells (PSCs). Herein, for the first time, an efficient model of a hole transport material (HTM) is demonstrated by optimized doping of a conjugated polymer TFB (poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-(4,4′-(N-(4-sec-butylphenyl)diphenylamine)]) with a non-hygroscopic p-type dopant F4-TCNQ (2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane) for high-efficiency PSCs.

While the nonperturbative interaction effects in the fractional quantum Hall regime can be readily simulated through exact diagonalization, it has been challenging to establish a suitable diagnostic that can label different phases in the presence of competing interactions and disorder. Here we introduce a multifaceted framework using a simple artificial neural network (ANN) to detect defining features of a fractional quantum Hall state, a charge-density-wave state and a localized state using the entanglement spectra and charge density as independent input.