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${\mathrm{FeTe}}_{0.55}{\mathrm{Se}}_{0.45}$(FTS) occupies a special spot in modern condensed matter physics at the intersections of electron correlation, topology, and unconventional superconductivity. The bulk electronic structure of FTS is predicted to be topologically nontrivial due to the band inversion between the $d_{xz}$and $p_z$bands along $\mathrm{\Gamma }\text{−}Z$. However, there remain debates in both the authenticity of the Dirac surface states (DSSs) and the experimental deviations of band structure from the theoretical band inversion picture. Here we resolve these debates through a comprehensive angle-resolved photoemission spectroscopy investigation. We first observe a persistent DSS independent of $k_z$. Then, by comparing FTS with FeSe, which has no band inversion along $\mathrm{\Gamma }\text{−}Z$, we identify the spectral weight fingerprint of both the presence of the $p_z$band and the inversion between the $d_{xz}$and $p_z$bands. Furthermore, we propose a renormalization scheme for the band structure under the framework of a tight-binding model preserving crystal symmetry. Our results highlight the significant influence of correlation on modifying the band structure and make a strong case for the existence of topological band structure in this unconventional superconductor. Published by the American Physical Society2024 

Abstract Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics‐based models. Given the importance of deepening our understanding and improving predictions of the Earth system on all time scales, efforts are now underway to develop Earth‐system models (ESMs) capable of representing all components of the coupled Earth system (or their aggregated behavior) and their response to external changes over long timescales. Building trust in ESMs is a much more difficult problem than for weather forecast models, not least because the model must represent the alternate (e.g., future or paleoclimatic) coupled states of the system for which there are no direct observations. Given that the physical principles that enable predictions about the response of the Earth system are often not explicitly coded in these ML‐based models, demonstrating the credibility of ML‐based ESMs thus requires us to build evidence of their consistency with the physical system. To this end, this paper puts forward five recommendations to enhance comprehensive, standardized, and independent evaluation of ML‐based ESMs to strengthen their credibility and promote their wider use. 

Abstract We study a Morita-equivalent version of the Zariski cancellation problem. 

Light emission from biased tunnel junctions has recently gained much attention owing to its unique potential to create ultracompact optical sources with terahertz modulation bandwidth1,2,3,4,5. The emission originates from an inelastic electron tunnelling process in which electronic energy is transferred to surface plasmon polaritons and subsequently converted to radiation photons by an optical antenna. Because most of the electrons tunnel elastically, the emission efficiency is typically about 10−5–10−4. Here, we demonstrate efficient light generation from enhanced inelastic tunnelling using nanocrystals assembled into metal–insulator–metal junctions. The colour of the emitted light is determined by the optical antenna and thus can be tuned by the geometry of the junction structures. The efficiency of far-field free-space light generation reaches ~2%, showing an improvement of two orders of magnitude over previous work3,4. This brings on-chip ultrafast and ultracompact light sources one step closer to reality.