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In this paper, we focus on exploiting the group structure for large-dimensional factor models, which captures the homogeneous effects of common factors on individuals within the same group. In view of the fact that datasets in macroeconomics and finance are typically heavy-tailed, we propose to identify the unknown group structure using the agglomerative hierarchical clustering algorithm and an information criterion with the robust two-step (RTS) estimates as initial values. The loadings and factors are then re-estimated conditional on the identified groups. Theoretically, we demonstrate the consistency of the estimators for both group membership and the number of groups determined by the information criterion. Under finite second moment condition, we provide the convergence rate for the newly estimated factor loadings with group information, which are shown to achieve efficiency gains compared to those obtained without group structure information. Numerical simulations and real data analysis demonstrate the nice finite sample performance of our proposed approach in the presence of both group structure and heavy-tailedness.

Cache serves as a temporary data memory module in many general-purpose processors and domain-specific accelerators. Its density, power, speed, and reliability play a critical role in enhancing the overall system performance and quality of service. Conventional volatile memories, including static random-access memory (SRAM) and embedded dynamic random-access memory (eDRAM) in the complementary metal-oxide-semiconductor technology, have high performance and good reliability. However, the inherent leakage in both SRAM and eDRAM hinders further improvement towards smaller feature sizes and higher energy efficiency. Although the emerging nonvolatile memories can eliminate the leakage efficiently, the penalties of lower speed and degraded reliability are significant. This article reveals a new opportunity towards leakage-free volatile static memory beyond the known paradigms of existing volatile and nonvolatile memories. By engineering a double-well energy landscape with the assistance of a clamping voltage bias, leakage-free and refresh-free state retention of volatile memory is achieved for the first time. This new memory is highlighted by both the ultra-low leakage of nonvolatile memories and the speed, energy, and reliability advantages of volatile memories. A proof-of-concept memory is demonstrated using in-house anti-ferroelectric field-effect transistors (AFeFETs), delivering an extrapolated endurance of about 1012 cycles, a retention time of over 10 years, and no subthreshold channel leakage current. Such a new concept of AFeFET-based memory enables an improved balance between density, power, and reliability beyond all existing memory solutions.

Hardware security has been a key concern in modern information technologies. Especially, as the number of Internet-of-Things (IoT) devices grows rapidly, to protect the device security with low-cost security primitives becomes essential, among which Physical Unclonable Function (PUF) is a widely-used solution. In this paper, we propose the first FeFET-based strong PUF exploiting the cycle-to-cycle (C2C) variation of FeFETs as the entropy source. Based on the experimental measurements, the proposed PUF shows satisfying performance including high uniformity, uniqueness, reconfigurability and reliability. To resist machine-learning attack, XOR structure was introduced, and simulations show that our proposed PUF has similar resistance to existing attack models with traditional arbiter PUFs. Furthermore, our design is shown to be power-efficient, and highly robust to write voltage, temperature and device size, which makes it a competitive security solution for Internet-of-Things edge devices.

Inter-site interactions between single atom catalysts (SACs) in the high loading regime are critical to tuning the catalytic performance. However, the understanding on such interactions and their distance dependent effects remains elusive, especially for the heteronuclear SACs. In this study, we reveal the effects of the distance-dependent inter-site interaction on the catalytic performance of SACs. Using the density functional theory calculations, we systematically investigate the heteronuclear iron and cobalt single atoms co-supported on the nitrogen-doped graphene (FeN4-C and CoN4-C) for oxygen reduction reaction (ORR). We find that as the distance between Fe and Co SACs decreases, FeN4-C exhibits a reduced catalytic activity, which can be mitigated by the presence of an axial hydroxyl ligand, whereas the activity of CoN4-C shows a volcano-like evolution with the optimum reached at the intermediate distance. We further unravel that the transition towards the high-spin state upon adsorption of ORR intermediate adsorbates is responsible for the decreased activity of both FeN4-C and CoN4-C at short inter-site distance. Such high-spin state transition is also found to significantly shift the linear relation between hydroxyl (*OH) and hydroperoxyl (*OOH) adsorbates. These findings not only shed light on the SAC-specific effect of the distance-dependent inter-site interaction between heteronuclear SACs, but also pave a way towards shifting the long-standing linear relations observed in multiple-electron chemical reactions.

Phase stability and properties of two-dimensional phosphorus carbide, P$_x$C$_{1-x}$, are investigated using the first-principles method in combination with cluster expansion and Monte Carlo simulation. Monolayer P$_x$C$_{1-x}$ is found to be a phase separating system which indicates difficulty in fabricating monolayer P$_x$C$_{1-x}$ or crystalline P$_x$C$_{1-x}$ thin films. Nevertheless, a bottom-up design approach is used to determine the stable structures of P$_x$C$_{1-x}$ of various compositions which turn out to be superlattices consisting of alternating carbon and phosphorus nanoribbons along the armchair direction. Results of first-principles calculations indicate that once these structures are produced, they are mechanically and thermodynamically stable. All the ordered structures are predicted to be semiconductors, with band gap ranging from 0.2 to 1.2 eV. In addition, the monolayer P$_x$C$_{1-x}$ are predicted to have high carrier mobility, and high optical absorption in the ultraviolet region which shows a red-shift as the P:C ratio increases. These properties make 2D P$_x$C$_{1-x}$ promising materials for applications in electronics and optoelectronics.

Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm, and theoretically exfoliate them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data (http://www.2dmatpedia.org/).