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Metasurface-enhanced Raman spectroscopy is used to characterize theclassical,crossover, andquantumregimes of a colloidal metasurface as a function of gap distance. 

Abstract Liquid water under nanoscale confinement has attracted intensive attention due to its pivotal role in understanding various phenomena across many scientific fields. MXenes serve an ideal paradigm for investigating the dynamic behaviors of nanoconfined water in a hydrophilic environment. Combining deep neural networks and an active learning scheme, here we elucidate the proton‐driven dynamics of water molecules confined between V₂CT_xsheets using molecular dynamics simulation. Firstly, we have found that the Eigen and Zundel cations can inhibit water‐induced oxidation by adjusting the orientation of water molecules, thus proposing a general antioxidant strategy. Besides, we also identified a hexagonal ice phase with abnormal bonding rules at room temperature, rather than only at ultralow temperatures as other studies reported, and further captured the proton‐induced water phase transition. This highlighted the importance of protons in the maintaining stable crystal phase and phase transition of water. Furthermore, we discussed the conversions of different water structures and water diffusivity with changing proton concentrations in detail. The results provide useful guidance in practical applications of MXenes including developing antioxidant strategies, identifying novel 2D water phases and optimizing energy storage and conversion. 

The recent theory-driven discovery of a class of clathrate hydrides (e.g., CaH₆, YH₆, YH₉, and LaH₁₀) with superconducting critical temperatures (T_c) well above 200 K has opened the prospects for “hot” superconductivity above room temperature under pressure. Recent efforts focus on the search for superconductors among ternary hydrides that accommodate more diverse material types and configurations compared to binary hydrides. Through extensive computational searches, we report the prediction of a unique class of thermodynamically stable clathrate hydrides structures consisting of two previously unreported H₂₄and H₃₀hydrogen clathrate cages at megabar pressures. Among these phases, LaSc₂H₂₄shows potential hot superconductivity at the thermodynamically stable pressure range of 167 to 300 GPa, with calculatedT_cs up to 331 K at 250 GPa and 316 K at 167 GPa when the important effects of anharmonicity are included. The very high critical temperatures are attributed to an unusually large hydrogen-derived density of states at the Fermi level arising from the newly reported peculiar H₃₀as well as H₂₄cages in the structure. Our predicted introduction of Sc in the La–H system is expected to facilitate future design and realization of hot superconductors in ternary clathrate superhydrides. 

Abstract Checkerboard lattices—where the resulting structure is open, porous, and highly symmetric—are difficult to create by self-assembly. Synthetic systems that adopt such structures typically rely on shape complementarity and site-specific chemical interactions that are only available to biomolecular systems (e.g., protein, DNA). Here we show the assembly of checkerboard lattices from colloidal nanocrystals that harness the effects of multiple, coupled physical forces at disparate length scales (interfacial, interparticle, and intermolecular) and that do not rely on chemical binding. Colloidal Ag nanocubes were bi-functionalized with mixtures of hydrophilic and hydrophobic surface ligands and subsequently assembled at an air–water interface. Using feedback between molecular dynamics simulations and interfacial assembly experiments, we achieve a periodic checkerboard mesostructure that represents a tiny fraction of the phase space associated with the polymer-grafted nanocrystals used in these experiments. In a broader context, this work expands our knowledge of non-specific nanocrystal interactions and presents a computation-guided strategy for designing self-assembling materials. 

Abstract MXenes are 2D materials with great potential in various applications. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but low cost. The oxidation behaviors of super large aqueous MXene systems are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. Free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponentially decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation of super‐sized aqueous MXene systems. It opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes. 

The molecular electronic devices based on self-assembled monolayer (SAM) on metal surfaces demonstrate novel electronic functions for device minimization yet are unable to realize in practical applications, due to their instability against oxidation of the sulfur-metal bond. This paper describes an alternative to the thiolate anchoring group to form stable SAMs on gold by selenides anchoring group. Because of the formation of strong selenium-gold bonds, these stable SAMs allow us to incorporate them in molecular tunnel junctions to yield extremely stable junctions for over 200 days. A detailed structural characterization supported by spectroscopy and first-principles modeling shows that the oxidation process is much slower with the selenium-gold bond than the sulfur-gold bond, and the selenium-gold bond is strong enough to avoid bond breaking even when it is eventually oxidized. This proof of concept demonstrates that the extraordinarily stable SAMs derived from selenides are useful for long-lived molecular electronic devices and can possibly become important in many air-stable applications involving SAMs. 

Abstract Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.