Significance: Understanding and controlling the ion migration in a solid-state semiconductor is essential for device performance in modern photonics, electronics, and optoelectronics. Direct microstructural observation and atomic-scale mechanism of the intrinsic lattice diffusion are still highly challenging. Here, we report the direct visualization of halide anion interdiffusion in CsPbCl ₃ –CsPbBr ₃ single crystalline nanowire heterojunctions via nanoscale imaging, allowing for the direct measuring of intrinsic lattice diffusivities. The intrinsic halide diffusivities were found to be several orders of magnitudes lower than those reported in polycrystalline thin films. Together with multiscale modeling, we further demonstrate how anionic diffusion in lead halides is determined by low barriers to vacancy hopping and facilitated by the soft perovskite lattice.

On the surface: The uptake and hydrolysis of N ₂ O ₅ from the atmosphere by aqueous aerosols was long thought to occur by solvation and subsequent hydrolysis in the bulk of the aerosol. However, this mechanistic hypothesis was unverifiable because of the fast reaction kinetics. Galib et al. used molecular simulations to show instead that the mechanism is the inverse: Interfacial hydrolysis is followed by solvation into the interior. Their reactive uptake model is consistent with some existing experimental observations. Science , this issue p. 921

Colloidal nanocrystals of metals, semiconductors, and other functional materials can self-assemble into long-range ordered crystalline and quasicrystalline phases, but insulating organic surface ligands prevent the development of collective electronic states in ordered nanocrystal assemblies. We reversibly self-assembled colloidal nanocrystals of gold, platinum, nickel, lead sulfide, and lead selenide with conductive inorganic ligands into supercrystals exhibiting optical and electronic properties consistent with strong electronic coupling between the constituent nanocrystals. The phase behavior of charge-stabilized nanocrystals can be rationalized and navigated with phase diagrams computed for particles interacting through short-range attractive potentials. By finely tuning interparticle interactions, the assembly was directed either through one-step nucleation or nonclassical two-step nucleation pathways. In the latter case, the nucleation was preceded by the formation of two metastable colloidal fluids.

Statistical mechanics of constant-potential molecular simulations yields a new fluctuation–dissipation relation for the differential capacitance.

By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. We find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid–vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated.

We describe a framework to reduce the computational effort to evaluate large deviation functions of time integrated observables within nonequilibrium steady states. We do this by incorporating an auxiliary dynamics into trajectory based Monte Carlo calculations, through a transformation of the system’s propagator using an approximate guiding function. This procedure importance samples the trajectories that most contribute to the large deviation function, mitigating the exponential complexity of such calculations. We illustrate the method by studying driven diffusion and interacting lattice models in one and two spatial dimensions. Our work offers an avenue to calculate large deviation functions for high dimensional systems driven far from equilibrium.