Three-dimensional Zn-based alloys for dendrite-free aqueous Zn battery in dual-cation electrolytes.

Aqueous zinc-ion batteries, in terms of integration with high safety, environmental benignity, and low cost, have attracted much attention for powering electronic devices and storage systems. However, the interface instability issues at the Zn anode caused by detrimental side reactions such as dendrite growth, hydrogen evolution, and metal corrosion at the solid (anode)/liquid (electrolyte) interface impede their practical applications in the fields requiring long-term performance persistence. Despite the rapid progress in suppressing the side reactions at the materials interface, the mechanism of ion storage and dendrite formation in practical aqueous zinc-ion batteries with dual-cation aqueous electrolytes is still unclear. Herein, we design an interface material consisting of forest-like three-dimensional zinc-copper alloy with engineered surfaces to explore the Zn plating/stripping mode in dual-cation electrolytes. The three-dimensional nanostructured surface of zinc-copper alloy is demonstrated to be in favor of effectively regulating the reaction kinetics of Zn plating/stripping processes. The developed interface materials suppress the dendrite growth on the anode surface towards high-performance persistent aqueous zinc-ion batteries in the aqueous electrolytes containing single and dual cations. This work remarkably enhances the fundamental understanding of dual-cation intercalation chemistry in aqueous electrochemical systems and provides a guide for exploring high-performance aqueous zinc-ion batteries and beyond.

Control of phase ordering and elastic properties in phase field crystals through three-point direct correlation.

Effects of three-point direct correlation on properties of the phase field crystal (PFC) modeling are examined for the control of various ordered and disordered phases and their coexistence in both three-dimensional and two-dimensional systems. Such effects are manifested via the corresponding gradient nonlinearity in the PFC free-energy functional that is derived from classical density functional theory. Their significant impacts on the stability regimes of ordered phases, phase diagrams, and elastic properties of the system, as compared to those of the original PFC model, are revealed through systematic analyses and simulations. The nontrivial contribution from three-point direct correlation leads to the variation of the critical point of order-disorder transition to which all the phase boundaries in the temperature-density phase diagram converge. It also enables the variation and control of system elastic constants over a substantial range as needed in modeling different types of materials with the same crystalline structure but different elastic properties. The capability of this PFC approach in modeling both solid and soft matter systems is further demonstrated through the effect of three-point correlation on controlling the vapor-liquid-solid coexistence and transitions for body-centered cubic phase and on achieving the liquid-stripe or liquid-lamellar phase coexistence. All these provide a valuable and efficient method for the study of structural ordering and evolution in various types of material systems.

Reinforcement learning to boost molecular docking upon protein conformational ensemble.

Intrinsically disordered proteins (IDPs) are widely involved in human diseases and thus are attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and tens of thousands of conformations. Here, we propose a reversible upper confidence bound (UCB) algorithm for the virtual screening of IDPs to address the influence of the conformation ensemble. The docking process is dynamically arranged so that attempts are focused near the boundary to separate top ligands from the bulk accurately. It is demonstrated in the example of transcription factor c-Myc that the average docking number per ligand can be greatly reduced while the performance is merely slightly affected. This study suggests that reinforcement learning is highly efficient in solving the bottleneck of virtual screening due to the conformation ensemble in the rational drug design of IDPs.

Fluctuation correlations as major determinants of structure- and dynamics-driven allosteric effects.

Allosteric control is essential for regulating biological functions whereby stimuli such as ligand binding at one site on a protein cause a response at a distant functional site. Correlations between different sites in proteins have been used widely in identifying allosteric sites and pathways, and in designing allosteric drugs. However, the deterministic connection between correlations and allostery remains unsolved, especially considering that there are various types of correlations. Here, we combine perturbation-theory analysis and numerical calculations to study both structure- and dynamics-driven allosteric effects in an anisotropic network model (ANM). The results reveal that the allosteries are determined by the correlation (covariance) of distance fluctuations, but are irrelevant to the usual displacement correlations or time-delayed correlations. Dynamics-driven allostery is weaker than structure-driven allostery by at least one to two orders of magnitude. The intrinsic allostery capacity decays with distance by an exponential law, with the resulting characteristic distance parameter lying in the range of 7-10 Å for structure-driven allostery and 4-5 Å for dynamics-driven allostery. The importance of the cutoff distance of the ANM is also addressed.