Characterizing Grain Boundary Effects on Mg2+ Conduction in Metal-Organic Frameworks

Physical Prior Mean Function-Driven Gaussian Processes Search for Minimum-Energy Reaction Paths with a Climbing-Image Nudged Elastic Band: A General Method for Gas-Phase, Interfacial, and Bulk-Phase Reactions

The climbing-image nudged elastic band (CI-NEB) method serves as an indispensable tool for computational chemists, offering insight into minimum-energy reaction paths (MEPs) by delineating both transition states (TSs) and intermediate nonstationary structures along reaction coordinates. However, executing CI-NEB calculations for reactions with extensive reaction coordinate spans necessitates a large number of images to ensure a reliable convergence of the MEPs and TS structures, presenting a computationally demanding optimization challenge, even with mildly costly electronic-structure methods. In this study, we advocate for the utilization of physically inspired prior mean function-based Gaussian processes (GPs) to expedite MEP exploration and TS optimization via the CI-NEB method. By incorporating reliable prior physical approximations into potential energy surface (PES) modeling, we demonstrate enhanced efficiency in multidimensional CI-NEB optimization with surrogate-based optimizers. Our physically informed GP approach not only outperforms traditional nonsurrogate-based optimizers in optimization efficiency but also on-the-fly learns the reaction path valley during optimization, culminating in significant advancements. The surrogate PES derived from our optimization exhibits high accuracy compared to true PES references, aligning with our emphasis on leveraging reliable physical priors for robust and efficient posterior mean learning in GPs. Through a systematic benchmark study encompassing various reaction pathways, including gas-phase, bulk-phase, and interfacial/surface reactions, our physical GPs consistently demonstrate superior efficiency and reliability. For instance, they outperform the popular fast inertial relaxation engine optimizer by approximately a factor of 10, showcasing their versatility and efficacy in exploring reaction mechanisms and surface reaction PESs.

Polysulfides in Magnesium-Sulfur Batteries

Mg-S batteries hold great promise as a potential alternative to Li-based technologies. Their further development hinges on solving a few key challenges, including the lower capacity and poorer cycling performance when compared to Li counterparts. At the heart of the issues is the lack of knowledge on polysulfide chemical behaviors in the Mg-S battery environment. In this Review, a comprehensive overview of the current understanding of polysulfide behaviors in Mg-S batteries is provided. First, a systematic summary of experimental and computational techniques for polysulfide characterization is provided. Next, conversion pathways for Mg polysulfide species within the battery environment are discussed, highlighting the important role of polysulfide solubility in determining reaction kinetics and overall battery performance. The focus then shifts to the negative effects of polysulfide shuttling on Mg-S batteries. The authors outline various strategies for achieving an optimal balance between polysulfide solubility and shuttling, including the use of electrolyte additives, polysulfide-trapping materials, and dual-functional catalysts. Based on the current understanding, the directions for further advancing knowledge of Mg polysulfide chemistry are identified, emphasizing the integration of experiment with computation as a powerful approach to accelerate the development of Mg-S battery technology.