Revisiting γ-alumina surface models through the topotactic transformation of boehmite surfaces

Advances in morphology-controlled alumina and its supported Pd catalysts: synthesis and applications

Alumina materials, as one of the cornerstones of the modern chemical industry, possess physical and chemical properties that include excellent mechanical strength and structure stability, which also make them highly suitable as catalyst supports. Alumina-supported Pd-based catalysts with the advantages of exceptional catalytic performance, flexible regulated surface metal/acid sites, and good regeneration ability have been widely used in many traditional chemical industry fields and have also shown great application prospects in emerging fields. This review aims to provide an overview of the recent advances in alumina and its supported Pd-based catalysts. Specifically, the synthesis strategies, morphology transformation mechanisms, and structural properties of alumina with various morphologies are comprehensively summarized and discussed in-depth. Then, the preparation approaches of Pd/Al2O3 catalysts (impregnation, precipitation, and other emerging methods), as well as the metal-support interactions (MSIs), are revisited. Moreover, Some promising applications have been chosen as representative reactions in fine chemicals, environmental purification, and sustainable development fields to highlight the universal functionality of the alumina-supported Pd-based catalysts. The role of the Pd species, alumina support, promoters, and metal-support interactions in the enhancement of catalytic performance are also discussed. Finally, some challenges and upcoming opportunities in the academic and industrial application of the alumina and its supported Pd-based are presented and put forward.

Managing Expectations and Imbalanced Training Data in Reactive Force Field Development: An Application to Water Adsorption on Alumina

ReaxFF is a computationally efficient model for reactive molecular dynamics simulations that has been applied to a wide variety of chemical systems. When ReaxFF parameters are not yet available for a chemistry of interest, they must be (re)optimized, for which one defines a set of training data that the new ReaxFF parameters should reproduce. ReaxFF training sets typically contain diverse properties with different units, some of which are more abundant (by orders of magnitude) than others. To find the best parameters, one conventionally minimizes a weighted sum of squared errors over all of the data in the training set. One of the challenges in such numerical optimizations is to assign weights so that the optimized parameters represent a good compromise among all the requirements defined in the training set. This work introduces a new loss function, called Balanced Loss, and a workflow that replaces weight assignment with a more manageable procedure. The training data are divided into categories with corresponding "tolerances", i.e., acceptable root-mean-square errors for the categories, which define the expectations for the optimized ReaxFF parameters. Through the Log-Sum-Exp form of Balanced Loss, the parameter optimization is also a validation of one's expectations, providing meaningful feedback that can be used to reconfigure the tolerances if needed. The new methodology is demonstrated with a nontrivial parametrization of ReaxFF for water adsorption on alumina. This results in a new force field that reproduces both the rare and frequent properties of a validation set not used for training. We also demonstrate the robustness of the new force field with a molecular dynamics simulation of water desorption from a γ-Al2O3 slab model.