Substitution of Sulfur Atoms on Ni-Mo-S by Ammonia – A DFT Study

Design of New Inorganic Crystals with the Desired Composition Using Deep Learning

New solid-state materials have been discovered using various approaches from atom substitution in density functional theory (DFT) to generative models in machine learning. Recently, generative models have shown promising performance in finding new materials. Crystal generation with deep learning has been applied in various methods to discover new crystals. However, most generative models can only be applied to materials with specific elements or generate structures with random compositions. In this work, we developed a model that can generate crystals with desired compositions based on a crystal diffusion variational autoencoder. We generated crystal structures for 14 compositions of three types of materials in different applications. The generated structures were further stabilized using DFT calculations. We found the most stable structures in the existing database for all but one composition, even though eight compositions among them were not in the data set trained in a crystal diffusion variational autoencoder. This substantiates the prospect of the generation of an extensive range of compositions. Finally, 205 unique new crystal materials with energy above hull <100 meV/atom were generated. Moreover, we compared the average formation energy of the crystals generated from five compositions, two of which were hypothetical, with that of traditional methods like atom substitution and a generative model. The generated structures had lower formation energy than those of other models, except for one composition. These results demonstrate that our approach can be applied stably in various fields to design stable inorganic materials based on machine learning.

Synthesis of Ni-Modified ZSM-5 Zeolites and Their Catalytic Performance in n-Octane Hydroconversion

Ni-modified ZSM-5 zeolites with different nickel contents were successfully prepared by the in situ synthesis method and the impregnation method. The synthesized samples were characterized by XRD, SEM, N₂ adsorption–desorption isothermals, and Py-FTIR. The characterization results show that both the textural properties and crystallization of Ni-modified ZSM-5 zeolites were preserved well, and their acidic properties can be modulated after nickel modification. The corresponding NiMo catalysts supported on Ni-modified ZSM-5 zeolites were prepared by the incipient wetness co-impregnation method, and their catalytic performances were evaluated in n-octane hydroconversion. Compared to the those modified by the in situ synthesis method, ZSM-5 zeolite-supported catalysts modified by the impregnation method exhibit higher stability and higher isomerization selectivity. This is due to the synergistic effect between Brønsted acid sites and Lewis acid sites on the Ni-modified ZSM-5 zeolites, especially for the NiMo/1Ni-Z5 catalyst.