Pressure-Induced Amorphization and Crystallization of Heterophase Pd Nanostructures
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Li, Qian↗
Engineering topics
Wen, Xiaodong
Publications and source records attributed to Wen, Xiaodong.
A stable low-temperature H 2 -production catalyst by crowding Pt on α-MoC
The water-gas shift (WGS) reaction is an industrially important source of pure hydrogen (H 2 ) at the expense of carbon monoxide and water. This reaction is of interest for fuel-cell applications, but requires WGS catalysts that are durable and highly active at low temperatures. Here we demonstrate that the structure (Pt 1 Pt n )/α-MoC, where isolated platinum atoms (Pt 1 ) and subnanometre platinum clusters (Pt-n) are stabilized on alpha-molybdenum carbide (α-MoC), catalyses the WGS reaction even at 313 kelvin, with a hydrogen-production pathway involving direct carbon monoxide dissociation identified. We find that it is critical to crowd the α-MoC surface with Pt 1 and Pt n species, which prevents oxidation of the support that would cause catalyst deactivation, as seen with gold/α-MoC, and gives our system high stability and a high metal-normalized turnover number of 4,300,000 moles of hydrogen per mole of platinum. We anticipate that the strategy demonstrated here will be pivotal for the design of highly active and stable catalysts for effective activation of important molecules such as water and carbon monoxide for energy production.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction
Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C 7 O 2 H 2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (Al x Ga y In z ) 2 O 3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features.
36 MATERIALS SCIENCE↗