Achieving rational design of alloy catalysts using a descriptor based on a quantitative structure–energy equation

Periodic Trends in Adsorption Energies around Single-Atom Alloy Active Sites

Single-atom alloys (SAAs) make up a special class of alloy surface catalysts that offer well-defined, isolated active sites in a more inert metal host. The dopant sites are generally assumed to have little or no influence on the properties of the host metal, and transport of chemical reactants and products to and from the dopant sites is generally assumed to be facile. Here, by performing density functional theory calculations and surface science experiments, we identify a new physical effect on SAA surfaces, whereby adsorption is destabilized by ≤300 meV on host sites within the perimeter of the reactive dopant site. We identify periodic trends for this behavior and demonstrate a zone of exclusion around the reactive sites for a range of adsorbates and combinations of host and dopant metals. Experiments confirm an increased barrier for diffusion of CO toward the dopant on a RhCu SAA. This effect offers new possibilities for understanding and designing active sites with tunable energetic landscapes surrounding them.

Towards the object-oriented design of active hydrogen evolution catalysts on single-atom alloys

Given a desired property, locating relevant materials is always highly desired but very challenging in a range of areas, including heterogeneous catalysis. Obviously, object-oriented design/screening is an ideal solution to this problem. Herein, we develop an inverse catalyst design workflow in Python (CATIDPy) that utilizes a genetic-algorithm-based global optimization method to guide on-the-fly density functional theory calculations, successfully realizing the highly accelerated location of active single-atom alloy (SAA) catalysts for the hydrogen evolution reaction (HER). 70 binary and 752 ternary SAA candidate catalysts are identified for the HER. Furthermore, via considering the segregation stability and cost of materials, we extracted 6 binary and 142 ternary SAA candidate catalysts that are recommended for experimental synthesis. Remarkably, guided by these theoretical identifications, homogeneously dispersed Ni-based bimetallic catalysts (e.g., NiMo, NiAl, Ni₃Al, NiGa, and NiIn) were synthesized experimentally to test the reliability of the CATIDPy workflow, and they showed superior HER performance to bare Ni foam, indicating huge potential for use in real-world water electrolysis techniques. Perhaps more importantly, these results demonstrate the capacity of such a proposed approach for investigating unexplored chemical spaces to efficiently design promising catalysts without knowledge from the expert domain, which has far-reaching implications.

An inverse catalyst design workflow in Python (CATIDPy) for discovering unexplored chemical spaces successfully realized the highly accelerated location of active single-atom alloy (SAA) catalysts for the hydrogen evolution reaction (HER).

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

CATKINAS: A large-scale catalytic microkinetic analysis software for mechanism auto-analysis and catalyst screening

As an effective method to analyze complex catalytic reaction networks, microkinetic modeling is gaining increasing popularity in the catalytic activity evaluation and rational design of heterogeneous catalysts. An automated simulator with stable and reliable performance is especially useful and in great request. Here we introduce the CATKINAS package developed for large-scale microkinetic modeling and analysis. Featuring with a multilevel solver and a multifunctional analyzer, CATKINAS can provide both accurate solutions and various quantitative and automatic analysis for a wide range of catalytic systems. The structure and the basic workflow are overviewed with the multilevel solver particularly illustrated. Also, we take the CO methanation reaction as an example to illustrate the application and efficiency of the CATKINAS package.

Rational catalyst design for CO oxidation: a gradient-based optimization strategy

In this work, we proposed a gradient-based optimization strategy for rational catalyst design.

Microscopic Nature of Gas Adsorption on WO3 Surfaces: Electron Interaction and Localization

Even though WO₃ has been used in gas-sensors for many years, little is known about the gas-sensing mechanism. Using first-principles density functional theory and experimental methods, we study the adsorption of CO, H₂, NH₃, and NO₂ on the surface of WO₃ (001). The results indicate that the surface undergoes reconstruction after gas adsorption, which inevitably causes the localization of surface electrons and a change of the electric (sensing) signal. Through the analysis of atomic orbital and molecular orbital, the adsorption mechanism can be effectively predicted. The above analysis confirms that the resistance change is only related to the electronic behavior of the surface and the gas. We corrected problems associated with adsorption energy to characterize the adsorption strength of a gas on a surface, and we investigated the effect of the test temperature and test environment on both electronic interaction and the final electric sensing signal.

Unraveling the Photogenerated Electron Localization on the Defect-Free CH3NH3PbI3(001) Surfaces: Understanding and Implications from a First-Principles Study

The localization of photogenerated electrons in photovoltaic and photocatalytic materials is crucial for reducing the electron-hole recombination rate. Here, the photogenerated electron localization is systematically investigated on the CH₃NH₃PbI₃ (MAPbI₃) perovskite using first-principles calculations. It is found that under vacuum conditions, the photogenerated electron is delocalized in the MAPbI₃ bulk as well as on the stochiometric MAPbI₃(001) surface with the CH₃NH₃I (MAI) termination, while it is trapped on the defect-free PbI₂-terminated surface. Our ab initio molecular dynamics simulations reveal that the introduction of solutions will prompt the formation of localized electronic states. The photogenerated electron is discovered to be localized on both the MAI- and PbI₂-terminated surfaces in the presence of solutions with different concentrations of HI, from pure water to the saturated solution. We demonstrate that the Pb-I bond weakening or breaking resulting in an unsaturated coordination of a Pb site is the prerequisite to trap the photogenerated electron.