A Highly Active Molybdenum Carbide Catalyst with Dynamic Carbon Flux for Reverse Water-Gas Shift Reaction.

Molybdenum carbide has been reported as an efficient and stable catalyst for reverse water-gas shift (RWGS) reaction. The conventional understanding of the mechanism suggests domination of the surface phenomena, with only surface or subsurface layers partaking in the catalytic cycle. In this study, we presented a highly active MoC catalyst from carburization process, which showed a mass-specific reaction rate over 260 μm with dynamic carbon flux in the bulk phase of the catalyst.

Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials.

First-principles approaches based on density functional theory (DFT) have played important roles in the theoretical study of multicomponent alloyed materials. Considering the highly demanding computational cost of direct DFT-based sampling of the configurational space, it is crucial to build efficient and low-cost surrogate Hamiltonian models with DFT accuracy for efficient simulation of alloyed systems with configurational disorder. Recently, the machine learning force field (MLFF) method has been proposed to tackle complicated multicomponent disordered systems.

Significance of Epitaxial Growth of PtO on Rutile TiO for Pt/TiO Catalysts.

TiO-supported Pt species have been widely applied in numerous critical reactions involving photo-, thermo-, and electrochemical-catalysis for decades. Manipulation of the state of the Pt species in Pt/TiO catalysts is crucial for fine-tuning their catalytic performance. Here, we report an interesting discovery showing the epitaxial growth of PtO atomic layers on rutile TiO, potentially allowing control of the states of active Pt species in Pt/TiO catalysts.

Perspective on optimal strategies of building cluster expansion models for configurationally disordered materials.

Cluster expansion (CE) provides a general framework for first-principles-based theoretical modeling of multicomponent materials with configurational disorder, which has achieved remarkable success in the theoretical study of a variety of material properties and systems of different nature. On the other hand, there remains a lack of consensus regarding what is the optimal strategy to build CE models efficiently that can deliver accurate and robust prediction for both ground state energetic properties and statistical thermodynamic properties at finite temperature. There have been continuous efforts to develop more effective approaches to CE model building, which are further promoted by recent tremendous interest of applying machine learning techniques in materials research.

Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence.

Cluster expansion (CE) is a powerful theoretical tool to study the configuration-dependent properties of substitutionally disordered systems. Typically, a CE model is built by fitting a few tens or hundreds of target quantities calculated by first-principles approaches. To validate the reliability of the model, a convergence test of the cross-validation (CV) score to the training set size is commonly conducted to verify the sufficiency of the training data.