Significance of Epitaxial Growth of PtO2 on Rutile TiO2 for Pt/TiO2 Catalysts.

TiO2-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/TiO2 catalysts is crucial for fine-tuning their catalytic performance. Here, we report an interesting discovery showing the epitaxial growth of PtO2 atomic layers on rutile TiO2, potentially allowing control of the states of active Pt species in Pt/TiO2 catalysts. The presence of PtO2 atomic layers could modulate the geometric configuration and electronic state of the Pt species under reduction conditions, resulting in a spread of the particle shape and obtaining a Pt/PtO2/TiO2 structure with more positive valence of Pt species. As a result, such a catalyst exhibits exceptional electrocatalytic activity and stability toward hydrogen evolution reaction, while also promoting the thermocatalytic CO oxidation, surpassing the performance of the Pt/TiO2 catalyst with no epitaxial structure. This novel epitaxial growth of the PtO2 structure on rutile TiO2 in Pt/TiO2 catalysts shows its potential in the rational design of highly active and economical catalysts toward diverse catalytic reactions.

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. In this Perspective, we present a critical review of recent methodological developments in building CE models for multicomponent materials, with particular focus on different approaches and strategies proposed to address cluster selection and training data generation. We comment on the pros and cons of different methods in a general formalism and present some personal views on the prospects of theoretical approaches to multicomponent materials.

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. However, the importance of integrating significant physical considerations, including, in particular, convex hull preservation, which is the prerequisite for the accurate prediction of phase diagrams, into the training process of the MLFF remains rarely addressed. In this work, a workflow is proposed to train a convex-hull-preserved (CHP) MLFF for binary alloy systems, based on which the order-disorder phase boundary is predicted by using the Wang-Landau Monte Carlo (WLMC) technique. The predicted values for order-disorder phase transition temperatures agree well with the experiment. The CHP-MLFF is further used to build CE models with the same accuracy as the MLFF and higher efficiency in sampling configurational space. Using the results obtained from the MLFF-based WLMC simulation as a reference, the performances of different schemes for constructing CE models were evaluated in a transparent manner, which revealed the close correlation between the prediction accuracy of ground-state configurations and that of the order-disorder phase transition temperature. This work clearly indicates the great importance of reproducing the convex hull and energetics of ground-state configurations when constructing surrogate Hamiltonians for the statistical modeling of alloyed systems.

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. However, such a test only confirms the convergence of the predictive capability of the CE model within the training set, and it is unknown whether the convergence of the CV score would lead to robust thermodynamic simulation results such as order-disorder phase transition temperature Tc. In this work, using carbon defective MoC1-x as a model system and aided by the machine-learning force field technique, a training data pool with about 13000 configurations has been efficiently obtained and used to generate different training sets of the same size randomly. By conducting parallel Monte Carlo simulations with the CE models trained with different randomly selected training sets, the uncertainty in calculated Tc can be evaluated at different training set sizes. It is found that the training set size that is sufficient for the CV score to converge still leads to a significant uncertainty in the predicted Tc and that the latter can be considerably reduced by enlarging the training set to that of a few thousand configurations. This work highlights the importance of using a large training set to build the optimal CE model that can achieve robust statistical modeling results and the facility provided by the machine-learning force field approach to efficiently produce adequate training data.