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Pt single atoms have unique power in enhancing and accelerating OER active NiOOH phase transformation from NiO., Phase transformation of electrode materials widely occurs in electrocatalytic reactions. Metal oxides are promising electrocatalysts for the oxygen evolution reaction (OER); their phase transformation is a key step for the multi-electron OER, and requires extra overpotential. However, little attention has been paid to accelerating and enhancing the phase transformation. Here, we report for the first time that single-atom Pt incorporated into the bulk crystalline phase of porous NiO nanocubes (0.5 wt% Pt/NiO) can greatly promote the active phase (NiOOH) evolution. The Pt doping was achieved by a scalable nanocasting approach using SiO2 as the hard template. In comparison with Pt/NiO samples with PtO2 nanoparticles segregated at the NiO surface (1 wt% Pt), as well as atomistic Pt atoms solely bound at the surface by atomic layer deposition, the bulk Pt doping shows the strongest power in facilitating active phase transformation, which leads to improved OER activity with reduced overpotential and Tafel slope. Experiential data revealed that the charge-transfer from Pt to Ni through O leads to a local weaker Ni–O bond. First principles calculations confirmed that rather than acting as an active site for the OER, monatomic Pt effectively increases the phase transformation rate by reducing the migration barrier of nearby Ni atoms. Our discoveries reveal the relationships of the heteroatom doped structure and phase transformation behavior during the electrochemical process and offer a new route for designing high-performance electrocatalysts.

A powerful material discovery tool is invented by combining SSW global optimization with neural network computing, which identifies unprecedented TiO2 phases., While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a “Global-to-Global” approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO2, is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.