Improved hydrogen evolution performance of Ni-based nanoporous catalyst with Mo and B co-addition.

The exploration of efficient and stable noble-metal-free electrocatalysts for hydrogen evolution reaction (HER) is of great interest for the development of electrochemical hydrogen production technologies. Herein, nanoporous Ni-based catalyst with Mo and B co-addition (NiMoB) prepared by dealloying is reported as an efficient electrocatalysts for HER. The nanoporous NiMoB achieves an overpotential of 31 mV at 10 mA cm-2, along with exceptional catalytic stability in alkaline electrolyte. Density functional theory (DFT) calculations reveal that the incorporation of Mo and B can synergistically optimize the electronic structure and regulate the adsorption of HER intermediates on the Ni active site, thus accelerating the HER kinetics. This study provides a new perspective for the development of non-precious Ni-based catalysts towards efficient hydrogen energy conversion.

Accelerating the discovery of novel magnetic materials using machine learning-guided adaptive feedback.

Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe3CoB2, through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe3CoB2 exhibits a high magnetic anisotropy (K1 = 1.2 MJ/m3) and saturation magnetic polarization (Js = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials.

Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements.

We integrate a deep machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary compounds. Using La-Si-P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La-Si-P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La5SiP3 and La2SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La-Si-P structures from our ML-guided predictions, a number of low-energy La-X-P phases (X = Ge, Sn, Pb) are predicted.

Amorphous FeNiNbPC nanoprous structure for efficient and stable electrochemical oxygen evolution.

The oxygen evolution reaction (OER) is a crucial process for water splitting. Reducing overpotential is a great challenge because of four electrons transfer and slow kinetics compared to the hydrogen evolution reaction (HER). Highly efficient and stable OER catalyst with low-cost is important for industrial hydrogen production by water splitting. Here we report a simple approach to synthesize free-standing amorphous FexNi77-xNb3P13C7 with the nanoporous structure through electrochemical dealloying. The np-Fe50Ni27Nb3P13C7 exhibits remarkable OER catalytic activity with a low overpotential of 248 mV to achieve the current density of 10 mA cm-2 in 6 M KOH solution. Also, the np-Fe50Ni27Nb3P13C7 exhibits good long-term stability. The improved OER property is due to bimetallic synergy, decreased resistance of charge transfer, nanoporous structure, amorphous nature, and the generation of NiOOH during the OER process. The free-standing amorphous catalysts with nanoporous structure via electrodealloying method provide a promising approach to boost the performance of non-noble metal OER catalysts for the applications.