MXene nanosheet materials with fast charge transfer characteristics and superior electrical conductivity are popular candidates for oxygen electrocatalysts. In this paper, the oxygen reduction and evolution reaction (ORR and OER, respectively) catalytic activities of 78 M-N4-Gr/MXene (Ti2C, Nb2C, and V2C) heterostructure nanosheets were investigated based on density functional theory (DFT) calculations and machine learning (ML) predictions. The random forest regression (RFR) algorithm proved to be the most desirable ML model for the prediction of catalytic activity with ORR/OER root mean square errors of 0.10 and 0.23 V, respectively. The obtained results show that Ni-N4-Gr/Nb2C (η *ORR = 0.31 V) and Ni-N4-Gr/V2C (η *ORR = 0.32 V) are effective ORR catalysts (prediction error of 0.03 V only), Ru-N4-Gr/Nb2C (η *OER = 0.40 V) is a promising OER catalyst, and Co-N4-Gr/V2C (η *ORR = 0.45 V/η *OER = 0.30 V) is an excellent bifunctional catalyst. Through further model analysis, the d band center of the metal active site was determined to be the most effective descriptor and a volcano curve relationship between the intrinsic descriptor and the overpotential was established based on the characteristic importance and Pearson correlation. The research approach adopted in this study may be used to screen and design other MXene heterojunction catalysts and remarkably accelerate catalyst design for many significant reactions.

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The properties and mechanism of adsorption of hydrogen molecules on NLi4-decorated graphdiyne nanosheets (NLi4-GDY) were investigated based on the density functional theory. Accordingly, the 76 hydrogen storage configurations of NLi4-SGDY, NLi4-GDY-A, and NLi4-SGDY-A were constructed by nonmetallic B/N substitution and A (A = B, N, Si, P) adsorption. The electronic structure of each configuration and the structure with the desired high hydrogen storage capacity were explored. The results showed that the charge on the lithium atom in the NLi4-GDY structure was transferred to the pristine GDY, thus forming a partial electron field around each lithium atom, which subsequently led to the polarized adsorption of the hydrogen molecule. Through B/N substitution and A adsorption, we have modulated the degree of charge transfer in Li atoms across different configurations, thereby manipulating the strength of partial electric fields and resulting in varying hydrogen storage capacities for NLi4-SGDY, NLi4-GDY-A, and NLi4-SGDY-A configurations. According to DFT calculations, the optimal hydrogen storage structures for NLi4-GDY, NLi4-SGDY, NLi4-GDY-A, and NLi4-SGDY-A were 6H2-2NLi4-GDY, 14H2-2NLi4-N11′, 14H2-2NLi4-GDY-Si, and 18H2-2NLi4-N1-P, respectively, with gravimetric densities of 3.85, 8.09, 7.29, and 8.91 wt %, respectively. Considering the exceptional hydrogen storage properties of the aforementioned configurations, we propose that NLi4-decorated GDY with B/N substitution and A adsorption offers a promising avenue for discovering novel hydrogen storage materials.

Magnesium (Mg)-based alloys exhibit excellent mechanical and electrical properties for fuel cell bipolar plates. Traditional Mgbased alloy design relies on theoretical and experimental methods with long design cycles based on trial-and-error that focuses on one material. We analyzed the elastic modulus and electrical conductivity of 248 Mgbased alloys by combining density functional theory (DFT) and machine learning (ML). The decision tree classification (DTC) and gradient boosting regression (GBR) algorithms were chosen to predict the mechanical stability and electrical conductivity of the Mg-based alloys (the accuracy and R 2 values of DTC and GBR were 0.94 and 0.83, respectively), and the atomic volume, density, and boiling point were identified as the three most important factors affecting alloy properties. ML successfully predicted the mechanical stability and scalar conductivity of HfMgPt 2 (T = 350 K, 0.666 × 10 21 ). We constructed a new Mg-based quaternary alloy, Mg 3 ZrTi 4 Ir 8 , based on the predicted Mg-based ternary alloy with a high scalar electrical conductivity and verified its good mechanical properties. This work provides a theoretical basis for the design of Mg-based alloys for fuel cell bipolar plates, accelerates the screening of Mg-based alloys, and reveals the reliability of the "composition−property−composition" model established by DFT and ML.