Surging interests exist in double‐atom catalysts (DACs), which not only inherit the advantages of single‐atom catalysts (SACs) (e.g., ultimate atomic utilization, high activity, and selectivity) but also overcome the drawbacks of SACs (e.g., low loading and isolated active site). The design of DACs, however, remains cost‐prohibitive for both experimental and computational studies, due to their huge design space. Herein, by means of density functional theory (DFT) and topological information‐based machine‐learning (ML) algorithms, we present a data‐driven high‐throughput design principle to evaluate the stability and activity of 16 767 DACs for oxygen evolution (OER) and oxygen reduction (ORR) reactions. The rational design reveals 511 types of DACs with OER activity superior to IrO2 (110), 855 types of DACs with ORR activity superior to Pt (111), and 248 bifunctional DACs with high catalytic performance for both OER and ORR. An intrinsic descriptor is revealed to correlate the catalytic activity of a DAC with the electronic structures of the DAC and its bonding carbon surface structure. This data‐driven high‐throughput approach not only yields remarkable prediction precision (>0.926 R‐squared) but also enables a notable 144 000‐fold reduction of screening time compared with pure DFT calculations, holding promise to drastically accelerate the design of high‐performance DACs.
Summary
The oxygen evolution reaction (OER) is a critical reaction for energy-related applications, yet suffers from its slow kinetics and large overpotential. It is desirable to develop effective OER electrocatalysts, such as single-atom catalysts (SACs). Here, we demonstrate machine learning (ML)-accelerated prediction of OER overpotential of all transition metals. Based on density functional theory (DFT) calculations of 15 species of SACs, we design a topological information-based ML model to map the OER overpotentials with atomic properties of the corresponding SACs. The trained ML model not only yields remarkable prediction precision (relative error of 6.49%) but also enables a 130,000-fold reduction of prediction time in comparison with pure DFT calculation. Furthermore, an intrinsic descriptor that correlates the overpotential of an SAC with its atomic properties is revealed. The approach and results from this study can be readily applicable to screen other SACs and significantly accelerate the design of high-performance catalysts for many other reactions.
With maximum atom-utilization efficiency, single atom catalysts (SACs) are surging as a new research frontier in catalysis science. However, fabricating SACs and maintaining their thermodynamic stability remain challenging and thus...
Extracting damage features precisely while overcoming the adverse interferences of measurement noise and incomplete data is a problem demanding prompt solution in structural health monitoring (SHM). In this article, we present a deep-learning-based method that can extract the damage features from mode shapes without utilizing any hand-engineered feature or prior knowledge. To meet various requirements of the damage scenarios, we use convolutional neural network (CNN) algorithm and design a new network architecture: a multi-scale module, which helps in extracting features at various scales that can reduce the interference of contaminated data; stacked residual learning modules, which help in accelerating the network convergence; and a global average pooling layer, which helps in reducing the consumption of computing resources and obtaining a regression performance. An extensive evaluation of the proposed method is conducted by using datasets based on numerical simulations, along with two datasets based on laboratory measurements. The transferring parameter methodology is introduced to reduce retraining requirement without any decreases in precision. Furthermore, we plot the feature vectors of each layer to discuss the damage features learned at these layers and additionally provide the basis for explaining the working principle of the neural network. The results show that our proposed method has accuracy improvements of at least 10% over other network architectures.
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