Mapping of atomic catalyst on graphdiyne

Sun, Mingzi; Wu, Tong; Xue, Yurui; Dougherty, Alan William; Huang, Bolong; Li, Yuliang; Yan, Chun Hua

doi:10.1016/j.nanoen.2019.06.008

Cited by 68 publications

(51 citation statements)

References 49 publications

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“…In previous work, [ 19 ] we have presented the potential approach Fuzzy C‐Means (FCM) model for the data separation by ML. Based on such a concept, we introduce a more in‐depth bag‐tree algorithm in ML techniques to develop the potential strategy for identifying the GDY catalysts and determining factors.…”

Section: Resultsmentioning

confidence: 99%

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Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Sun

Dougherty

Huang

et al. 2020

Advanced Energy Materials

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123

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Atomic catalysts (AC) are emerging as a highly attractive research topic, especially in sustainable energy fields. Lack of a full picture of the hydrogen evolution reaction (HER) impedes the future development of potential electrocatalysts. In this work, the systematic investigation of the HER process in graphdyine (GDY) based AC is presented in terms of the adsorption energies, adsorption trend, electronic structures, reaction pathway, and active sites. This comprehensive work innovatively reveals GDY based AC for HER covering all the transition metals (TM) and lanthanide (Ln) metals, enabling the screening of potential catalysts. The density functional theory (DFT) calculations carefully explore the HER performance beyond the comparison of sole H adsorption. Therefore, the screened catalysts candidates not only match with experimental results but also provide significant references for novel catalysts. Moreover, the machine learning (ML) technique bag‐tree approach is innovatively utilized based on the fuzzy model for data separation and converse prediction of the HER performance, which indicates a similar result to the theoretical calculations. From two independent theoretical perspectives (DFT and ML), this work proposes pivotal guidelines for experimental catalyst design and synthesis. The proposed advanced research strategy shows great potential as a general approach in other energy‐related areas.

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Section: Resultsmentioning

confidence: 99%

“…In our previous mapping work, we have proposed the concept of the redox energy barrier to quantify the electron transfer capability of GDY‐AC. [ 19 ] Thus, we further supply the redox energy barrier of all the elements as a new parameter to improve the prediction by ML. For the H adsorption, the prediction shows similar results.…”

Section: Resultsmentioning

confidence: 99%

Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Sun

Dougherty

Huang

et al. 2020

Advanced Energy Materials

Self Cite

123

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“…With the high-speed development of artificial intelligence, the deep learning algorithm attracted more and more attention in a broad research field. Sun et al for the first time used the deep learning algorithm to develop graphdiyne-supported SAECs with zero-valenced central metal atoms [ 111 ]. By quantifying the electron transfer ability and zero-valence stability between metals and graphdiyne support, it was found that among all transition metals, Co, Pd, and Pt showed exceptional stability of zero-valence SAECs based on the evident energy barrier difference between losing electrons and gaining electrons.…”

Section: Synthetic Methods For Saecsmentioning

confidence: 99%

Recent Advances in Single-Atom Electrocatalysts for Oxygen Reduction Reaction

2020

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Oxygen reduction reaction (ORR) plays significant roles in electrochemical energy storage and conversion systems as well as clean synthesis of fine chemicals. However, the ORR process shows sluggish kinetics and requires platinum-group noble metal catalysts to accelerate the reaction. The high cost, rare reservation, and unsatisfied durability significantly impede large-scale commercialization of platinum-based catalysts. Single-atom electrocatalysts (SAECs) featuring with well-defined structure, high intrinsic activity, and maximum atom efficiency have emerged as a novel field in electrocatalytic science since it is promising to substitute expensive platinum-group noble metal catalysts. However, finely fabricating SAECs with uniform and highly dense active sites, fully maximizing the utilization efficiency of active sites, and maintaining the atomically isolated sites as single-atom centers under harsh electrocatalytic conditions remain urgent challenges. In this review, we summarized recent advances of SAECs in synthesis, characterization, oxygen reduction reaction (ORR) performance, and applications in ORR-related H2O2 production, metal-air batteries, and low-temperature fuel cells. Relevant progress on tailoring the coordination structure of isolated metal centers by doping other metals or ligands, enriching the concentration of single-atom sites by increasing metal loadings, and engineering the porosity and electronic structure of the support by optimizing the mass and electron transport are also reviewed. Moreover, general strategies to synthesize SAECs with high metal loadings on practical scale are highlighted, the deep learning algorithm for rational design of SAECs is introduced, and theoretical understanding of active-site structures of SAECs is discussed as well. Perspectives on future directions and remaining challenges of SAECs are presented.

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“…Similar methods have been applied to the design of lead‐free organic–inorganic hybrid perovskite, monoatomic catalysts, light‐emitting diode (LED), organic light‐emitting diode (OLED), and other key materials. The latter two methods have also been verified by experiments.…”

Section: Ai Applications For Materials Science and Engineeringmentioning

confidence: 99%

Artificial Intelligence to Power the Future of Materials Science and Engineering

Sha

Guo

Qing

et al. 2020

Advanced Intelligent Systems

105

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Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.

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Mapping of atomic catalyst on graphdiyne

Cited by 68 publications

References 49 publications

Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Recent Advances in Single-Atom Electrocatalysts for Oxygen Reduction Reaction

Artificial Intelligence to Power the Future of Materials Science and Engineering

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