Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm

Yang, Fan; Cheng, Guanjian; Yin, Wan-Jian

doi:10.1007/s40843-024-2868-x

Sci. China Mater.

2024

DOI: 10.1007/s40843-024-2868-x

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Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm

Fan Yang,

Guanjian Cheng,

Wan-Jian Yin

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Cited by 2 publications

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Editorial: special topic on computation-assisted materials screening and design

Wang,

Sun,

Dong

2024

Sci. China Mater.

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Editorial: special topic on computation-assisted materials screening and design

Wang,

Sun,

Dong

2024

Sci. China Mater.

View full text Add to dashboard Cite

Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis

Cheng,

Wu,

et al. 2024

Precision Chemistry

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This Perspective explores the integration of machine learning potentials (MLPs) in the research of heterogeneous catalysis, focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes. MLPs utilize extensive databases from high-throughput density functional theory (DFT) calculations to train models that predict atomic configurations, energies, and forces with near-DFT accuracy. These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods. Coupled with global optimization algorithms, MLPs enable systematic investigations across vast structural spaces, making substantial contributions to the modeling of catalyst surface structures under reactive conditions. The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis. The prevailing challenges faced by this approach are also discussed.

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Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm

Cited by 2 publications

References 48 publications

Editorial: special topic on computation-assisted materials screening and design

Editorial: special topic on computation-assisted materials screening and design

Leveraging Machine Learning Potentials for In-Situ Searching of Active sites in Heterogeneous Catalysis

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