First principles data-driven potentials for prediction of iron carbide clusters

Many have reported the use of quantum chemistry approaches for evaluating the catalytic properties of iron carbide clusters. Unfortunately, structural energy calculations are computationally expensive when using density functional theory. The computational cost is prohibitive for high-throughput sim...

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Principais autores:	Enhu Diao, Yurong He, Xuhong Liu, Qiang Tong, Tao Yang, Xiaotong Liu, James P. Lewis
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Frontiers Media S.A. 2023-05-01
סדרה:	Frontiers in Quantum Science and Technology
נושאים:	iron carbide clusters machine learning potentials adsorbate prediction cluster optimization high-throughput data analysis
גישה מקוונת:	https://www.frontiersin.org/articles/10.3389/frqst.2023.1190522/full
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