氢气储存
石墨烯
密度泛函理论
材料科学
氢
概化理论
吸附
储能
热力学
纳米技术
化学
计算化学
物理化学
数学
物理
有机化学
功率(物理)
统计
作者
Zepeng Jia,Sen Lu,Pei Song,Tiren Peng,Zi Gao,Zhiguo Wang,Qing Jiang,Xue Bai,Hong Cui,Weizhi Tian,Rong Feng,Qin Kang,Zhiyong Liang,Hongkuan Yuan
标识
DOI:10.1016/j.seppur.2024.128229
摘要
To accelerate the exploration of modified graphene hydrogen storage materials, this paper proposes a Data-Driven Exploration Framework (DDEF) based on the best-fitting machine learning (ML) algorithms (Random Forest: Recall = 0.83, Accuracy = 0.83, Gradient boosted regression: R2 = 0.90, RMSE = 0.06) and density functional theory (DFT). The hydrogen storage capacity of bilayer double-deficient graphene (BDG[Li]) doped with B atoms and modified with Li and Ti atoms was predicted using ML, which shortens the design cycle of hydrogen storage materials. The interlayer composite hydrogen storage mechanism of the BDG[Li] structure is revealed by electronic property analysis. For the first time, the calculation method of the interlayer electrostatic force energy (Ej) is proposed, and the equation for calculating the interlayer H2 adsorption energy is optimized. The hydrogen storage data of BDG[Li] and the construction of Ej provide new feature data for subsequent ML calculations. Both DDEF and Ej are computationally verified to have high accuracy and generalizability, which is of research significance for accelerating the design of hydrogen storage materials.
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