Work

FusionStackBoost: A machine learning approach for accurate prediction of hydrogen storage performance in metal organic frameworks

Year: 2025

Type: article

Source: International Journal of Hydrogen Energy

DOI: 10.1016/j.ijhydene.2025.04.506

Authors: Yuling Luo, Li Xie, Dan Cai, Lixian Sun, Fen Xu, Hao Feng, Haibin Jia

Institutions: Guilin University of Electronic Technology, Guilin Institute of Information Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: closed

Funders: Guangxi Key Laboratory of Information Materials, National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, Guilin University of Electronic Technology, Scientific Research and Technology Development Program of Guangxi, Science and Technology Department of Guangxi Zhuang Autonomous, National Key Research and Development Program of China

Awards: G2209040623, G2289064930, G2530267853, G275959984, G3798210512, G4649030836, G5211140260, G5440420482, G620206433, G7886473108, G8754288123, G8829505570

OpenAlex ID: W4412452658

FWCI: 1.498

Cites: 41

Cited by: 8

Related to: 10