Metal hydride composition-derived parameters as machine learning features for material design and H2 storage

Sean Nations; Tarak Nandi; Ali Ramazani; Shengnian Wang; Yuhua Duan

doi:10.1016/j.est.2023.107980

Metal hydride composition-derived parameters as machine learning features for material design and H₂ storage

Sean Nations
, Tarak Nandi
, Ali Ramazani
, Shengnian Wang
, Yuhua Duan

National Energy Technology Laboratory, Pittsburgh
Institute for Micromanufacturing

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	107980
Journal	Journal of Energy Storage
Volume	70
DOIs	https://doi.org/10.1016/j.est.2023.107980
State	Published - Oct 15 2023
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

ASJC Scopus Subject Areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Electrical and Electronic Engineering

Keywords

Big data screening
Data science
Decision trees
Hydrogen storage
Machine learning
Materials informatics
Metal hydrides database

Access to Document

10.1016/j.est.2023.107980

Cite this

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title = "Metal hydride composition-derived parameters as machine learning features for material design and H2 storage",

keywords = "Big data screening, Data science, Decision trees, Hydrogen storage, Machine learning, Materials informatics, Metal hydrides database",

author = "Sean Nations and Tarak Nandi and Ali Ramazani and Shengnian Wang and Yuhua Duan",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

year = "2023",

month = oct,

day = "15",

doi = "10.1016/j.est.2023.107980",

language = "English",

volume = "70",

journal = "Journal of Energy Storage",

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T1 - Metal hydride composition-derived parameters as machine learning features for material design and H2 storage

AU - Nations, Sean

AU - Nandi, Tarak

AU - Ramazani, Ali

AU - Wang, Shengnian

AU - Duan, Yuhua

PY - 2023/10/15

Y1 - 2023/10/15

KW - Big data screening

KW - Data science

KW - Decision trees

KW - Hydrogen storage

KW - Machine learning

KW - Materials informatics

KW - Metal hydrides database

UR - https://www.scopus.com/pages/publications/85163405247

UR - https://www.scopus.com/pages/publications/85163405247#tab=citedBy

U2 - 10.1016/j.est.2023.107980

DO - 10.1016/j.est.2023.107980

M3 - Article

AN - SCOPUS:85163405247

SN - 2352-152X

VL - 70

JO - Journal of Energy Storage

JF - Journal of Energy Storage

M1 - 107980

ER -