DOI: 10.1039/c9ta02356a. Machine learning for renewable energy materials

Machine learning for renewable energy materials

10.1039/c9ta02356a

Crossref journal-article

Royal Society of Chemistry (RSC)

Journal of Materials Chemistry A (292)

Abstract

Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies. Machine learning has demonstrated many successes to accelerate the discovery renewable energy materials.

Bibliography

Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019). Machine learning for renewable energy materials. Journal of Materials Chemistry A, 7(29), 17096â17117.

Authors 4

Geun Ho Gu (first)
Juhwan Noh (additional)
Inkyung Kim (additional)
Yousung Jung (additional)

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Dates

Type	When
Created	6 years, 3 months ago (April 30, 2019, 6:06 a.m.)
Deposited	1 year, 1 month ago (July 17, 2024, 8:51 a.m.)
Indexed	6 hours, 11 minutes ago (Aug. 23, 2025, 9:49 p.m.)
Issued	6 years, 7 months ago (Jan. 1, 2019)
Published	6 years, 7 months ago (Jan. 1, 2019)
Published Online	6 years, 7 months ago (Jan. 1, 2019)

Funders 2

KAIST 10.13039/501100007107 Korea Advanced Institute of Science and Technology
Region: Asia
pri (Universities (academic only))
Labels2
1. 한국과학기술원
2. KAIST
National Research Foundation of Korea 10.13039/501100003725
Region: Asia
pri (Trusts, charities, foundations (both public and private))
Labels3
1. 한국연구재단이 창의적 연구와
2. National Research Foundation (South Korea)
3. NRF
Awards1
1. 2018M3D1A1089310

BibTeX

@article{Gu_2019, title={Machine learning for renewable energy materials}, volume={7}, ISSN={2050-7496}, url={http://dx.doi.org/10.1039/c9ta02356a}, DOI={10.1039/c9ta02356a}, number={29}, journal={Journal of Materials Chemistry A}, publisher={Royal Society of Chemistry (RSC)}, author={Gu, Geun Ho and Noh, Juhwan and Kim, Inkyung and Jung, Yousung}, year={2019}, pages={17096–17117} }

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