Machine learning for renewable energy materials

Cited 6 time in webofscience Cited 0 time in scopus
  • Hit : 53
  • Download : 0
Achieving the 2016 Paris agreement goal of limiting global warming below 2 degrees C and securing a sustainable energy future require materials innovations in renewable energy technologies. While the window of opportunity is closing, meeting these goals necessitates deploying new research concepts and strategies to accelerate materials discovery by an order of magnitude. Recent advancements in machine learning have provided the science and engineering community with a flexible and rapid prediction framework, showing a tremendous potential impact. Here we summarize the recent progress in machine learning approaches for developing renewable energy materials. We demonstrate applications of machine learning methods for theoretical approaches in key renewable energy technologies including catalysis, batteries, solar cells, and crystal discovery. We also analyze notable applications resulting in significant real discoveries and discuss critical gaps to further accelerate materials discovery.
Publisher
ROYAL SOC CHEMISTRY
Issue Date
2019-08
Language
English
Article Type
Review
Citation

JOURNAL OF MATERIALS CHEMISTRY A, v.7, no.29, pp.17096 - 17117

ISSN
2050-7488
DOI
10.1039/c9ta02356a
URI
http://hdl.handle.net/10203/264904
Appears in Collection
EEW-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 6 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0