High throughput computational and experimental methods for accelerated electrochemical materials discovery
The full integration of sustainable technologies to combat climate change relies on discovering cost-competitive, safe, and durable materials, specifically for electrochemical systems that generate energy, store energy, and produce chemicals.
Authors
- Jeff Wu
- Kunran Yang (ORCID: https://orcid.org/0000-0002-7777-5547)
- Stuart Miller (ORCID: https://orcid.org/0000-0003-1264-5888)
- Jorge Ernesto González Mesa (ORCID: https://orcid.org/0000-0001-7221-4604)
- Uzoma Nwabara
- Akshay Talekar
- Varinia Bernales
Institutions
- American Institutes for Research (US)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta00331h
- Citations
- 11
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 5.70
Funders
- Urmia Lake Research Institute, Urmia University