Machine learning-guided discovery of thermodynamically stable single-atom catalysts on functionalized MXenes for enhanced oxygen reduction and evolution reactions
DFT and machine learning are combined to screen transition-metal-doped MXene (Ti 3 C 2 T 2 ) catalysts for oxygen electrocatalysis. Ni–Ti 3 C 2 S 2 and Cu–Ti 3 C 2 S 2 show low ORR/OER overpotentials, highlighting the power of ML-accelerated catalyst design.
Authors
- Seung Geol Lee (ORCID: https://orcid.org/0000-0001-7965-7387)
- Hengquan Guo (ORCID: https://orcid.org/0000-0003-4394-9710)
Institutions
- Ulsan National Institute of Science and Technology (KR)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta02929e
- Citations
- 7
- Primary Topic
- MXene and MAX Phase Materials
- Type
- article
- Field-Weighted Citation Impact
- 3.62
Funders
- Ulsan National Institute of Science and Technology
- Ministry of Science, ICT and Future Planning