Challenges and opportunities in using Kinetic Monte Carlo for battery research and innovation
Kinetic Monte Carlo is already exploited to understand and improve batteries via molecular-level insights, but would be even more reliable with improved algorithms for time-scale disparities, more flexible lattices and more accurate time-constants.
Authors
- Theodorus de Bruin (ORCID: https://orcid.org/0000-0001-6997-1786)
- Mohammed Bin Jassar (ORCID: https://orcid.org/0000-0002-1246-0819)
- Stephan N. Steinmann (ORCID: https://orcid.org/0000-0002-2777-356X)
- Carlos Nieto‐Draghi (ORCID: https://orcid.org/0000-0001-5956-9259)
Institutions
- École Normale Supérieure de Lyon (FR)
- Centre National de la Recherche Scientifique (FR)
- IFP Énergies nouvelles (FR)
- Université Paris Sciences et Lettres (FR)
- École Normale Supérieure - PSL (FR)
- Sorbonne Université (FR)
- École Normale Supérieure (BI)
- Laboratoire de Chimie (FR)
Publication Details
- Journal
- EES batteries.
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5eb00070j
- Primary Topic
- Advanced Battery Technologies Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00