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Challenges and opportunities in using Kinetic Monte Carlo for battery research and innovation

Year: 2025

Type: article

Abstract: 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.

Source: EES batteries.

DOI: 10.1039/d5eb00070j

Authors: Mohammed Bin Jassar, Theodorus De Bruin, Carlos Nieto-Draghi, Stephan N. Steinmann

Institutions: Centre National de la Recherche Scientifique, École Normale Supérieure - PSL, Sorbonne Université, Chimie Physique et Chimie du Vivant, IFP Énergies nouvelles, École Normale Supérieure de Lyon, Laboratoire de Chimie

Language: English

Topic: Advancements in Battery Materials

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: diamond

OpenAlex ID: W4411435235

FWCI: 0.924

Cites: 104

Cited by: 4

Related to: 10