Insights into lithium manganese oxide–water interfaces using machine learning potentials

2021 | journal article; research paper. A publication with affiliation to the University of Göttingen.

Jump to: Cite & Linked | Documents & Media | Details | Version history

Cite this publication

​Eckhoff, Marco, and Jörg Behler. "Insights into lithium manganese oxide–water interfaces using machine learning potentials​." ​The Journal of Chemical Physics, vol. 155, no. 24, ​2021, p. 244703​, ​doi: 10.1063/5.0073449. 

Documents & Media


GRO License GRO License


Eckhoff, Marco; Behler, Jörg
Unraveling the atomistic and the electronic structure of solid-liquid interfaces is the key to the design of new materials for many important applications, from heterogeneous catalysis to battery technology. Density functional theory (DFT) calculations can, in principle, provide a reliable description of such interfaces, but the high computational costs severely restrict the accessible time and length scales. Here, we report machine learning-driven simulations of various interfaces between water and lithium manganese oxide (LixMn2O4), an important electrode material in lithium ion batteries and a catalyst for the oxygen evolution reaction. We employ a high-dimensional neural network potential to compute the energies and forces several orders of magnitude faster than DFT without loss in accuracy. In addition, a high-dimensional neural network for spin prediction is utilized to analyze the electronic structure of the manganese ions. Combining these methods, a series of interfaces is investigated by large-scale molecular dynamics. The simulations allow us to gain insights into a variety of properties, such as the dissociation of water molecules, proton transfer processes, and hydrogen bonds, as well as the geometric and electronic structure of the solid surfaces, including the manganese oxidation state distribution, Jahn-Teller distortions, and electron hopping.
Issue Date
The Journal of Chemical Physics 
SFB 1073 | Topical Area C | C03 Vom Elektronentransfer zur chemischen Energiespeicherung: ab-initio Untersuchungen korrelierter Prozesse 
SFB 1073 | Topical Area C: Photonen- und elektronengetriebene Reaktionen 
SFB 1073: Kontrolle von Energiewandlung auf atomaren Skalen 



Social Media