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

TL;DR

This paper introduces machine learning potentials to efficiently simulate water-Lithium manganese oxide interfaces, revealing atomistic and electronic insights relevant for battery and catalytic applications.

Contribution

It develops neural network potentials for accurate, large-scale simulations of solid-liquid interfaces, enabling detailed analysis of electronic and structural properties.

Findings

Water dissociation and proton transfer mechanisms identified

Manganese oxidation states and Jahn-Teller distortions characterized

Electronic structure insights into electron hopping processes

Abstract

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 (Li$_x$Mn$_2$O$_4$), an important electrode material in lithium ion batteries and a catalyst for the oxygen evolution reaction. We employ a high-dimensional neural network potential (HDNNP) 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 (HDNNS) 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 like 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.