  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.
