Amir Omranpour

Is an important catalyst for the oxidation of organic molecules in the liquid phase. Still, understanding the atomistic details of –water interfaces under operando conditions remains extremely challenging. While ab initio molecular dynamics have become an essential tool for investigating these dynamic interfaces in silico, they are limited to only a few picoseconds and a few hundred atoms. In this work, we overcome these limitations by training a high‐dimensional neural network potential (HDNNP) on density functional theory data, which allows us to significantly extend the accessible time and length scales. Employing this HDNNP, we perform simulations to unravel the structure, dynamics, and reactivity of (001)–water interfaces in detail. Our simulations reveal distinct characteristics of the two possible A and B terminations. The B‐terminated surface stabilizes a compact, quasi‐epitaxial hydration layer with strong templating effects, enhanced hydroxylation, and a well‐organized hydrogen‐bond network. In contrast, the A‐termination forms a more diffuse contact layer with weaker templating, lower hydroxylation, and less ordered interfacial water. Extended simulations further uncover proton transfer pathways, including intermittent protonation of surface hydroxyls, migration of water molecules into the epitaxial layer, and rare hydronium‐like configurations.

The production of many bulk chemicals relies on heterogeneous catalysis. The rational design or improvement of the required catalysts critically depends on insights into the underlying mechanisms on the atomic scale. In recent years, substantial progress has been made in applying advanced experimental techniques to complex catalytic reactions in operando, but in order to achieve a comprehensive understanding, additional information from computer simulations is indispensable in many cases. In particular, ab initio molecular dynamics (AIMD) has become an important tool to explicitly address the atomistic level structure, dynamics, and reactivity of interfacial systems, but the high computational costs limit applications to systems consisting of at most a few hundred atoms for simulation times of up to tens of picoseconds. Rapid advances in the development of modern machine learning potentials (MLP) now offer a promising approach to bridge this gap, enabling simulations of complex catalytic reactions with ab initio accuracy at a small fraction of the computational costs. In this Perspective, we provide an overview of the current state of the art of applying MLPs to systems relevant for heterogeneous catalysis along with a discussion of the prospects for the use of MLPs in catalysis science in the years to come.

The Co₃O₄ spinel is an important material in oxidation catalysis. Its properties under catalytic conditions, i.e. at finite temperatures, can be studied by molecular dynamics simulations, which critically depend on an accurate description of the atomic interactions. Due to the high complexity of Co₃O₄, which is related to the presence of multiple oxidation states of the cobalt ions, to date ab initio methods have been essentially the only way to reliably capture the underlying potential energy surface, while more efficient atomistic potentials are very challenging to construct. Consequently, the accessible length and time scales of computer simulations of systems containing Co₃O₄ are still severely limited. Rapid advances in the development of modern machine learning potentials (MLPs) trained on electronic structure data now make it possible to bridge this gap. In this work, we employ a high-dimensional neural network potential (HDNNP) to construct a MLP for bulk Co₃O₄ spinel based on density functional theory calculations. After a careful validation of the potential, we compute various structural, vibrational, and dynamical properties of the Co₃O₄ spinel with a particular focus on its temperature-dependent behavior, including the thermal expansion coefficient.

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing us to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective, we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid–liquid interfaces.

The interaction of 2-propanol with Co3O4(001) was studied by vibrational sum frequency spectroscopy and ab initio molecular dynamics simulations of 2-propanol dissolved in a water film to gain an insight, at the molecular level, into the pathways of catalytic oxidation. The experimental study has been performed under near ambient conditions, where the presence of water vapor is unavoidable, resulting in a water film on the sample and, thereby, allowing us to mimic the solution–water interface. Both experiment and theory conclude that 2-propanol adsorbs molecularly. The lack of dissociation is attributed to the adsorption geometry of 2-propanol in which the O–H bond does not point toward the surface. Furthermore, the copresent water not only competitively adsorbs on the surface but also inhibits 2-propanol deprotonation. The calculations reveal that the presence of water deactivates the lattice oxygen, thereby reducing the surface activity. This finding sheds light on the multifaceted role of water at the interface for the electrochemical oxidation of 2-propanol in aqueous solution as recently reported [Falk et al., ChemCatChem 13, 2942–2951 (2021)]. At higher temperatures, 2-propanol remains molecularly adsorbed on Co3O4(001) until it desorbs with increasing surface temperature.