2D High-Entropy Materials for Energy Conversion

Modern energy systems are highly dependent on materials with optimized properties for specific applications, such as for charge storage in batteries, selective ion transport in electrochemical cells, or conversion of light to charge carriers in photovoltaic systems. Recent advances in both computer power and computational methods enable the design of materials with targeted functional properties through high-throughput modelling combined with machine learning. By developing such an approach, we may introduce important factors such as structural stability and energy levels already at the computational stage. In this project, we will develop a computational framework and apply it to a relatively new class of materials, so-called high-entropy materials (HEMs), where a large number of atomic elements (typically 5 or more) are combined into a stable structure. The emphasis will be on layered structures that are promising for tuneable catalytic reactions, specifically water electrolysis and hydrogen generation. The project combines advanced computational and experimental tools, along with international collaboration, in order to gain fundamental understanding of the interplay between composition, structure, and properties in these new materials. The aim is to develop a methodology for predicting stable functional HEMs and to demonstrate water electrolysis with a novel HEM synthesized in the project.

Due to recent advances in computational methods, materials with desired functional properties can now be designed through high-throughput ab initio methods and atomistic modeling combined with machine learning, introducing already at the computational stage critical factors such as structural stability and energy band structure. This emerging methodology has great promises, as it bypasses the traditional trial-and-error approach, but it is critically dependent on feedback from experiments (ideally in-operando data). In this project this approach is used on a relatively new class of materials, so-called high-entropy materials (HEMs) where a large number of elements (5 or more, including non-metals) are combined to a stable uniform structure. In particular, the focus is on 2D HEMs, that has recently been found to have a range of unusual properties, making them highly promising for tunable catalytic reactions, such as water splitting and H2-generation. The project uses an interdisciplinary approach with international collaboration, combining state-of-the-art computational and experimental tools to gain predictive power and fundamental understanding of composition-structure-property relationships in these high-entropy materials, with the aim of developing a generic methodology for predicting stable functional HEMs and finally demonstrating beyond state-of-the-art performance for water splitting with a novel 2D HEM.