Research

BAT: High-throughput computational methods for BATtery materials discovery

In the BAT research line, we generate virtual libraries of candidate compounds for energy storage, and perform multi-scale calculations on them within a high-throughput computational framework using a down-selection funnel approach. To facilitate the economic development of electricity storage technologies at various scales, a key step will be the discovery of functional electroactive compounds for batteries. We develop and use novel in silico material design frameworks for rapid exploration of a great diversity of completely new compounds, particularly for aqueous and non-aqueous redox flow batteries, via different subprojects. The aim of our group in the BAT research line is to fully automate the workflows and procedures to accelerate the development of new electroactive materials for redox flow batteries.

CAT: High-throughput computational methods for electroCATalytic materials discovery

In the CAT research line, we generate virtual libraries of candidate (nano-)materials, and carry out high-throughput density functional theory (HT-DFT) calculations to predict their electrocatalytic behaviors during conversion reactions of chemical feedstock into useful chemical products. Effective new catalysts are needed to breakdown the chemically inert and stable feedstock molecules, such as CO2 and N2, and to drive the electrocatalytic processes of these molecules. To improve our understanding of new candidate catalysts and to accelerate their computational exploration, our HT-DFT data-driven approach integrates a broad collection of data from our own computations on composition-structure-activity relationships of candidate materials and integrates these into a data-driven regression model. The aim of our group in the CAT research line is to open up new design avenues for new and better catalysts.

DAT: DATa science methods for functional energy materials discovery

In the DAT research line, we develop and use different data science methods to extract relationships between the key features of materials. By revealing these structure-property relationships, we work on predicting the properties of materials using only a tiny fraction of time and computational cost in comparison to the state-of-the-art experiments and computations. We develop a continuously growing database of functional materials, and their calculated properties, for advanced energy conversion and storage applications. We apply different machine learning methods to understand the fundamentals of materials and to expand the borders of chemical space of interest to beyond that of the reach of modern computation. The aim of our group in the DAT research line is to inverse design completely new energy materials, with a predefined set of desired properties and functionalities.