The aim of our lab is rational design and discovery of superior material systems that will help improve resource sustainability. We focus on multinary materials, for example high entropy materials, as catalysts for processes that help in realizing a sustainable future. To that end, we use machine learning- guided combinatorial materials science and high-throughput techniques. The designed materials are investigated for their ability to solve issues in catalysts for sustainable resources and materials for energy applications.
Our lab researches fundamental questions such as: How do composition and structure effect the functionality of the multinary materials as sustainable catalysts? How can rational design lead to improved material properties? How is (chemical) stability affected? And much more.