Research Vision
Uncertainty over the future security of our energy is a major concern with a rising global population, increasing energy demands, and impending climate change. Discovery and consumption of massive quantities of carbon-based energy sources such as fossil fuels (coal, oil, natural gas) have contributed to our modern high standard of living, but, unfortunately at the expense of the climate due to CO₂ emissions. However, a drastic change away from a carbon-based society is not expected in the near future.
Therefore, to overcome the aforementioned challenges;
(1) in the short-term: a constant strive for higher yields, increased selectivity, and improved energy efficiency for existing processes in the chemical industry, as well as the development of innovative new selective processes
(2) in the long-term: a transition to a society based on CO₂-neutral sustainable energy resources (solar, wind, and hydroelectric) is essential for the sustainability of modern civilization.
This will require immense scientific and technological developments in processes that transform low carbon feedstocks (CO₂ and CH₄) into fuels and chemicals, and renewable energy capture, storage, and conversion devices. These processes and devices rely strongly on catalytic materials in order to attain the required performance involved in their operation. However, today’s catalytic materials are inadequate. Therefore, the grand challenge is to design and discover advanced catalytic materials that satisfy activity, selectivity, and stability, and are based on Earth-abundant and non-critical elements to achieve a sustainable energy future.
Research Approach
The overarching goal of my research is to develop innovative strategies to produce renewable energy, fuels and chemicals by the computational design of efficient thermo- and electro-catalytic processes. To achieve this, I combine my expertise in the fields of computational catalysis, kinetic modeling, and machine learning, with a special focus on underpinning structure-property relationships of advanced catalytic materials to accelerate materials discovery and establish catalyst design principles.
(1) Operando computational catalysis: active site, coverage, reaction path analysis
1.1 Thermal CO₂ reduction to value-added products
1.2 Mechanistic insights and catalyst design strategies for tandem reactions of CO₂ reduction and ethane dehydrogenation
1.3 Nanoengineering stable multifunctional catalysts
(2) Machine Learning approaches to accelerate next-generation advanced catalytic materials discovery and gain new chemical insights
2.1 High–throughput (HT) calculations in heterogeneous catalysis
2.2 Efficiently identify complex and multi-dimensional descriptors
2.3 Accelerated catalyst discovery by calculating catalyst properties using graph representations