Talented chemist chosen for data science fellowship researching batteries

Talented chemist chosen for data science fellowship researching batteries

Rasmus Christensen, a research assistant at Department of Chemistry and Bioscience AAU, has been awarded a PhD fellowship as one of the first from the prestigious Danish Data Science Academy. His goal is to help build better batteries using new disordered electrode materials such as glass.

Last modified: 27.06.2022

Safer and more efficient batteries than the standard Li-ion batteries used around the world in any industry. This is one of the end goals of young researcher at Aalborg University Rasmus Christensen.

Monday he was awarded one of the 10 very first fellowships from the prestigious Danish Data Science Academy (DDSA). He received the grant at Statens Museum for Kunst with an audience of 250 guests.

As a PhD fellow at DDSA, he will become a pivotal part of the Danish data science community. DDSA plans to create events that will unite and grow the Danish data science community and encourage the research talents and their PhD advisors to share knowledge within the group of fellows.

Improving batteries

In Christensen’s project he will use different simulation techniques coupled with data science methods such as topological data analysis and machine learning to facilitate the design of disordered electrode materials with improved performance.

Foto: Rasmus Christensen at Statens Museum for Kunst

Rasmus Christensens research on batteries

  • Safe and efficient batteries are one of the key technologies for electrification of transport and sustainable energy storage and thus enabling the green transition.
  • Li-ion batteries are by far the most studied and commercially successful battery type. The electrodes in these batteries have traditionally been crystalline materials, but new research suggests that improvements can also be achieved by using electrode materials with different types of disorder.
  • In this project, different data science methods such as topological data analysis and machine learning will be used to facilitate the design of disordered electrode materials with improved performance. Taken as a whole, the project will find “order in disorder” in a promising new family of electrode materials, which in turn will enable future development of novel batteries.

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