Machine Learning Accelerated Materials Discovery for Energy Conversion and Storage

 

Machine Learning Accelerated Materials Discovery for Energy Conversion and Storage

 

Date: 19^th March, 2026 (Thursday)

Time: 11 am - 12 pm (HKT)

 

Venue: Tam Wing Fan Innovation Wing Two, G/F Run Run Shaw Building

 

 

Theory Department

Fritz- Haber-Institut der Max-Planck-Gesellschaft

 

Prof. Karsten Reuter is Director of the Theory Department of the Fritz Haber Institute (FHI) of the Max Planck Society in Berlin, Germany. He specifically works on multiscale models that combine predictive-quality first-principles techniques with coarse-grained methodologies and machine learning to achieve microscopic insight into the processes in working catalysts and energy conversion devices. Karsten did his doctoral studies on theoretical surface physics in Erlangen, Madrid and Milwaukee. Following research experiences at the FHI in Berlin and the FOM Institute in Amsterdam, he headed an independent Max Planck Research Group. From 2009 to 2020 he was Chair for Theoretical Chemistry at the Technical University of Munich (TUM). He held visiting professorships at Stanford (2014), MIT (2018), and Imperial College London (2019), and is a Distinguished Affiliated Professor at TUM as well as Honorary Professor at the Free University and Humboldt University in Berlin. He is an ELLIS fellow, and serves on multiple advisory and supervisory boards, including the Helmholtz Center Berlin and the German Physical Society.

 

More performant and durable materials are urgently needed to further drive the transition to a sustainable energy system. Unfortunately, accelerated materials discovery is in this field presently still more claim than practical reality. Computational screening approaches hinge on efficient descriptors that only reflect nominal materials properties of the crystalline bulk, simple bulk-truncated surfaces or idealized lattice-matching interfaces. They can thus not account for the substantial, complex and continuous structural, compositional and morphological transitions at the working surfaces or interfaces of functional materials in catalysts, electrolyzers or batteries. Accelerated experimental discovery in turn still suffers from severe throughput limitations, as easily automatable human steps are rarely limiting the overall workflows. In my talk, I will survey this field and discuss our recent computational and experimental endeavors to overcome the named bottlenecks.

 

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