Systematically Improvable Modeling of Electrochemical Processes

This Focus Group consists of Hans Fischer Senior Fellow Prof. Heather J. Kulik (Massachusetts Institute of Technology) and her hosts Prof. Christopher J. Stein (TUM School of Natural Sciences) and Prof. Jennifer L. M. Rupp (TUM School of Natural Sciences).

This Focus Group seeks to model electrochemical processes in batteries and electrocatalysts. To overcome present limitations, we aim to develop systematic approaches to bring high accuracy to low-cost methods and develop techniques to detect when lower cost methods are insufficient. We are working to develop a machine learning (ML) framework for fitting low-cost tight binding parameters to higher accuracy density functional theory (DFT) and wavefunction theory methodology, devise schemes for systematic beyond-DFT embedding, and build ML models techniques to detect when low-level theories will fail. In doing so, we will address length and timescales in electrochemical modeling that have not been possible before, providing new insight into batteries and electrocatalysts to optimize next-generation aqueous, solid state, and hybrid batteries by limiting the catalytic activity of electrode materials or to alternately optimize electrocatalysts for oxygen evolution for fuel cells. This work is expected to provide both fundamental insights into electrochemical phenomena and provide opportunities for technological advances via novel design principles for battery technology

TUM-IAS funded doctoral candidate:
Tatiana Nikolaeva, Theoretical Chemistry, TUM