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A Helicopter View on Electrocatalysis

Project description

Understanding catalytic reactions through advanced simulations in realistic environments

Converting CO2 into valuable chemicals is vital for a circular carbon economy and can be achieved through the electrochemical CO2 reduction reaction (CO2RR). However, no catalyst meets the necessary industrial standards for selectivity and stability. While theoretical models have helped in catalyst development, they often ignore the varied structures existing under real working conditions. The ERC-funded HeliECat project will enhance our understanding of catalytic processes, including side reactions and degradation, by modelling the changing surface structure employing realistic operando conditions. HeliECat will combine semi-empirical models with accurate kinetic studies and use deep reinforcement learning to connect independently explored surfaces. Focusing on CO2 reduction on a Cu/Au catalyst, it emphasises the importance of structural diversity for accurate simulations and effective in silico catalyst screening.

Objective

Converting carbon dioxide, an unwanted by-product of industrial processes and combustion engines, into valuable chemical compounds is key for a cyclic carbon economy and can be achieved by the electrochemical CO2 reduction reaction (CO2RR). Despite the immense importance of this reaction in the fight against climate change, no catalyst to date meets the industrial requirements for product selectivity and catalyst stability.

Theoretical models and simulations have played a fundamental role in the understanding of these and similar catalytic processes and aided the development of more efficient catalysts for this reaction. These models, however, assume few often idealized surface structures, ignoring the fact that a variety of structures is present under working conditions that define the catalyst properties in terms of efficiency, selectivity, and stability.

With HeliECat, I will for the first time establish an approach that offers a holistic view of the catalytic processes, including side reactions and degradation mechanisms. To achieve this ambitious goal, I will combine an efficient semiempirical model for structural exploration with accurate embedded cluster models for kinetic studies. This combination shifts the focus from idealized structures to the diversity of the catalyst surface under realistic conditions. I will then model the interconversion of accessible surface structures with deep reinforcement learning techniques. The analysis of the emerging connected kinetic reaction networks will ultimately allow me to simultaneously study the catalytic activity, stability, and selectivity in silico.

I will study the CO2RR on a Cu/Au catalyst surface and demonstrate that automated kinetic modeling, considering structural diversity and realistic thermodynamic boundary conditions, is the key to a predictive simulation of catalyst behavior under actual working conditions. HeliECat will hence pave the way for realistic in silico catalyst screening.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

(opens in new window) ERC-2025-STG

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Host institution

TECHNISCHE UNIVERSITAET MUENCHEN
Net EU contribution
€ 1 486 906,00
Address
Arcisstrasse 21
80333 Muenchen
Germany
Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost
€ 1 486 906,00

Beneficiaries (1)