Summary
It is our aim to transform the research field of electrocatalysis from the established “initial (as-synthesized) state approach” to a data-centric understanding of the metastable active interface of electrocatalysts, constantly evolving under reaction conditions. We want to overcome the limitations of elemental or binary alloy catalysts by exploring and exploiting high entropy materials (HEM) as a discovery platform for sustainable materials, with the aim to identify in the extremely large, multidimensional search space new electrocatalysts that are both stable and active.
To understand and control the active interface of HEM electrocatalysts, we combine the core expertises of the PIs: theoretical modelling and simulations, high-throughput synthesis and characterization, nanoparticle synthesis, electrochemical operando techniques as well as machine learning. Our synergistic approach will significantly advance these individual competences by key conceptual innovations: (i) Evolutionary screening of micro-libraries to efficiently identify stable materials covering the complete HEM composition space; (ii) Accelerated atomic-scale characterization of HEM surfaces by combining combinatorial HEM synthesis with atom probe tomography; (iii) High-throughput operando experiments with thin film material libraries; (iv) Developing inverse activity-structure relationships and theoretical descriptors for metastability; v) Implementing active learning approaches based on materials informatics and using a semantic data lake.
We will establish a theory of metastability as a core concept for the understanding of electrocatalysis for the most important energy conversion reactions: oxygen reduction and evolution, and CO2 reduction. Instead of passively accepting the degradation of catalysts during operation, we will direct the evolution through the highly multidimensional space towards long-lasting, active HEM interfaces.
To understand and control the active interface of HEM electrocatalysts, we combine the core expertises of the PIs: theoretical modelling and simulations, high-throughput synthesis and characterization, nanoparticle synthesis, electrochemical operando techniques as well as machine learning. Our synergistic approach will significantly advance these individual competences by key conceptual innovations: (i) Evolutionary screening of micro-libraries to efficiently identify stable materials covering the complete HEM composition space; (ii) Accelerated atomic-scale characterization of HEM surfaces by combining combinatorial HEM synthesis with atom probe tomography; (iii) High-throughput operando experiments with thin film material libraries; (iv) Developing inverse activity-structure relationships and theoretical descriptors for metastability; v) Implementing active learning approaches based on materials informatics and using a semantic data lake.
We will establish a theory of metastability as a core concept for the understanding of electrocatalysis for the most important energy conversion reactions: oxygen reduction and evolution, and CO2 reduction. Instead of passively accepting the degradation of catalysts during operation, we will direct the evolution through the highly multidimensional space towards long-lasting, active HEM interfaces.
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| Web resources: | https://cordis.europa.eu/project/id/101118768 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2029 |
| Total budget - Public funding: | 9 973 679,00 Euro - 9 973 679,00 Euro |
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Original description
It is our aim to transform the research field of electrocatalysis from the established “initial (as-synthesized) state approach” to a data-centric understanding of the metastable active interface of electrocatalysts, constantly evolving under reaction conditions. We want to overcome the limitations of elemental or binary alloy catalysts by exploring and exploiting high entropy materials (HEM) as a discovery platform for sustainable materials, with the aim to identify in the extremely large, multidimensional search space new electrocatalysts that are both stable and active.To understand and control the active interface of HEM electrocatalysts, we combine the core expertises of the PIs: theoretical modelling and simulations, high-throughput synthesis and characterization, nanoparticle synthesis, electrochemical operando techniques as well as machine learning. Our synergistic approach will significantly advance these individual competences by key conceptual innovations: (i) Evolutionary screening of micro-libraries to efficiently identify stable materials covering the complete HEM composition space; (ii) Accelerated atomic-scale characterization of HEM surfaces by combining combinatorial HEM synthesis with atom probe tomography; (iii) High-throughput operando experiments with thin film material libraries; (iv) Developing inverse activity-structure relationships and theoretical descriptors for metastability; v) Implementing active learning approaches based on materials informatics and using a semantic data lake.
We will establish a theory of metastability as a core concept for the understanding of electrocatalysis for the most important energy conversion reactions: oxygen reduction and evolution, and CO2 reduction. Instead of passively accepting the degradation of catalysts during operation, we will direct the evolution through the highly multidimensional space towards long-lasting, active HEM interfaces.
Status
SIGNEDCall topic
ERC-2023-SyGUpdate Date
12-03-2024
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