Summary
The aim of project CAMELLIA (ComputAtional Mapping of ELectrocataLytic InterfAces) is to derive fundamental insights, predict and design electrocatalytic properties (activity, selectivity, stability) of nanoparticles with realistic sizes, under operando conditions. To achieve this goal, a computational approach, based on electronic structure methods – Density Functional Theory calculations – in conjunction with newly developed Artificial Neural Network-trained interatomic potentials, is used. Coverage- , solvent-, and potential-cognizant static and molecular dynamics simulations are employed to develop a methodology for the construction of an open-source computational database, collecting properties of electrocatalytically active interfaces, under relevant experimental conditions. These insights will be used to i. elucidate the in-situ nature of electrocatalyst active sites, in the context of technologically-relevant chemical reactions; ii. bridge the gap between experimental and computational methods in addressing key processes hindering the development of active and stable electrocatalytic materials for successful deployment of fuel cells technology; iii. go beyond state-of-the-art computational models based on well-defined extended surfaces, towards realistic simulations of synthesized nanoparticles, in a complex electrochemical environment.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101063836 |
| Start date: | 15-05-2022 |
| End date: | 14-05-2024 |
| Total budget - Public funding: | - 214 934,00 Euro |
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Original description
The aim of project CAMELLIA (ComputAtional Mapping of ELectrocataLytic InterfAces) is to derive fundamental insights, predict and design electrocatalytic properties (activity, selectivity, stability) of nanoparticles with realistic sizes, under operando conditions. To achieve this goal, a computational approach, based on electronic structure methods – Density Functional Theory calculations – in conjunction with newly developed Artificial Neural Network-trained interatomic potentials, is used. Coverage- , solvent-, and potential-cognizant static and molecular dynamics simulations are employed to develop a methodology for the construction of an open-source computational database, collecting properties of electrocatalytically active interfaces, under relevant experimental conditions. These insights will be used to i. elucidate the in-situ nature of electrocatalyst active sites, in the context of technologically-relevant chemical reactions; ii. bridge the gap between experimental and computational methods in addressing key processes hindering the development of active and stable electrocatalytic materials for successful deployment of fuel cells technology; iii. go beyond state-of-the-art computational models based on well-defined extended surfaces, towards realistic simulations of synthesized nanoparticles, in a complex electrochemical environment.Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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