Computational Mapping of Electrocatalytic Interfaces In-Operando Conditions

Projektinformationen

CAMELLIA

ID Finanzhilfevereinbarung: 101063836

DOI

10.3030/101063836

Das Projekt endete am 1 September 2023

EK-Unterschriftsdatum 19 Mai 2022

Startdatum 15 Mai 2022

Enddatum 14 Mai 2024

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 214 934,40

Koordiniert durch

DANMARKS TEKNISKE UNIVERSITET
Denmark

Periodic Reporting for period 1 - CAMELLIA (Computational Mapping of Electrocatalytic Interfaces In-Operando Conditions)

Berichtszeitraum: 2022-05-15 bis 2024-05-14

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

In the realm of chemical and energy production, catalysis plays a pivotal role in driving innovation and sustainability. Enhanced catalytic technologies are crucial for optimizing resources, reducing environmental impacts, and making essential commodities, such as pharmaceuticals, more accessible and cost-effective.

A profound understanding of catalysts at the microscopic level is essential for this technological evolution. Utilizing tools like quantum mechanics and machine learning, we aim to uncover nuanced details of catalytic processes in both homogeneous and heterogeneous environments. These insights will foster technological advancements by enabling the optimization of catalytic processes.

Project CAMELLIA has embarked on this challenging journey with specific objectives:

Refining Computational Models:

We intend to improve existing computational models to more accurately represent the dynamics of catalysts in various chemical reactions. By incorporating sophisticated techniques like accelerated Molecular Dynamics, machine learning, and artificial intelligence, we aim to deepen our understanding of catalyst behaviors at microscopic levels.
Developing a Comprehensive Catalysis Database:

Our goal is to assemble a robust database that will serve as a global knowledge reservoir, encompassing extensive information on catalytic properties and behaviors. This initiative will promote collaborative research and innovation by providing a rich resource of information in the field of catalysis.

Achieving these objectives will significantly advance the field of catalysis, facilitating the development of more efficient and sustainable production processes, thereby enriching industries such as healthcare and energy.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

The project navigated through a dynamic landscape of machine learning and artificial intelligence in chemistry and materials science, adapting its focus in response to rapid advancements in the field.

Homogeneous Catalysts:

Initially aimed at understanding electrified interfaces for energy conversion, the project shifted focus towards exploring homogeneous catalysts due to the emergence of extensive research in the initial area.
A detailed study was conducted on the Buchwald-Hartwig amination reaction, crucial in industries like pharmaceuticals and agrochemicals. Through rigorous analysis involving 37 different catalysts and a combination of quantum chemistry calculations, we unveiled fundamental insights, pushing the boundaries of current knowledge.
High-Throughput Computational Screening:

A comprehensive high-throughput computational screening was performed, revealing over 70 new catalyst candidates. This exploration was enriched by developing insights into the transferability of scaling relations across different catalyst and substrate combinations.
Database Development:

A substantial database was constructed, encompassing information on over 400 catalyst and substrate combinations specifically for the Buchwald-Hartwig amination reaction.
Electrocatalysis:

Parallel research avenues were pursued in electrocatalysis, focusing on applications like fuel cells. Multifaceted methodologies involving static and molecular dynamics calculations were employed to glean insights into processes like NaBH4 electro-oxidation and ammonia production.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

Our project has made substantial contributions in both homogeneous and heterogeneous catalysis, leading to significant advancements beyond the current state of the art:

Comprehensive Database Development:

A detailed database has been established, focusing on the thermodynamics and kinetics of the Buchwald-Hartwig amination reaction. This database, containing over 400 catalyst/substrate combinations, enables enhanced understanding and the application of mathematical and chemical insights across various catalysts and substrates. Further research is planned to explore the applicability of these insights from homogeneous to heterogeneous catalysis.

Understanding Solid-Electrolyte Interface Formation:

Preliminary work has provided insights into the formation of a copper/lithium/tetrahydrofurane solid-electrolyte interface during the nitrogen reduction reaction. Through Ab Initio Molecular Dynamics simulations, potential mechanisms of tetrahydrofurane decomposition have been identified. These initial findings will be further validated through more extensive and replicated simulations to ensure their statistical significance and relevance.

Insights into NaBH4 Decomposition:

The project has generated essential knowledge regarding the mechanism of NaBH4 decomposition and oxidation on Pt-group metal surfaces. This contribution is significant due to the limited existing research in this area. Future work will expand beyond monometallic surfaces, exploring the potential of more effective electrocatalysts to improve the oxidation of NaBH4 and minimize competing hydrolysis processes.

Harnessing Computational Simulations for applications in Machine Learning and AI:

The computational simulations undertaken have generated thousands of data points. This rich dataset is a valuable resource for training machine learning models and artificial neural networks, enabling the calculation of properties of realistic chemical systems from molecular-level descriptions.

These achievements mark notable progress in catalysis, providing a solid foundation for further research and exploration in the field.