Design And Modeling of Oxide Catalysts by machine LEarning and atomistic Simulations

Información del proyecto

DAMOCLES

Identificador del acuerdo de subvención: 101108769

DOI

10.3030/101108769

Proyecto cerrado

Fecha de la firma de la CE 12 Abril 2023

Fecha de inicio 1 Septiembre 2023

Fecha de finalización 31 Agosto 2025

Financiado con arreglo a

• Marie Skłodowska-Curie Actions (MSCA)

Coste total

Sin datos

Aportación de la UE

€ 196 829,28

Coordinado por

AARHUS UNIVERSITET
Denmark

Objetivo

The DAMOCLES project aims to apply data-driven modeling (i.e. molecular simulations combined with machine learning) to study and tailor metal oxide catalysts for CO2 hydrogenation processes (reverse water-gas shift, CO2 methanation, and CO2 to methanol), with the final goal of screening oxides in search of new and better catalytic materials. The starting point of the project is the newly-released OC22 oxide data set (from Meta FAIR and Ulissi's group) comprising approximately 50k adsorption energies of relevant molecules on multi-component oxide surfaces spanning 52 elements of the periodic table. State-of-the-art machine learning models (e.g. Gaussian process regression, and graph neural networks), will be applied for the prediction of relevant adsorption energies not included in the sparsely labeled OC22 dataset. New density-functional theory (DFT) calculations will target the adsorption energy of molecules not considered in the data set and activation energies of the elementary reactions of the CO2 hydrogenation paths. The catalytic performances (i.e. activity and selectivity) of the oxide surfaces in OC22 will be evaluated through microkinetic modeling. Active learning will be applied to iteratively improve the model predictions with additional DFT calculations targeting the parameters selected with sensitivity analysis and uncertainty quantification. The overarching goal of the DAMOCLES project is to understand the effects of the structure and composition of the catalyst surface in the processes of CO2 hydrogenation and identify new promising catalytic materials. This insight can guide experimental researchers in the synthesis of oxide materials with improved catalytic performances, thus helping the development of economically sustainable processes for the transformation of waste CO2 into useful chemicals and fuels.

Ámbito científico (EuroSciVoc)

• ciencias naturales › ciencias químicas › química orgánica › alcoholes
• ciencias naturales › ciencias químicas › catálisis
• ingeniería y tecnología › ingeniería ambiental › energía y combustibles

Palabras clave

• Catalyst screening
• multi-component oxides
• atomistic modeling
• machine learning
• graph neural networks
• Gaussian process regression

Programa(s)

• HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) PROGRAMA PRINCIPAL
  

Tema(s)

• HORIZON-MSCA-2022-PF-01-01 - MSCA Postdoctoral Fellowships 2022
  

Régimen de financiación

HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

Convocatoria de propuestas

HORIZON-MSCA-2022-PF-01

Coordinador

AARHUS UNIVERSITET

• Aportación neta de la UEn: € 196 829,28
• Dirección: NORDRE RINGGADE 1 ; 8000 Aarhus C ; Dinamarca
• Región: Danmark › Midtjylland › Østjylland
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: Sin datos

Socios (1)

Socio

CARNEGIE MELLON UNIVERSITY

Estados Unidos

• Aportación neta de la UEn: € 0,00
• Dirección: FORBES AVENUE 5000 ; 15213 PITTSBURGH
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: Sin datos