Descripción del proyecto
Modelización del control de los campos eléctricos de los marcos organometálicos para promover la absorción de hidrógeno
Los marcos organometálicos (MOM, o MOF por sus siglas en inglés) son materiales cristalinos porosos formados por una red tridimensional de iones metálicos sujetos por moléculas orgánicas. Su porosidad muy elevada y las grandes superficies resultantes en relación con los volúmenes han captado la atención para el almacenamiento y la separación de gases. El equipo del proyecto HydroMOF, que cuenta con el apoyo de las Acciones Marie Skłodowska-Curie, investigará la utilidad de los MOM para el almacenamiento de hidrógeno, un desafío clave para la economía del hidrógeno. En concreto, se utilizarán potenciales interatómicos de aprendizaje automático para investigar el fomento del almacenamiento de hidrógeno en los MOM mediante cambios estructurales, que incluyen la apertura y el cierre de los poros a través de la aplicación de un campo eléctrico.
Objetivo
Hydrogen storage is a key technological barrier to the development and widespread use of hydrogen energy in transportation, stationary, and portable applications. The safe and efficient storage of H2 is an important and still challenging issue. In this regard, metal-organic frameworks (MOFs) possessing large surface areas, a variety of topological/chemical structures, high porosities, and high stabilities are considered promising nanoporous materials for gas storage applications. Theoretical studies have shown that electric fields can promote gas storage in MOFs by inducing controlled structural changes, which has recently been experimentally confirmed.
To unravel the underlying mechanisms at the atomic level, I aim to investigate the H2 storage characteristics of electric field responsive MOFs by combining machine learning (ML) and molecular modeling methods. In order to model switchable MOFs, I will construct machine learning potentials and use them in Molecular Dynamics (MD) simulations in order to overcome the restricted time and length scales of ab-Initio MD, and the accuracy and reliability concerns of MD. In order to determine the H2 uptakes of the MOFs, I will push the limits further and combine machine learning potentials with the Grand Canonical Monte Carlo (GCMC) simulations.
The main research objectives are to generate accurate machine learning potentials with a low computational time cost, determine how selected MOFs react to an applied electric field, control pore opening/closing, control and improve H2 uptake by switching electric field, determine the impact of an electric field on the interaction between the host and H2 as well as on the presence of different adsorption sites. This project has the potential to be a “door-opener” for fast and accurate design of switchable MOFs and boosting their applications in H2 storage by providing a fundamental perspective.
Ámbito científico
Palabras clave
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
MSCA-PF - MSCA-PFCoordinador
44801 Bochum
Alemania