HydroMOF · Hydrogen Storage in Electric Field Responsive Metal Organic Frameworks Studied by Machine Learning Potentials
Horizon Europe — Marie Skłodowska-Curie Actions
- Duration
- 2022-09-01 → 2024-08-31
- EU contribution
- €189,687
- Participants
- 2
- Scheme
- HORIZON-TMA-MSCA-PF-EF
Lines connect the coordinator with its partners.
Results in brief
Hydrogen Storage in Electric Field Responsive Metal Organic Frameworks Studied by Machine Learning Potentials
Let’s imagine that, in a near-future city, only hydrogen-powered cars move quietly along streets. The air is fresh, thanks to nearby hydrogen refueling stations that allow drivers to fill up quickly. Factories have changed too, using hydrogen as a primary energy source to power their machinery. In parks, families enjoy picnics, while children fly hydrogen-powered drones. The imagination of this future city is bright and inspiring, but there's a big challenge: efficient hydrogen storage systems haven't been developed yet. The HydroMOF project, supported by the EU’s HORIZON Marie Skłodowska-Curie Actions, aims to tackle this issue by developing smart and controllable systems for storing hydrogen using nanoporous metal-organic frameworks (MOFs). Recent studies have shown that external electric fields can induce controlled structural changes in MOFs, allowing them to undergo reversible transformations, which opens up new possibilities for hydrogen storage. Computational chemistry and machine learning methods have been applied in this project to investigate the potential of switchable MOFs. By combining machine learning potentials with molecular dynamics simulations, the HydroMOF project aimed to overcome the limitations of quantum chemistry methods in terms of simulation time and length scales, as well as the accuracy and reliability issues associated with classical molecular dynamics simulations. The results of the HydroMOF project can be a door-opener for fast and accurate modeling of switchable MOFs, significantly boosting their applications in hydrogen storage.
Data: CORDIS, © European Union
Project objective
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.
Original text from CORDIS.
Participants
Links
- View on CORDIS
- DOI: 10.3030/101063496
- https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e512e6cd4f&appId=PPGMS
- https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5f85699b0&appId=PPGMS
Data: CORDIS, © European Union