Contact person: Sabrina Sartori, Jonathan Polfus
Keywords: energy storage materials, machine learning, metal hydrides, kinetics and thermodynamics
Research groups: Energy Systems – Department of Technology Systems; Electrochemistry - Department of Chemistry
The deployment of high shares of renewable energy sources into the future energy systems depends on our ability to solve the problem of energy storage. For stationary applications some of the technologies considered so far, for instance super capacitors and lithium-ion batteries (LIBs), although have significantly improved, are not capable to cover the growing demand of tomorrow´s energy storage and power supply in terms of durability, performance, cost, and recyclability. Hydrogen has the potential to be part of the solution: it offers a great degree of flexibility, and its role is considered critical for alleviating the intermittence of renewable energy sources, with medium- to long-term storage in both on-/off-grid energy system configurations. Solid state storage of hydrogen in metal hydrides has attracted a growing attention as safe and efficient storage medium. Hydrogen storage mechanisms in metals occur by reaction-induced metal-to-hydride phase transformations. Capturing these in kinetically aware, non-equilibrium models represents a grand challenge because the operating mechanisms feature complex coupling of surface/interfacial reactions, diffusion, structural changes, and large volume expansion. Research proposals will seek to develop novel computational approaches at different length and time scales to comprehensively explore the co-evolution of relevant chemical and reaction pathways of novel hydrogen storage materials. This will accelerate efforts to determine the design rules that dictate thermodynamic and kinetic properties across the wide chemical and crystallographic space of metal hydrides. Economic and environmental impacts will also guide the discovery of sustainable novel hydrides for large-scale energy storage applications.
Methodological research topics:
- Multiscale modeling of structure-chemistry relationships
- High-throughput atomistic modelling using density functional theory (DFT)
- Establish relationships between physicochemical properties and materials performance and durability with machine learning (ML)
- Prediction and validation of kinetics and thermodynamics of reactions
- Mining computational and experimental data resources
- Characterization and validation of most promising materials trough synchrotron and neutron methods
- Integration of ML into the experimental process
Technological topics (Application domain):
- Energy storage
- Autonomous materials discovery
- Sustainability
External partners:
- Mentoring and internship will be offered by a relevant external partner, and through interaction with the FME HYDROGENi consortium.
