SOURCE-LINKED INTELLIGENCE

Rational design of hydrophobic MOFs for energy applications

CORDIS · observation · Publication date unknown

of MOFs currently being exploited for IE. Addressing these challenges HydroMOF sets to enable the rational design of IE MOFs. This is achieved first via the creation of WetNet, a physically-informed machine learning model able to predict the stability of water inside the cavities of database MOFs. Circumventing the need for case-by-case free energy simulations WetNet enables the screening of thousands of MOFs yielding designs tailored around specific applications. At a later stage molecular insight will inform original lattice models able to simulate actual IE cycles through realistic MOF cavity networks, unprecedentedly encompassing non-equilibrium, finite-size and disorder-related effects. Through these advances HydroMOF’s impact extends beyond IE, opening new avenues in the design of liquid+reticular material devices and in the study of nanoconfined water. Nanofluidics, Molecular Simulation, Wetting, Confined Water, Molecular Dynamics

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recordType

award

status

SIGNED

region

EU

value

388940.52

unit

EUR