S. Vandenhaute
One crystal, two phases: Designing phase coexistence in metal-organic frameworks for nanosensing and -actuating
Enthalpy or entropy? How temperature changes the dynamics of alkene cracking intermediates via ab initio derived machine learning potentials
Dynamic nano-sponges: nuclear quantum effects meet neural network potentials
Beyond periodicity: finite crystal modelling of metal-organic frameworks
Accurate description of phase transitions in flexible materials using transferable machine learning potentials
Master of Science in Engineering Physics
2022
UGain (building 60, Magnel), Technologiepark, Zwijnaarde
Towards realistic computational models of nanoporous materials using dimensionality reduction methods
Efficiently overcoming free energy barriers through the identification of collective variables via machine learning approaches
Development of on-the-fly machine learning potentials to investigate phase transformation mechanisms in perovskites
Defect migration in metal halide perovskites using machine learning potentials
Pages
