S. Vandenhaute
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
Accurate description of phase transitions in flexible materials using transferable machine learning potentials
Exploring the black-to-yellow phase transformation mechanism of metal halide perovskites via machine learning potentials
It's all about time: Understanding the impact of coarse graining on accelerated diffusion in nanoporous materials
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