T. Verstraelen
Nanoscale simulations of lubricants and lubricant additives?
Exploring the black-to-yellow phase transformation mechanism of metal halide perovskites via machine learning potentials
Bridging the accuracy gap in material discovery using machine learning
Heating up the search for energy materials: deep learning from disorder
Simulation of lubricants behaviour in gear and bearing contacts
Investigating adsorption-induced flexibility in metal-organic frameworks using machine learning potentials
Development of a semi-analytic thermodynamic model for the grand canonical potential of flexible MOFs
Improved force-field approximations of symmetry-adapted perturbation theory
Ranking molecular crystals with a many-body expansion
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