M. Cools-Ceuppens
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
The eMLP: a novel machine learning potential to model electronic properties with explicit-electrons
ISBN/ISSN:
Talk
Conference / event / venue
AutoCheMo International Reactive Force Field Workshop
Ghent, Belgium
Wednesday, 8 December, 2021 to Thursday, 9 December, 2021
Machine learning of the ground state density of molecules for force field development
Is classic enough? Exploring how nuclear quantum effects affect proton mobility in zeolites through ab initio-derived machine learning potentials
Accurate description of phase transitions in flexible materials using transferable machine learning potentials
Bridging the accuracy gap in material discovery using machine learning
Heating up the search for energy materials: deep learning from disorder
Investigating adsorption-induced flexibility in metal-organic frameworks using machine learning potentials
Machine learning of atomic polarizabilities for polarizable force field development
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