8 citations · 11 across the 5 of their papers we have counts for
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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation
Paul Hagemann, Katharina Ueltzen, Simon Müller +2
Generative machine learning is increasingly used for inorganic crystal structure generation. Most models and the corresponding evaluation approaches rely on simple forms of crystal…
Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties
Jonas Grandel, Philipp Benner, Janine George
Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-t…
A critical assessment of bonding descriptors for predicting materials properties
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen +4
Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuabl…