4 papers · 1 filter
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…
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…
Transport Novelty Distance: A Distributional Metric for Evaluating Material Generative Models
Paul Hagemann, Simon Müller, Janine George +1
Recent advances in generative machine learning have opened new possibilities for the discovery and design of novel materials. However, as these models become more sophisticated, th…
SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
Sasan Amariamir, Janine George, Philipp Benner
Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a c…