most citedTransport Novelty Distance: A Distributional Metric for Evaluating Material Generative Models

2 citations · 3 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

1 paper · 1 filter

cs.LG2026

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…