3 papers
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…
physics.chem-ph2026
Extension of openCOSMO-RS Into a Full Open-Source Equation of State: Implementation, Parameterization, and Benchmarking
Jan Markgraf, D. G. Lisboa Girardi, Irina Smirnova +1
The COSMO-SAC-Phi model developed by Soares et al. extends the COSMO-SAC activity-coefficient framework into a full equation of state by explicitly accounting for pressure effects.…
cond-mat.mtrl-sci2025
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…