Author

N. Erichson

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#artificial intelligence Preprint Oct 2026

Zatom-2: Multitask Pretraining on Atomistic Data for Generative Modeling across Domains

Unified atomistic modeling has the potential to accelerate discovery in chemistry, materials science, and biology by bridging data-rich chemical domains and data-scarce biological contexts. However, existing generative approaches to atomistic modeling remain highly specialized to scientific disciplines (chemistry vs. b...

M. Cretu, A. Abrudan, Antonia Panescu et al. · 0 citations

#machine learning Preprint Oct 2026

Variational Streaming Flow: Probabilistic Forecasting in Physical Time

Probabilistic forecasting is important for predicting complex dynamical systems because intrinsic randomness and incomplete observations can cause the same observed state to evolve into multiple plausible futures. While flow matching is a flexible approach for probabilistic forecasting, it is computationally expensive....

Hans Hao-Hsun Hsu, Minseon Gwak, Soon Hoe Lim et al. · 0 citations