Web: http://arxiv.org/abs/2205.01223

May 4, 2022, 1:11 a.m. | Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, Zachary Ulissi

cs.LG updates on arXiv.org arxiv.org

Machine learning approaches have the potential to approximate Density
Functional Theory (DFT) for atomistic simulations in a computationally
efficient manner, which could dramatically increase the impact of computational
simulations on real-world problems. However, they are limited by their accuracy
and the cost of generating labeled data. Here, we present an online active
learning framework for accelerating the simulation of atomic systems
efficiently and accurately by incorporating prior physical information learned
by large-scale pre-trained graph neural network models from the Open …

arxiv fine-tuning physics simulations

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