596 citations · 716 across the 8 of their papers we have counts for
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physics.chem-ph2025★ 100 cited
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng +44
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for m…
physics.comp-ph2025★ 2 cited
Learning atomic forces from uncertainty-calibrated adversarial attacks
Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson +3
Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatom…