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I think it's actually correct, because G is the gradient not the force so $F = -G$ and so (G + F_target)**2 = ((-G) - F_target)**2 = (F - F_target)**2.
from https://github.com/jax-md/jax-md/blob/main/notebooks/equivariant_neural_networks.ipynb
The plus for forces looks odd
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