All empirical evidence suggests rather that equivariance unlocks significant gains.
At least when using the right type of architecture - making the wrong choices, the NNs end up over-constrained. Regular GCNNs are essentially always working better (even though comp expensive).
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I might be missing something, but I don't see direct empirical evidence for this claim. Their approach works well, but is orthogonal to equivariance. Combining molecular conformer fields with equivariance would likely improve performance metrics further.
"We [...] empirically show that explicitly enforcing roto-translation equivariance is not a strong requirement for generalization."
"Furthermore, we also show that approaches that do not explicitly enforce roto-translation equivariance (like ours) can match or outperform…
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