Position paper sparks debate: hidden compute costs may inflate ML speedup claims for molecular dynamics
rishabh16_ · x · 2026-10-28
Commenting on a position paper by @smnlssn, @FrankNoeBerlin, @grantrotskoff and @LindorffLarsen on building ML methods for molecular dynamics (MD) more rigorously, the poster argues the biggest problem is that the true compute cost is often hidden: the compute spent on data generation/curation, training and inference is frequently not disclosed. Making these costs fully transparent, they argue, would yield more accurate estimates of the real speedup ML methods deliver. The paper also includes a practical checklist at the end.
Related event: Scholars Criticize Flawed Evaluation of Generative Molecular Dynamics(2 posts)→
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