Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics

Abstract:This position paper argues that the generative molecular dynamics (GenMD) community is not properly evaluating its progress: current evaluations let a model match what is reported while being arbitrarily wrong on what was claimed, which risks over-claiming advantage over classical baselines. This reflects a structural misalignment between common evaluation practices and the scientific tasks the field sets for itself. The reason is that the ground truth---exhaustive molecular dynamics on the systems of interest---is computationally costly, and this cost is what motivates the field. With ground truth inaccessible, evaluation drifts toward convenient proxies, and the resulting norms entrench easily. We identify misaligned evaluation patterns that persist as a community-level equilibrium, and propose concrete observables, reporting standards, and benchmark designs, both computational and experimental, that better reflect what GenMD aspires to deliver. Our aim is to give the field shared language and tools for evaluation that match its scientific ambitions before today's conventions calcify into norms.

Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)

arXiv:2610.12518 [physics.chem-ph]

(or arXiv:2610.12518v1 [physics.chem-ph] for this version)

https://doi.org/10.48550/arXiv.2610.12518 Focus to learn more arXiv-issued DOI via DataCite

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