Representing Hydrogels for Machine Learning: Descriptors, Datasets, and the Data Bottleneck

Two hydrogels made to the same recipe in two laboratories need not be the same material, and the descriptor sets in routine use do not explain why. We argue that machine learning for hydrogels is limited by representation, not architecture. Of the thirteen variables that set a gel’s stiffness, four reach the data table; and with composition fixed completely, 1728 measurement protocols remain, each returning a number published under one heading. Neither the inputs nor the targets are what their names suggest. We diagnose four compounding failure modes and propose a minimum reporting set and benchmark tasks defined by their splits.