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This work trains a regression ensemble on 37 polymers using bulk physical properties and elemental composition ratios to predict atomic-oxygen erosion yield in low Earth orbit. While achieving a leave-one-out R^2(log) of 0.768 on the training set, the model showed limited predictive power on post-MISSE-2 polymers due to their narrow target range. The study highlights the need for reconciled measurement records and prospectively fixed inputs for effective orbital-data screening models.
Existing models struggle to predict atomic-oxygen erosion in polymers, but a data-driven ensemble trained on bulk properties achieves 0.768 R^2(log) on a diverse MISSE-2 dataset.
Atomic oxygen (AO) erodes polymer surfaces in low Earth orbit, but durability data are limited. We evaluated a regression ensemble trained on 37 MISSE-2 polymers using eight selected bulk properties and five elemental-composition ratios. Its archived leave-one-out R^2(log) was 0.768; this estimate is conditional on feature selection using the complete training table. For nine post-MISSE-2 polymers, the ensemble gave R^2(log) = -0.037 and all predictions were within a factor of two. The training arithmetic-mean baseline also met that tolerance for all nine, highlighting the limited discrimination provided by their narrow target range. Ground-based comparisons included four PLAVIS grades. For an out-of-training mystery polymer with undisclosed composition, the predicted erosion yield was within a factor of 1.92 of the ground-based measurement. Conflicting PLAVIS measurement records prevent a definitive residue-erosion ordering or ground-validation score. Thermal residue, SEM contrast and surface chemistry provide distinct observational evidence; they do not establish a protective-layer mechanism. The results support further evaluation of orbital-data screening models with reconciled measurement records and prospectively fixed inputs..