Year: 2026
Type: preprint
Abstract: Machine-learning screens of metal–organic frameworks (MOFs) for CO2 capture increasingly report the validation protocol that produced a score, and topology hold-out has become the standard stricter alternative to random splitting. Prior work already reads topology hold-out as a combined structural and chemical distribution shift; this work measures the size of the chemistry that the split withholds. On the hypothetical-MOF library hMOF (324,426 structures), 74.7% of coarse-grained chemical compositions occur in a single topology, accounting for 61.3% of structures, and seven of eleven topologies leave every structure without a chemical counterpart in training. On the one usable chemistry-matched pair (pcu ↔nbo), removing the partner topology raised nbo-side MAE by 70.0% while a size-matched control did not, and removing only the shared-composition partner rows improved pcu-side MAE, so partner coverage is direction-dependent rather than uniformly beneficial. A chemistry-key hold-out produces only 21.1% of the topology hold-out's R2 decrease, so the support loss is real but does not translate proportionally into performance loss. Across five independently redrawn 25,000-sample support sets, the top-1% hit rate fell from 50.4% to 19.3% under topology-grouped validation. A support audit in ARC-MOF, which is partly dependent on hMOF and uses different composition keys, confirms strong coupling under bounded sensitivity rather than as an independent replication. We retain topology hold-out as a stress test while recommending that groupedsplit reports disclose their chemical support, deployment-relevant aggregation and ranking performance.
Source: ChemRxiv
Author: Tao Wang
Institution: Nanjing Tech University
Language: English
Subfield: Inorganic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7222415650