The datasets used in these tasks encompassed a wide variety of molecules. For lattice energy prediction, the dataset included a set of 10 structurally related molecules with small changes in hydrogen bonding functionality, derived from earlier crystal structure prediction (CSP) studies , providing a relevant test for fine-tuning a model to study a closely related family of molecules. By contrast, the ∆-E task involved a deliberately diverse set of 1018 organic molecules, designed to develop a generalised ML model capable of improving lattice energy predictions across broad and chemically diverse areas of molecular space . The charge mobility predictions focused on 7 pentacene and azapentacene molecules , which are key compounds in organic semiconductor research, while the methane capacity and diffusivity tasks were based on CSP structures for the HOF-forming molecule T2 . All datasets (except for ∆-E) were randomly split with a train-validation-test ration of 80%:10%:10%. The ∆-E dataset was split according to the original paper . A detailed description of the datasets and the methods used for their generation can be found in Section 4.