Continued challenges in high-throughput materials predictions: MatterGen predicts compounds from the training dataset

High-throughput computational tools and generative AI models aim to revolutionise materials discovery by enabling the rapid prediction of novel inorganic compounds. However, these tools face persistent challenges with modelling compounds where multiple elements occupy the same crystallographic site, often leading to misclassification of known disordered phases as new ordered compounds. Recently, Microsoft revealed MatterGen as a tool for predicting new materials. As a proof of concept, MatterGen was used to predict the novel compound TaCr₂O₆, which was subsequently synthesised in a disordered form as Ta_1/3Cr_2/3O₂. However, detailed crystallographic analysis presented in this paper reveals that this is not a novel compound but is identical to the previously reported Ta_1/2Cr_1/2O₂, first described in 1971 and included in MatterGen's training dataset. These findings underscore the necessity of rigorous human verification in AI-assisted materials research, limiting their use for rapid and large-scale prediction of new materials. While generative models hold great promise, their effectiveness is currently limited by unresolved issues with disorder prediction and dataset validation. Improved integration with crystallographic expertise is essential to realise their full potential.