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We introduce M$^2$Hub, a toolkit for advancing machine learning in materialsdiscovery. Machine learning has achieved remarkable progress in modelingmolecular structures, especially biomolecules for drug discovery. However, thedevelopment of machine learning approaches for modeling materials structureslag behind, which is partly due to the lack of an integrated platform thatenables access to diverse tasks for materials discovery. To bridge this gap,M$^2$Hub will enable easy access to materials discovery tasks, datasets,machine learning methods, evaluations, and benchmark results that cover theentire workflow. Specifically, the first release of M$^2$Hub focuses on threekey stages in materials discovery: virtual screening, inverse design, andmolecular simulation, including 9 datasets that covers 6 types of materialswith 56 tasks across 8 types of material properties. We further provide 2synthetic datasets for the purpose of generative tasks on materials. Inaddition to random data splits, we also provide 3 additional data partitions toreflect the real-world materials discovery scenarios. State-of-the-art machinelearning methods (including those are suitable for materials structures butnever compared in the literature) are benchmarked on representative tasks. Ourcodes and library are publicly available at https://github.com/yuanqidu/M2Hub.

M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery