M$^2$Hub: Unlocking the Potential of Machine Learning for Materials
  Discovery
Yuanqi Du Yingheng Wang Yining Huang Jianan Canal Li Yanqiao Zhu Tian Xie Chenru Duan John M. Gregoire Carla P. Gomes
We introduce M$^2$Hub, a toolkit for advancing machine learning in materials
discovery. Machine learning has achieved remarkable progress in modeling
molecular structures, especially biomolecules for drug discovery. However, the
development of machine learning approaches for modeling materials structures
lag behind, which is partly due to the lack of an integrated platform that
enables 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 the
entire workflow. Specifically, the first release of M$^2$Hub focuses on three
key stages in materials discovery: virtual screening, inverse design, and
molecular simulation, including 9 datasets that covers 6 types of materials
with 56 tasks across 8 types of material properties. We further provide 2
synthetic datasets for the purpose of generative tasks on materials. In
addition to random data splits, we also provide 3 additional data partitions to
reflect the real-world materials discovery scenarios. State-of-the-art machine
learning methods (including those are suitable for materials structures but
never compared in the literature) are benchmarked on representative tasks. Our
codes and library are publicly available at https://github.com/yuanqidu/M2Hub.