(a) A data explorer employed in MatterSim for generating datasets covering wide potential energy surface. Histogram of the stress (GPa) and effective temperature (K) of: (b) the generated materials in this work (c) the MPF2021 dataset (d) the Alexandria dataset. (e) Comparative performance metrics of MatterSim across six tasks: energy prediction on MPF-TP and random-TP datasets, phonon properties including max frequency and density of states (DOS), Bulk Modulus, and inverse F1 score in MatBench-Discovery leaderboard. Lower scores indicating superior performance for all tasks.
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Table S1 from the MatterSim paper
Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset (‘3DSC’), featuring the critical temperature Tc of superconducting materials additionally to tested non-superconductors.
Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past 10 years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. Chi Chen & Shyue Ping Ong https://www.nature.com/articles/s43588-022-00349-3 Preprint version from arXiv