Performance of MatterSim on benchmark datasets

(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.

Authors present MatterSim, a deep learning model actively learned from large-scale first-principles computations, for efficient atomistic simulations at first-principles level and accurate prediction of broad material properties across the periodic table, spanning temperatures from 0 to 5000 K and pressures up to 1000 GPa. https://arxiv.org/abs/2405.04967

Table S5 from the MatterSim paper: Comparison of property prediction performance for M3GNet and Graphormer models.