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
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Table S1 from the MatterSim paper
Machine-learned force fields have transformed the atomistic modeling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on molecules and materials.
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
(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.