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      "text": "In 2022, equivariant message passing neural networks (MPNN) combined with efficient many-body messages (MACE) were introduced at NeurIPS.\nNow, the authors present MACE-MP-0, which uses the MACE architecture and is trained on the Materials Project Trajectory dataset, which contains millions of structures, energies, magnetic moments, forces and stresses.\nThe model reduces the number of message passing layers to two by considering interactions involving four atoms simultaneously, and it only uses nonlinear activations in selective parts of the network.\nIt is capable of molecular dynamics simulation across a wide variety of chemistries in the solid, liquid and gaseous phases.",
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