PaMM augments an equivariant atomistic model with explicit pair and triplet motif memory tables, producing modest gains in energy and force MAE on OMAT benchmarks at fixed training budget.
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MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.
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PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interface
PaMM augments an equivariant atomistic model with explicit pair and triplet motif memory tables, producing modest gains in energy and force MAE on OMAT benchmarks at fixed training budget.
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MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.