MatterSim-MT is a foundation model pretrained on over 35 million first-principles structures that predicts material structure, dynamics, and thermodynamics while enabling multi-task simulations of phonon splitting, ferroelectric hysteresis, and redox transitions.
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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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MatterSim-MT: A multi-task foundation model for in silico materials characterization
MatterSim-MT is a foundation model pretrained on over 35 million first-principles structures that predicts material structure, dynamics, and thermodynamics while enabling multi-task simulations of phonon splitting, ferroelectric hysteresis, and redox transitions.
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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.