CGAA-FF encodes atoms into grain nodes within equivariant graph models to predict grain energies and atom forces, achieving 0.201-0.253 eV/Å errors with 5-10x efficiency on EC/EMC and RDX systems.
Application of pretrained universal machine- learning interatomic potential for physicochemical simulation of liquid electrolytes in li-ion battery,
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MACE-MP-0 is a general-purpose atomistic ML force field trained on public data that enables stable simulations of diverse chemical systems with qualitative and sometimes quantitative accuracy, serving as a starting point for fine-tuning.
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Coarse-grained graph architectures for all-atom force predictions
CGAA-FF encodes atoms into grain nodes within equivariant graph models to predict grain energies and atom forces, achieving 0.201-0.253 eV/Å errors with 5-10x efficiency on EC/EMC and RDX systems.
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A foundation model for atomistic materials chemistry
MACE-MP-0 is a general-purpose atomistic ML force field trained on public data that enables stable simulations of diverse chemical systems with qualitative and sometimes quantitative accuracy, serving as a starting point for fine-tuning.