VQV Signal
Symmetrix-XL is an inference engine that scales pretrained MACE checkpoints for equivariant atomistic foundation models without retraining or modifying learned weights. It addresses the computational and memory challenges of executing these models at simulation scale.
Symmetrix-XL is an inference engine that scales pretrained MACE checkpoints for equivariant atomistic foundation models without retraining or modifying learned weights. It addresses the computational and memory challenges of executing these models at simulation scale.
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Equivariant atomistic foundation models provide broadly transferable interatomic potentials trained against quantum-mechanical reference data, but their repeated execution at simulation scale remains computationally and memory intensive. We present Symmetrix-XL, an inference engine that scales pretrained MACE...
This approach allows for efficient large-scale simulations using accurate interatomic potentials derived from quantum-mechanical data, facilitating broader application without the cost of retraining. It preserves model accuracy while enabling practical deployment in computationally intensive tasks.
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