LaMM pre-trains neural network potentials on about 300 million mixed labeled and unlabeled structures using denoising pseudo-forces and a load-balanced multi-GPU pipeline, improving HME21 fine-tuning accuracy and speed.
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models
LaMM pre-trains neural network potentials on about 300 million mixed labeled and unlabeled structures using denoising pseudo-forces and a load-balanced multi-GPU pipeline, improving HME21 fine-tuning accuracy and speed.