A co-trained multifidelity mixture-of-experts MLIP partitions simulations into high- and low-capacity regions, maintains exact energy conservation and bulk modulus alignment, and runs more than twice as fast as a single high-fidelity model on a Pt+CO system.
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.Nature communica- tions, 13(1):2453
2 Pith papers cite this work. Polarity classification is still indexing.
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An equivariant message-passing neural network embeds atomic spins explicitly to learn magnetic interactions, achieving near-DFT accuracy and data efficiency across magnetic systems via fine-tuning.
citing papers explorer
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Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations
A co-trained multifidelity mixture-of-experts MLIP partitions simulations into high- and low-capacity regions, maintains exact energy conservation and bulk modulus alignment, and runs more than twice as fast as a single high-fidelity model on a Pt+CO system.
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Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials
An equivariant message-passing neural network embeds atomic spins explicitly to learn magnetic interactions, achieving near-DFT accuracy and data efficiency across magnetic systems via fine-tuning.