Using embeddings from a pretrained neural network interatomic potential as features for small machine learning models gives competitive or better property predictions than end-to-end deep networks, especially with limited data.
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Leveraging neural network interatomic potentials for a foundation model of chemistry
Using embeddings from a pretrained neural network interatomic potential as features for small machine learning models gives competitive or better property predictions than end-to-end deep networks, especially with limited data.