Synthetic pre-training on ML-generated tensor data followed by fine-tuning on ground-truth calculations improves data efficiency for graph models of solid-state NMR parameters when the pre-training and fine-tuning domains match.
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ML-potential MD simulations of sodium disilicate, tetrasilicate and hexasilicate melts show sodium hopping via bimodal van Hove functions and strongest non-Gaussian parameter for oxygen atoms.
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Synthetic pre-training of graph-network models for predicting solid-state NMR parameters
Synthetic pre-training on ML-generated tensor data followed by fine-tuning on ground-truth calculations improves data efficiency for graph models of solid-state NMR parameters when the pre-training and fine-tuning domains match.
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Dynamic heterogeneity in sodium silicate melts via machine-learning potential
ML-potential MD simulations of sodium disilicate, tetrasilicate and hexasilicate melts show sodium hopping via bimodal van Hove functions and strongest non-Gaussian parameter for oxygen atoms.