A knowledge-distillation framework that uses an off-the-shelf pre-trained neural network potential as teacher, followed by a small density-functional-theory fine-tuning set, produces fast and accurate molecular dynamics models for two test materials with far fewer expensive DFT labels.
ACS Applied Materials & Interfaces16(28), 36878–36891 (2024)
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Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations
A knowledge-distillation framework that uses an off-the-shelf pre-trained neural network potential as teacher, followed by a small density-functional-theory fine-tuning set, produces fast and accurate molecular dynamics models for two test materials with far fewer expensive DFT labels.