most citedAccelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function

13 citations · 16 across the 4 of their papers we have counts for

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cond-mat.mtrl-sci2024

When More Data Hurts: Optimizing Data Coverage While Mitigating Diversity Induced Underfitting in an Ultra-Fast Machine-Learned Potential

Jason B. Gibson, Tesia D. Janicki, Ajinkya C. Hire +3

Machine-learned interatomic potentials (MLIPs) are becoming an essential tool in materials modeling. However, optimizing the generation of training data used to parameterize the ML…