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Creators/Authors contains: "Andolina, Christopher M"

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  1. ; (, Digital Discovery)
    Understanding how materials melt is crucial for their practical applications and development, machine learning atomistic potentionals are enabling us to better predict these behaviors in real-world environmental conditions. 
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  2. ; (, Digital discovery)
    Machine learning atomistic potentials (MLPs) trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost. 
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  3. ; ; (, The Journal of Physical Chemistry Letters)
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  4. ; ; ; ; ; ; ; ; (, Nanoscale)
    While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. 
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  5. ; ; (, The Journal of Physical Chemistry Letters)
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  6. ; ; ; (, Digital Discovery)
    Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy in the prediction of material properties. However, the generation of large datasets needed for training MLPs is daunting. Herein, we show that MLP-based material property predictions converge faster with respect to precision for Brillouin zone integrations than DFT-based property predictions. We demonstrate that this phenomenon is robust across material properties for different metallic systems. Further, we provide statistical error metrics to accurately determine a priori the precision level required of DFT training datasets for MLPs to ensure accelerated convergence of material property predictions, thus significantly reducing the computational expense of MLP development. 
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  7. ; ; ; (, The Journal of Physical Chemistry C)
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  8. ; ; ; (, Physical Review Materials)
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  9. ; ; ; ; ; ; ; ; ; ; et al (, Nanotechnology)
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