Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table
نویسندگان
چکیده
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 its computational cost.
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ژورنال
عنوان ژورنال: Digital discovery
سال: 2023
ISSN: ['2635-098X']
DOI: https://doi.org/10.1039/d3dd00046j