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Machine Learning Electrostatic Interactions in Materials
Maschinelles Lernen für interatomare Potentiale (MLIPs) erreicht nahezu first-principles-Genauigkeit bei deutlich geringerem Rechenaufwand. Die meisten MLIPs basieren jedoch auf lokalen oder semi-lokalen Darstellungen atomarer Umgebungen, was ihre Fähigkeit einschränkt, langreichweitige Elektrostati
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