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This is a Quantitative Study study.

October 3, 2025Open Access

GPUTB: Efficient Machine Learning Tight-Binding Method for Large-Scale Electronic Properties Calculations

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GPUTB allows efficient calculations of electronic properties for systems with millions of atoms, enhancing predictive capability.
The method accurately reproduces the carrier concentration and room temperature mobility relationship in graphene, validating its effectiveness.
By utilizing atomic environment descriptors, the model adapts to various basis and systems, demonstrating flexibility.
Combined with linear scaling quantum transport, GPUTB handles complex materials like h-BN/graphene heterojunctions with precision.

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This framework employs GPU-accelerated tight-binding to calculate electronic properties, indicating its applicability to large-scale systems.

Wang et al. (2025) studied this question.

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This framework employs GPU-accelerated tight-binding to calculate electronic properties, indicating its applicability to large-scale systems.