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Deep Learning Methods for 2D Material Electronic Properties
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DOI：10.1039/D5DD00155B.png)
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Abstract
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This review explores the impact of deep learning (DL) techniques on understanding and predicting electronic structures in two-dimensional (2D) materials. We highlight unique computational challenges posed by 2D materials and discuss how DL approaches -- such as physics-aware models; generative AI; and inverse design -- have significantly improved predictions of critical electronic properties; including band structures; density of states; and quantum transport phenomena. Through selected case studies; we illustrate how DL methods accelerate discoveries in emergent quantum phenomena; topology; superconductivity; and autonomous materials exploration. Finally; we outline promising future directions; stressing the need for robust data standardization and advocating for integrated frameworks that combine theoretical modeling; DL methods; and experimental validations.
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