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arXiv · 2505.09846

Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials

Kailai Lin·Matthew J. Coley-O'Rourke·Eran Rabani
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Abstract

The semi-empirical pseudopotential method (SEPM) has been widely applied to provide computational insights into the electronic structure, photophysics, and charge carrier dynamics of nanoscale materials. We present "DeepPseudopot", a machine-learned atomistic pseudopotential model that extends the SEPM framework by combining a flexible neural network representation of the local pseudopotential with parameterized non-local and spin-orbit coupling terms. Trained on bulk quasiparticle band structures and deformation potentials from GW calculations, the model captures many-body and relativistic effects with very high accuracy across diverse semiconducting materials, as illustrated for silicon and group III-V semiconductors. DeepPseudopot's accuracy, efficiency, and transferability make it well-suited for data-driven in silico design and discovery of novel optoelectronic nanomaterials.

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BibTeXRIS

Kailai Lin, Matthew J. Coley-O'Rourke, Eran Rabani. 2025-05-14. Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials. https://doi.org/10.1038/s41524-025-01862-5

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