Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909978380468224 |
|---|---|
| author | Lin, Kailai Coley-O'Rourke, Matthew J. Rabani, Eran |
| author_facet | Lin, Kailai Coley-O'Rourke, Matthew J. Rabani, Eran |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials Lin, Kailai Coley-O'Rourke, Matthew J. Rabani, Eran Materials Science Mesoscale and Nanoscale Physics Computational Physics 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. |
| title | Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials |
| topic | Materials Science Mesoscale and Nanoscale Physics Computational Physics |
| url | https://arxiv.org/abs/2505.09846 |