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ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density — arXiv:2608.03260

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.03260
License (as archived): CC0-1.0
Archived: 2026-10-31T12:42:23.453Z
SHA-256 of the archived text: 9dfb0b45f497730f1bce7abbcbb06b78f865b14e3fbc2182fe76c918aa786c61
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How to cite

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density — arXiv:2608.03260 — arXiv (en), https://arxiv.org/abs/2608.03260 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aa77973bc012 (Metraipolis archive dc_aa77973bc012, sha256 9dfb0b45f497730f1bce7abbcbb06b78f865b14e3fbc2182fe76c918aa786c61)

BibTeX

@misc{dcaa77973bc012,
  title = {ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density — arXiv:2608.03260},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.03260},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aa77973bc012; sha256 9dfb0b45f497730f1bce7abbcbb06b78f865b14e3fbc2182fe76c918aa786c61},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.03260. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Pretraining has shown strong potential for learning transferable representations, yet it remains underexplored for electron-density-based molecular learning. Electron density provides a continuous three-dimensional description of molecular electronic structure, capturing both local spatial patterns and global physical quantities. This raises a key question: can electron-density fields be used for self-supervised pretraining to learn a shared representation that transfers across diverse electronic-structure-related tasks? We propose ED-DiT, a physics-guided Diffusion Transformer for self-supervised pretraining on electron-density point clouds. ED-DiT learns reusable representations by reconstructing corrupted and partially masked log-density fields across diffusion noise levels. An electron-number consistency constraint is further introduced to preserve the total electronic mass. The pretrained encoder can be adapted to property prediction, open-/closed-shell classification, molecule-electron-density retrieval, and molecule-conditioned electron-density prediction. Experiments on six EDBench tasks show that ED-DiT consistently outperforms the same architecture trained from scratch, especially under limited supervision. For molecule-conditioned electron-density prediction, it reduces RMSE from 2.2474 to 1.3753 and surpasses the available baseline. With only 10% labels, it improves orbital energy prediction RMSE from 0.0293 to 0.0138. These results demonstrate the effectiveness of physics-guided electron-density pretraining for learning transferable molecular representations.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-05 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.03260 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:31:30.346Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).