ArticleACS omega2026
Struct2ML: End-to-End Prediction of Electronic Charge Densities Using 3D U‑Net.
Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present Struct2ML, a scalable, low-latency volumetric inference pipeline that predicts three-dimensional electronic charge densities directly from atomic configurations. By leveraging a 3D U-Net architecture, our method provides a surrogate route to approximate charge-density fields without performing a full self-consistent Kohn-Sham calculation for each queried structure, mapping multichannel structural descriptors with explicit periodic-boundary handling to volumetric charge fields. Unlike graph-based approaches that often require complex basis functions to reconstruct continuous fields, our grid-based representation maintains a computational complexity of
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.