ArticleNature communications2025
Image super-resolution inspired electron density prediction.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
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Who cites it
2 citing papers in PubMed.
- Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework.Journal of chemical theory and computation · 2025Article
- Image super-resolution inspired electron density prediction.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Predicting ground-state electron densities of chemical systems has recently received growing attention in machine learning quantum chemistry, given their fundamental importance as highlighted by the Hohenberg-Kohn theorem. Drawing inspiration from the domain of image super-resolution, we view the electron density as a 3D grayscale image and use a convolutional residual network to transform a crude and trivially generated guess of the molecular density into an accurate ground-state quantum mechanical density. Here we show that this model produces more accurate predictions than all prior density prediction approaches. Due to its simplicity, the model is directly applicable to unseen molecular conformations and chemical elements. We show that fine-tuning on limited new data provides high accuracy even in challenging cases of exotic elements and charge states.
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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 OpenQuestion graph.