ArticleJACS Au2025
A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy.
Article in JACS Au, 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Study of the Gas-Phase Pyrolysis of N-Aryl-3-Oxobutanamides and the 2-Aryl Hydrazone Derivatives: A Novel Computational Approach Including DFT with Multivariant Analysis.Molecules (Basel, Switzerland) · 2026Article
- Improvements in chemical reaction pathway exploration algorithms and dataset generation.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Accurate prediction of reaction energetics remains a fundamental challenge in computational chemistry, as conventional density functional theory (DFT) often fails to reconcile high accuracy with computational efficiency. Here, we introduce Deep post-Hartree-Fock (DeePHF), a machine learning framework that integrates neural networks with quantum mechanical descriptors to achieve CCSD-(T)-level precision while retaining the efficiency of DFT to solve the reaction problems. By establishing a direct mapping between the eigenvalues of local density matrices and high-level correlation energies, DeePHF circumvents the traditional accuracy-scalability tradeoff. Trained on a limited data set of small-molecule reactions, our model demonstrates superior performance across multiple benchmark data sets, exhibiting exceptional transferability. In fact, its accuracy even surpasses that of advanced double-hybrid functionals, all while maintaining O-(N
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.