ArticleChemical science2023
Δ
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- AIMNet2-rxn: A machine-learned potential for generalized reaction modeling on a millions-of-pathways scale.Science advances · 2026Article
- NN-xTB: density functional accuracy at semi empirical speed with neural network extended tight binding.Nature communications · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Adaptive Transition-State Refinement with Learned Equilibrium Flows.Journal of chemical information and modeling · 2026Article
- Article
- Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states.Digital discovery · 2025Article
- AIQM2: organic reaction simulations beyond DFT.Chemical science · 2025Article
- A Deep Learning-Augmented Density Functional Framework for Reaction Modeling with Chemical Accuracy.JACS Au · 2025Article
- Quantum chemical properties of chlorinated polycyclic aromatic hydrocarbons for delta machine learning.Scientific data · 2025Article
- Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry.Journal of chemical information and modeling · 2025Article
- Machine learning applications for thermochemical and kinetic property prediction.Reviews in chemical engineering · 2025Review
- Navigating structure-based drug discovery with emerging innovations in physics- and knowledge-based approaches.npj drug discovery · 2025Review
- GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts.Journal of cheminformatics · 2024Article
- 3DReact: Geometric Deep Learning for Chemical Reactions.Journal of chemical information and modeling · 2024Article
- Benchmarking machine-readable vectors of chemical reactions on computed activation barriers.Digital discovery · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a correction to a low-level energy calculation, provides a versatile route to accelerate high-level energy evaluations at a given geometry. However, Δ-learning models are inapplicable to reaction properties like heats of reaction and activation energies that require both a high-level geometry and energy evaluation. Here, a Δ
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.