ArticleJournal of chemical theory and computation2024
Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- An Energy-Corrected Fast Post-SCF Local-Hybrid Scheme for Highly Accurate Energy Differences of Large Main-Group Systems.Journal of computational chemistry · 2026Article
- "Gold-Standard" Δ-Machine Learned Transferable Potential for Linear Alkanes.The journal of physical chemistry letters · 2025Article
- Dynamics of protonated oxalate from machine-learned simulations and experiment: infrared signatures, proton transfer dynamics and tunneling splittings.Physical chemistry chemical physics : PCCP · 2025Article
- NN-VRCTST: Neural Network Potentials Meet Variable Reaction Coordinate Transition State Theory for the Rate Constant Determination of Barrierless Reactions.Journal of chemical theory and computation · 2025Article
- Toward Improving Multiple Time Step QM/MM Simulations with Δ-Machine Learning.The journal of physical chemistry. B · 2025Article
- Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.Chemical science · 2025Article
- Targeted Transferable Machine-Learned Potential for Linear Alkanes Trained on CJournal of chemical theory and computation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently, Δ-machine learning has been used to elevate the quality of a potential energy surface (PES) based on low-level, e.g., density functional theory (DFT) energies and gradients to close to the gold-standard coupled cluster level of accuracy. We have demonstrated the success of this approach for molecules, ranging in size from H
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.