ArticleThe journal of physical chemistry letters2024
To Pair or not to Pair? Machine-Learned Explicitly-Correlated Electronic Structure for NaCl in Water.
Article in The journal of physical chemistry letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Densities, Viscosities, and Self-Diffusion Coefficients of Sodium Chloride in Mixed Water-Polyethylene Glycol Solvents.Journal of chemical and engineering data · 2026Article
- Short-Range Machine-Learning Potentials for Aqueous Electrolyte Solutions.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous Solutions.The journal of physical chemistry. B · 2026Article
- Clay Edges Are Dynamic Proton-Conducting Networks Modulated by Structure and pH.The journal of physical chemistry letters · 2026Article
- Entropy Governs the Structure and Reactivity of Water Dissociation Under Electric Fields.Journal of the American Chemical Society · 2025Article
- Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water.Journal of chemical theory and computation · 2025Article
- Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.Chemical science · 2025Article
- Article
- Modelling ligand exchange in metal complexes with machine learning potentials.Faraday discussions · 2025Article
- Crumbling crystals: on the dissolution mechanism of NaCl in water.Physical chemistry chemical physics : PCCP · 2024Article
- The Interplay of Solvation and Polarization Effects on Ion Pairing in Nanoconfined Electrolytes.Nano letters · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The extent of ion pairing in solution is an important phenomenon to rationalize transport and thermodynamic properties of electrolytes. A fundamental measure of this pairing is the potential of mean force (PMF) between solvated ions. The relative stabilities of the paired and solvent shared states in the PMF and the barrier between them are highly sensitive to the underlying potential energy surface. However, direct application of accurate electronic structure methods is challenging, since long simulations are required. We develop wave function based machine learning potentials with the random phase approximation (RPA) and second order Møller-Plesset (MP2) perturbation theory for the prototypical system of Na and Cl ions in water. We show both methods in agreement, predicting the paired and solvent shared states to have similar energies (within 0.2 kcal/mol). We also provide the same benchmarks for different DFT functionals as well as insight into the PMF based on simple analyses of the interactions in the system.
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.