ArticleChemphyschem : a European journal of chemical physics and physical chemistry2026
Short-Range Machine-Learning Potentials for Aqueous Electrolyte Solutions.
Article in Chemphyschem : a European journal of chemical physics and physical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Machine-learning potentials (MLPs) extend the time and length scales of atomistic simulations, enabling the study of complex systems, such as electrolyte solutions. Yet most models face a tradeoff between accuracy, computational cost, and the ability to capture long-range interactions. Large foundation models promise generality but often come with substantial overhead and energy demands. In contrast, compact, system-specific models may offer a more sustainable path for large-scale simulations. Here, we benchmark the MACE architecture on aqueous sodium chloride (NaCl) solutions, systematically varying model size and the level of equivariance to assess their effect on accuracy, stability, and efficiency. We find that predictive accuracy of the investigated MLPs has little influence on key physical observables considered here but is crucial for stability, highlighting the potential of minimal, dedicated models for efficient simulations of electrolyte solutions.
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.