ArticleScientific reports2026
PairPotMCinator: a tool for fast simulations of the organic-molecular ordering on the solid surfaces using pair potentials.
Article in Scientific reports, 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
Organic molecules on nonreactive surfaces self-assemble to ordered superstructures. This problem is typically studied using quantum mechanical models, such as Density functional theory (DFT). However, the use of those models for systems with hundreds of atoms is computationally impractical if at least approximate structures are not known. Here, we demonstrate an algorithm employing a Monte Carlo simulation and a method of pair potentials - thus simplifying the quantum mechanical description by using pair-parameterized forces. We incorporate this model into a Monte Carlo simulation which, from a given approximate molecular superstructure periodicity obtained from experimental data, predicts the position and deformation of the ordered molecules to minimize the potential energy of the system. We test the algorithm on published results of self-ordering of selected pigments in different environments: 1) on highly oriented pyrolytic graphite in solution, and 2) on the Si(111)-In[Formula: see text] surface prepared under ultra-high vacuum. The generated structures can be used as close-to-relaxed candidates for inputs to more accurate DFT calculations.
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.