Evidence map›Paper›PMID 30307179›Full record

ArticleThe Journal of chemical physics2018

Predicting structural properties of fluids by thermodynamic extrapolation.

Nathan A Mahynski, Sally Jiao, Harold W Hatch, Marco A Blanco, Vincent K Shen

Abstract read
In one paragraph

Article in The Journal of chemical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nathan A MahynskiChemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8320, USA.ORCID 0000000200088749
Sally JiaoDepartment of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, USA.ORCID 0000000317290414
Harold W HatchChemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8320, USA.ORCID 0000000329269145
Marco A BlancoChemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8320, USA.ORCID 0000000157689038
Vincent K ShenChemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8320, USA.

Funding

Intramural NIST DOC 9999-NIST
6 · The paper itself

Abstract

We describe a methodology for extrapolating the structural properties of multicomponent fluids from one thermodynamic state to another. These properties generally include features of a system that may be computed from an individual configuration such as radial distribution functions, cluster size distributions, or a polymer's radius of gyration. This approach is based on the principle of using fluctuations in a system's extensive thermodynamic variables, such as energy, to construct an appropriate Taylor series expansion for these structural properties in terms of intensive conjugate variables, such as temperature. Thus, one may extrapolate these properties from one state to another when the series is truncated to some finite order. We demonstrate this extrapolation for simple and coarse-grained fluids in both the canonical and grand canonical ensembles, in terms of both temperatures and the chemical potentials of different components. The results show that this method is able to reasonably approximate structural properties of such fluids over a broad range of conditions. Consequently, this methodology may be employed to increase the computational efficiency of molecular simulations used to measure the structural properties of certain fluid systems, especially those used in high-throughput or data-driven investigations.

Identifiers

PMID30307179
PMCPMC6183068

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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.