Evidence map›Paper›PMID 29748868›Full record

ArticleThe European physical journal. E, Soft matter2018

How to simulate patchy particles

Lorenzo Rovigatti, John Russo, Flavio Romano

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Article in The European physical journal. E, Soft matter, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Design strategies for the self-assembly of polyhedral shells.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Charge Anisotropy of Nitrogen: Where Chemical Intuition Fails.Journal of chemical theory and computation · 2020
    Article
  13. Topical Issue on Dielectric Spectroscopy Applied to Soft Matter.The European physical journal. E, Soft matter · 2020
    Article
  14. Article
  15. Topical Issue on Advances in Computational Methods for Soft Matter Systems.The European physical journal. E, Soft matter · 2018
    Article
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

3 authors.

Lorenzo RovigattiCNR-ISC, Uos Sapienza, Piazzale A. Moro 2, 00185, Roma, Italy. lorenzo.rovigatti@gmail.com.
John RussoSchool of Mathematics, University of Bristol, BS8 1TW, Bristol, UK.
Flavio RomanoDipartimento di Scienze Molecolari e Nanosistemi, Università Ca' Foscari di Venezia, Via Torino 155, 30172, Venezia Mestre, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patchy particles is the name given to a large class of systems of mesoscopic particles characterized by a repulsive core and a discrete number of short-range and highly directional interaction sites. Numerical simulations have contributed significantly to our understanding of the behaviour of patchy particles, but, although simple in principle, advanced simulation techniques are often required to sample the low temperatures and long time-scales associated with their self-assembly behaviour. In this work we review the most popular simulation techniques that have been used to study patchy particles, with a special focus on Monte Carlo methods. We cover many of the tools required to simulate patchy systems, from interaction potentials to biased moves, cluster moves, and free-energy methods. The review is complemented by an educationally oriented Monte Carlo computer code that implements all the techniques described in the text to simulate a well-known tetrahedral patchy particle model.

Indexed as

Topical issue: Advances in Computational Methods for Soft Matter Systems

Identifiers

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Registered trials

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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.