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  • Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials

    Authors: Bora Karasulu, Jean-Marc Leyssale, Patrick Rowe, Cedric Weber, Carla de Tomas
    DOI: 10.1016/j.carbon.2022.01.031
    Submitted by bkarasulu    
    Number of reviews:   1
    Why should we attempt to reproduce this paper?

    This paper presents a fine example of high-throughput computational materials screening studies, mainly focusing on the carbon nanoclusters of different sizes. In the paper, a set of diverse empirical and machine-learned interatomic potentials, which are commonly used to simulate carbonaceous materials, is benchmarked against the higher-level density functional theory (DFT) data, using a range of diverse structural features as the comparison criteria. Trying to reproduce the data presented here (even if you only consider a subset of the interaction potentials) will help you devise an understanding as to how you could approach a high-throughput structure prediction problem. Even though we concentrate here on isolated/finite nanoclusters, AIRSS (and other similar approaches like USPEX, CALYPSO, GMIN, etc.,) can also be used to predict crystal structures of different class of materials with applications in energy storage, catalysis, hydrogen storage, and so on.

  • The role of conidia in the dispersal of Ascochyta rabiei

    Authors: Khaliq, I., Fanning, J., Melloy, P. et al.
    DOI: 10.1007/s10658-020-02126-2
    Submitted by hub-admin    

    Why should we attempt to reproduce this paper?

    I suggested a few papers last year. I’m hoping that we’ve improved our reproducibility with this one, this year. We’ve done our best to package it up both in Docker and as an R package. I’d be curious to know what the best way to reproduce it is found to be. Working through vignettes or spinning up a Docker instance. Which is the preferred method?

    Tags: R Docker
  • Growth Dynamics of Independent Gametophytes of Pleurosoriopsis makinoi ( Polypodiaceae)

    Authors: Atsushi Ebihara, Joel H. Nitta, Yurika Matsumoto, Yuri Fukazawa, Marie Kurihara, Hitomi Yokote, Kaoru Sakuma, Otowa Azakami, Yumiko Hirayama, Ryoko Imaichi
    Submitted by hub-admin    
      Mean reproducibility score:   10.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    It uses the drake R package that should make reproducibility of R projects much easier (just run make.R and you're done). However, it does depend on very specific package versions, which are provided by the accompanying docker image.

    Tags: R Docker Drake
  • Population structure and phenotypic variation of Sclerotinia sclerotiorum from dry bean (Phaseolus vulgaris) in the United States

    Authors: Kamvar ZN, Amaradasa BS, Jhala R, McCoy S, Steadman JR, Everhart SE
    DOI: 10.7717/peerj.4152
    Submitted by hub-admin    
      Mean reproducibility score:   6.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    This paper is reproduced weekly in a docker container on continuous integration, but it is also set up to work via local installs as well. It would be interesting to see if it's reproducible with a human operator who knows nothing of the project or toolchain.

    Tags: R make Docker