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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.

  • Analytical solutions for the isobaric evaporation of pure cryogens in storage tanks

    Authors: Felipe Huerta, Velisa Vesovic,
    DOI: 10.1016/j.ijheatmasstransfer.2019.118536
    Submitted by hub-admin    

    Why should we attempt to reproduce this paper?

    1. Because it contains customized numerical methods to implement analytical solutions for an engineering problem relevant to cryogenic storage. This will become increasingly relevant in the future with the increase in the use of liquid hydrogen and LNG as fuel. 2. The storage tank is implemented as a Class and there is an opportunity to understand the object oriented programming mindset of the authors. 3. In the provided Jupyter Notebook, thermodynamic data for nitrogen and methane are provided which enable the users the quick implementation. 4. To reproduce some of the figures and results, the storage tanks need to be modified with inputs available in the paper.

    Tags: Python Matlab
  • Good Me Bad Me: Prioritization of the Good-Self During Perceptual Decision-Making

    Authors: Hu, C.-P., Lan, Y., Macrae, C. N., & Sui, J.
    DOI: 10.1525/collabra.301
    Submitted by hub-admin    
      Mean reproducibility score:   7.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    It'll a great helpful to independently check the scientific record I've published, so that errors, if there are any, could be corrected. Also, I will learn how to share the data in a more accessible to other if you could give me feedback.

    Tags: Python R Matlab
  • Cell Contractility Facilitates Alignment of Cells and Tissues to Static Uniaxial Stretch

    Authors: Rens, E. G., & Merks, R. M. H.
    Submitted by hub-admin    
      Mean reproducibility score:   1.0/10   |   Number of reviews:   2
    Why should we attempt to reproduce this paper?

    This paper shows a fun and interesting simulation result. I find it (of course) very important that our results are reproducible. In this paper, however, we did not include the exact code for these specific simulations, but the results should be reproducible using the code of our previous paper in PLOS Computational Biology (Van Oers, Rens et al. https://doi.org/10.1371/journal.pcbi.1003774). I am genuinely curious to see if there is sufficient information for the Biophys J paper or if we should have done better. Other people have already successfully built upon the 2014 (PLOS) paper using our code; see e.g., https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.012408 and https://doi.org/10.1101/701037).

    Tags: C Matlab