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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.

  • Highly efficient conversion of laser energy to hard X-rays in high intensity laser-solid simulations

    Authors: S. Morris, A. Robinson, C. Ridgers
    DOI: 10.1063/5.0055398
    Submitted by Stuart_Morris      

    Why should we attempt to reproduce this paper?

    There are many applications to multi-MeV X-rays. Their penetrative properties make them good for scanning dense objects for industry, and their ionising properties can destroy tumours in radiotherapy. They are also around the energy of nuclear transitions, so they can trigger nuclear reactions to break down nuclear waste into medical isotopes, or to reveal smuggled nuclear-materials for port security. Laser-driven X-ray generation offers a compact and efficient way to create a bright source of X-rays, without having to construct a large synchrotron. To fully utilise this capability, work on optimising the target design and understanding the underlying X-ray mechanisms are essential. The hybrid-PIC code is in a unique position to model the full interaction, so its ease-of-use and reproducibility are crucial for this field to develop.

  • REMoDNaV: robust eye-movement classification for dynamic stimulation

    Authors: Asim H. Dar, Adina S. Wagner, Michael Hanke
    DOI: https://doi.org/10.3758/s13428-020-01428-x
    Submitted by adswa    

    Why should we attempt to reproduce this paper?

    In theory, reproducing this paper should only require a clone of a public Git repository, and the execution of a Makefile (detailed in the README of the paper repository at https://github.com/psychoinformatics-de/paper-remodnav). We've set up our paper to be dynamically generated, retrieving and installing the relevant data and software automatically, and we've even created a tutorial about it, so that others can reuse the same setup for their work. Nevertheless, we've for example never tried it out across different operating systems - who knows whether it works on Windows? We'd love to share the tips and tricks we found to work, and even more love feedback on how to improve this further.