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  • Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials

    Authors: Berk Onat, Christoph Ortner and James Kermode
    DOI: 10.1063/5.0016005
    Submitted by jameskermode      

    Why should we attempt to reproduce this paper?

    Popular descriptors for machine learning potentials such as the Behler-Parinello atom centred symmetry functions (ACSF) or the Smooth Overlap of Interatomic Potentials (SOAP) are widely used but so far not much attention has been paid to optimising how many descriptor components need to be included to give good results.

  • 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.