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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.

  • Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling

    Authors: Sander van Rijn, Sebastian Schmitt, Matthijs van Leeuwen, Thomas Bäck
    Submitted by sjvrijn    
      Mean reproducibility score:   9.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    Because: - Two fellow PhDs working on different topics have been able to reproduce some figures by following the README instructions and I hope this extends to other people - I've tried to incorporate as many of the best practices as possible to make my code and data open and accessible - I've tried to make sure that my data is exactly reproducible with the specified random seed strategy - the paper suggests a method that should be useful to other researchers in my field, which is not useful unless my results are reproducible

  • Determination of the fundamental absorption and optical bandgap of dielectric thin films from single optical transmittance measurements

    Authors: A. Tejada, L. Montañez, C. Torres, P. Llontop, L. Flores-Escalante, F. De Zela, A. Winnacker, and J. A. Guerra
    Submitted by hub-admin    

    Why should we attempt to reproduce this paper?

    We propose a simple method to retrieve optical constants from single optical transmittance measurements, in particular in the fundamental absorption region. The construction of needed envelopes is arbitrary and will depend on the user. However, the method should still be robust and deliver similar results.

  • Algorithm configuration data mining for CMA evolution strategies

    Authors: Sander van Rijn, Hao Wang, Bas van Stein, Thomas Bäck
    DOI: 10.1145/3071178.3071205
    Submitted by sjvrijn    
      Mean reproducibility score:   10.0/10   |   Number of reviews:   1
    Why should we attempt to reproduce this paper?

    The original data took quite a while to produce for a previous paper, but for this paper, all tables and figures should be exactly reproducible by simply running the jupyter notebook.