Most electron beam physics is considered in the context of a vacuum, but there are applications to long-range electron beam transmission in air. As particle acceleration sources become more compact, we may have the chance to take particle beams out to the real world. The example provided in the paper describes that of x-ray backscatter detectors, where significantly stronger signals could be achieved by scanning objects with electron beams. This paper forms the basis for a potential new mode of particle-beam research, and it is important to ensure the reproducibility of this work for groups who wish to explore the applications of this new technology.
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.
DFT calculations are in principle reproducible between different codes, but differences can arise due to poor choice of convergence tolerances, inappropriate use of pseudopotentials and other numerical considerations. An independent validation of the key quantities needed to compute electrical conductivity would be valuable. In this case we have published our input files for calculating the four quantities needed to parametrise the transport simulations from which we compute the electrical conductivity. These are specifically electronic band structure, phonon dispersions, electron-phonon coupling constants and third derivatives of the force constants. Each in turn in more sensitive to convergence tolerances than the last, and it is the final quantity on which the conclusions of the paper critically depend. Reference output data is provided for comparison at the data URL below. We note that the pristine CNT results (dark red line) in figure 3 are an independent reproduction of earlier work and so we are confident the Boltzmann transport simulations are reproducible. The calculated inputs to these from DFT (in the case of Be encapsulation) have not been independently reproduced to our knowledge.
The negative surface enthalpies in figure 5 are surprising. At least one group has attempted to reproduce these using a different code and obtained positive enthalpies. This was attributed to the inability of that code to independently relax the three simulation cell vectors resulting in an unphysical water density. This demonstrates how sensitive these results can be to the particular implementation of simulation algorithms in different codes. Similarly the force field used is now very popular. Its functional form and full set of parameters can be found in the literature. However differences in how different simulation codes implement truncation, electrostatics etc can lead to significant difference in results such as these. It would be a valuable exercise to establish if exactly the same force field as that used here can be reproduced from only its specification in the literature. The interfacial energies of interest should be reproducible with simulations on modest numbers of processors (a few dozen) with run times for each being 1-2 days. Each surface is an independent calculation and so these can be run concurrently during the ReproHack.
Most of the material is available through Jupyter notebooks in GitHub, and it should be easy to reproduce with the help of Binder. With the notebooks, you could experiment with different parameters to the ones analyzed in the paper. It also contains a large dataset of physical parameters of galaxies analysed in this work. We expect this work to be easily reproducible in the steps described in the repository.
Paper and codes+data have been published 4 years ago, will they still work? I always try to release data and codes to reproduce my papers, but I seldom receive feedback. It would be useful to have comments from a reproducers' team, in order to improve sharing for future research (I switched from MATLAB to Python already).
I tried hard to make this paper as reproducible as possible, but as techniques and dependencies become more complex, it is hard to make it 100% clear. Any form of feedback is more than welcome.