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.
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.
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.