I tried as hard as possible to make it reproducible, which it is on my computer. I would be happy to see if this still works on other computers. Moreover, by allowing easy reproducibility, I hope that other people may easily build research on top of this work.
I tried hard to make it reproducible, so hopefully this paper can serve as an example on how reproducibility can be achieved. I think that being reproducible with only few commands to type in a terminal is quite an achievment. At least in my field, where I usually see code published along with paper, but with almost no documentation on how to rerun it.
This article used an open-source python repository for its analysis. It is well-suited for reproduction as more literature evolves on the intersection of urban planning and climate change. The adapted code is published alongside the article.
The method is trained on the data that were available, but it is meant to be re-trainable as soon as new data are published. It would be great to be really sure that even someone else will be able to do it. In case we receive any feedback, we would be really happy to improve our Github repository so as to make the reproduction easier!
We do care about reproducibility. In case we receive any feedback, we would be really happy to improve our Github repository and/or submitted manuscript so as to make the reproduction easier!
Systematically improvable machine learning potentials could have a significant impact on the range of properties that can be modelled, but the toolchain associated with using them presents a barrier to entry for new users. Attempting to reproduce some of our results will help us improve the accessibility of the approach.
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.
