gblearn: Machine Learning for Grain Boundaries

This is an outdated version. Please see https://github.com/rosenbrockc/gblearn for the most recent.

API Documentation

Recently, we proposed a universal descriptor for grain boundaries that has desirable mathematical properties, and which can be applied to arbitrary grain boundaries. Using this descriptor, we were able to create a feature matrix for machine learning based on the local atomic environments present at the grain boundary. In addition to being useful for predicting grain boundary energy and mobility, the method also allows important atomic environments to be discovered for each of the properties.

If you use this package, please cite the paper:

    @article{Rosenbrock:2017vd,
    author = {Rosenbrock, Conrad W and Homer, Eric R and Csanyi, G{\'a}bor and Hart, Gus L W},
    title = {{Discovering the building blocks of atomic systems using machine learning: application to grain boundaries}},
    journal = {npj Computational Materials},
    year = {2017},
    volume = {3},
    number = {1},
    pages = {29}
    }

You can generate the Local Environment Representation for the Olmsted dataset using the following code. It assumes that all the Olmsted [1] dump files from LAMMPS are in /dbs/olmsted. We tell the framework to store all representations in the /gbs/olmsted folder.

   from gblearn.gb import GrainBoundaryCollection as GBC
   olmsted = GBC("olmsted", "/dbs/olmsted", "/gbs/olmsted",
		 r"ni.p(?P<gbid>\d+).out",
                 rcut=3.25, lmax=12, nmax=12, sigma=0.5)
   olmsted.soap()
   olmsted.LER(0.0025)

References

[1]: Olmsted, D. L., Foiles, S. M. & Holm, E. A. Survey of computed grain boundary properties in face-centered cubic metals: I. Grain boundary energy. Acta Mater. 57, 3694–3703 (2009).