  Grain boundaries (GBs) often control the processing and properties of
polycrystalline materials. Here, a potentially transformative research is
represented by constructing GB property diagrams as functions of temperature
and bulk composition, also called "complexion diagrams," as a general materials
science tool on par with phase diagrams. However, a GB has five macroscopic
(crystallographic) degrees of freedom (DOFs). It is essentially a "mission
impossible" to construct property diagrams for GBs as a function of five DOFs
by either experiments or modeling. Herein, we combine isobaric
semi-grand-canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid
MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN)
models to tackle this grand challenge. The DNN prediction is ~108 faster than
atomistic simulations, thereby enabling the construction of the property
diagrams for millions of distinctly different GBs of five DOFs. Notably,
excellent prediction accuracies have been achieved for not only symmetric-tilt
and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter
are more complex and much less understood, but they are ubiquitous and often
limit the performance properties of real polycrystals as the weak links. The
data-driven prediction of GB properties as function of temperature, bulk
composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new
paradigm.
