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

Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams: Addressing the Challenge of Five Degrees of Freedom