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

TL;DR

This paper introduces a novel approach combining genetic algorithms, deep neural networks, and hybrid simulations to efficiently construct grain boundary property diagrams across five degrees of freedom, significantly advancing materials science modeling.

Contribution

It presents a new data-driven framework that predicts grain boundary properties across five crystallographic degrees of freedom using deep learning, greatly speeding up traditional atomistic simulations.

Findings

DNN prediction is ~10^8 times faster than atomistic simulations.

High accuracy achieved for symmetric, asymmetric, and mixed tilt-twist grain boundaries.

Enables construction of property diagrams in a 7D space for complex grain boundaries.

Abstract

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.