Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials

17 February 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Grain boundaries (GBs) in two-dimensional (2D) materials are known to dramatically impact material properties ranging from the physical, chemical, mechanical, electronic, and optical to name a few. Predicting a range of physically realistic GB structures for 2D materials is critical to exercising control over their properties. This, however, is non-trivial given the vast structural and configurational (defect) search space between lateral 2D sheets with varying misfits. Here, in a departure from traditional evolutionary search methods, we introduce a workflow that combines the Graph Neural Network (GNN) and an evolutionary algorithm for the discovery and design of novel 2D lateral interfaces. We use a representative 2D material, blue phosphorene (BP), and identify 2D GB structures to test the efficacy of our GNN model. The GNN was trained with a computationally inexpensive machine learning bond order potential (Tersoff formalism), and density functional theory (DFT). Systematic downsampling of the training datasets indicates that our model can predict structural energy under 0.5% mean absolute error with sparse (< 2000) DFT generated energy labels for training. We further couple the GNN model with a multi-objective genetic algorithm (MOGA) and demonstrate strong accuracy in the ability of the GNN to predict GBs. Our method is generalizable, material agnostic, and is anticipated to accelerate the discovery of 2D GB structures.

Keywords

Graph Neural Networks
Genetic Algorithm
2D Materials
Grain Boundary
Blue phosphorene
Machine Learning
First-principle Simulation

Supplementary materials

Title
Description
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Title
Supporting Information Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials
Description
Supporting information includes additional details for transferring GB structures into graphs; the method for filtering out the disconnected GB structures; the histogram and training result of the DFT R+U dataset; the results of the MOGA search based on the GNN that trained on the SS DFT dataset; the example structures and corresponding novelty score; the hyperparameters used for training, and the challenges and future work of the GNN model.
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