Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials

TL;DR

This paper introduces a Graph Neural Network approach to model and predict material interface diffusion phenomena, providing insights and tools for material design and engineering.

Contribution

The study presents a novel GNN-based framework tailored for interface diffusion modeling, integrating atomic structure data for accurate predictions and mechanistic insights.

Findings

Accurate prediction of diffusion coefficients and rates.

Effective modeling of complex atomic interactions.

Potential to optimize material interfaces for industrial applications.

Abstract

Understanding and predicting interface diffusion phenomena in materials is crucial for various industrial applications, including semiconductor manufacturing, battery technology, and catalysis. In this study, we propose a novel approach utilizing Graph Neural Networks (GNNs) to investigate and model material interface diffusion. We begin by collecting experimental and simulated data on diffusion coefficients, concentration gradients, and other relevant parameters from diverse material systems. The data are preprocessed, and key features influencing interface diffusion are extracted. Subsequently, we construct a GNN model tailored to the diffusion problem, with a graph representation capturing the atomic structure of materials. The model architecture includes multiple graph convolutional layers for feature aggregation and update, as well as optional graph attention layers to capture complex relationships between atoms. We train and validate the GNN model using the preprocessed data, achieving accurate predictions of diffusion coefficients, diffusion rates, concentration profiles, and potential diffusion pathways. Our approach offers insights into the underlying mechanisms of interface diffusion and provides a valuable tool for optimizing material design and engineering. Additionally, our method offers possible strategies to solve the longstanding problems related to materials interface diffusion.