Enhancing material property prediction with ensemble deep graph convolutional networks

TL;DR

This paper demonstrates that ensemble deep graph convolutional networks significantly improve the accuracy of material property predictions, such as formation energy, band gap, and density, across a large dataset of inorganic materials.

Contribution

It provides an in-depth evaluation of ensemble strategies in deep graph neural networks for material property prediction, highlighting their effectiveness over traditional methods.

Findings

Ensemble averaging improves prediction precision for key material properties.

Deep graph neural networks benefit from ensemble strategies in accuracy and robustness.

The approach is validated on nearly 34,000 inorganic materials.

Abstract

Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vital for developing advanced technologies across fields such as energy, electronics, and biomedicine, potentially reducing the time and resources needed for new material exploration and promoting rapid innovation cycles. Recent efforts have focused on employing advanced ML algorithms, including deep learning - based graph neural network, for property prediction. Additionally, ensemble models have proven to enhance the generalizability and robustness of ML and DL. However, the use of such ensemble strategies in deep graph networks for material property prediction remains underexplored. Our research provides an in-depth evaluation of ensemble strategies in deep learning - based graph neural network, specifically targeting material property prediction tasks. By testing the Crystal Graph Convolutional Neural Network (CGCNN) and its multitask version, MT-CGCNN, we demonstrated that ensemble techniques, especially prediction averaging, substantially improve precision beyond traditional metrics for key properties like formation energy per atom ($\Delta E^{f}$), band gap ($E_{g}$) and density ($\rho$) in 33,990 stable inorganic materials. These findings support the broader application of ensemble methods to enhance predictive accuracy in the field.