Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

TL;DR

This study benchmarks structure-based graph neural networks for out-of-distribution materials property prediction, revealing significant performance gaps and providing insights to enhance model robustness in realistic material discovery scenarios.

Contribution

It introduces a comprehensive OOD benchmark for GNNs in materials science and analyzes factors affecting their generalization performance.

Findings

State-of-the-art GNNs underperform on OOD tasks

Identified sources of robustness in certain GNN models

Provided insights for improving OOD generalization

Abstract

In real-world material research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation of ML model performances in property prediction of out-of-distribution (OOD) materials that are different from the training set distribution. Traditional performance evaluation of materials property prediction models through random splitting of the dataset frequently results in artificially high performance assessments due to the inherent redundancy of typical material datasets. Here we present a comprehensive benchmark study of structure-based graph neural networks (GNNs) for extrapolative OOD materials property prediction. We formulate five different categories of OOD ML problems for three benchmark datasets from the MatBench study. Our extensive experiments show that current state-of-the-art GNN algorithms significantly underperform for the OOD property prediction tasks on average compared to their baselines in the MatBench study, demonstrating a crucial generalization gap in realistic material prediction tasks. We further examine the latent physical spaces of these GNN models and identify the sources of CGCNN, ALIGNN, and DeeperGATGNN's significantly more robust OOD performance than those of the current best models in the MatBench study (coGN and coNGN), and provide insights to improve their performance.