Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics

TL;DR

This paper demonstrates that Deep InfoMax, a self-supervised learning method, effectively enhances property prediction in materials informatics by leveraging unlabeled crystal data to improve models trained on small datasets.

Contribution

The study introduces Deep InfoMax pretraining for materials informatics, enabling better property prediction with limited labeled data without reconstructing crystal structures.

Findings

Deep InfoMax improves property prediction accuracy on small datasets.

Pretraining with Deep InfoMax enhances transfer learning for band gap and formation energy.

The approach effectively leverages unlabeled crystal data for supervised tasks.

Abstract

The scarcity of property labels remains a key challenge in materials informatics, whereas materials data without property labels are abundant in comparison. By pretraining supervised property prediction models on self-supervised tasks that depend only on the "intrinsic information" available in any Crystallographic Information File (CIF), there is potential to leverage the large amount of crystal data without property labels to improve property prediction results on small datasets. We apply Deep InfoMax as a self-supervised machine learning framework for materials informatics that explicitly maximises the mutual information between a point set (or graph) representation of a crystal and a vector representation suitable for downstream learning. This allows the pretraining of supervised models on large materials datasets without the need for property labels and without requiring the model to reconstruct the crystal from a representation vector. We investigate the benefits of Deep InfoMax pretraining implemented on the Site-Net architecture to improve the performance of downstream property prediction models with small amounts (<10^3) of data, a situation relevant to experimentally measured materials property databases. Using a property label masking methodology, where we perform self-supervised learning on larger supervised datasets and then train supervised models on a small subset of the labels, we isolate Deep InfoMax pretraining from the effects of distributional shift. We demonstrate performance improvements in the contexts of representation learning and transfer learning on the tasks of band gap and formation energy prediction. Having established the effectiveness of Deep InfoMax pretraining in a controlled environment, our findings provide a foundation for extending the approach to address practical challenges in materials informatics.