A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks

TL;DR

This paper introduces a workflow that aligns experimental material databases with crystallographic data to enable advanced graph neural network models for property prediction, significantly improving accuracy.

Contribution

It presents a novel alignment process that integrates experimental data with atomic coordinate information, facilitating the use of GNNs and transfer learning in materials discovery.

Findings

Improved MAE in predicting ordering temperatures.

Enhanced CCR in classifying magnetic ground states.

Validated approach with significant performance gains.

Abstract

Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training data, as many properties are too complicated to calculate with high-throughput first principles techniques. To address this, recent research has created experimental databases from information extracted from scientific literature. However, most existing experimental databases do not provide full atomic coordinate information, which prevents them from supporting advanced ML architectures such as Graph Neural Networks (GNNs). In this work, we propose to bridge this gap through an alignment process between experimental databases and Crystallographic Information Files (CIF) from the Inorganic Crystal Structure Database (ICSD). Our approach enables the creation of a database that can fully leverage state-of-the-art model architectures for material property prediction. It also opens the door to utilizing transfer learning to improve prediction accuracy. To validate our approach, we align NEMAD with the ICSD and compare models trained on the resulting database to those trained on NEMAD originally. We demonstrate significant improvements in both Mean Absolute Error (MAE) and Correct Classification Rate (CCR) in predicting the ordering temperatures and magnetic ground states of magnetic materials, respectively.