Materials Properties Prediction (MAPP): Empowering the prediction of material properties solely based on chemical formulas

TL;DR

The MAPP framework uses graph neural networks and multi-task learning to accurately predict multiple material properties solely from chemical formulas, advancing materials science research.

Contribution

We introduce the first version of MAPP, a GNN-based framework that predicts material properties from chemical formulas using permutation-invariant graphs and bootstrap training.

Findings

Enhanced prediction accuracy through bootstrap methods.

Improved performance on small datasets via multi-task learning.

Robust permutation-invariant graph representation of materials.

Abstract

Predicting material properties has always been a challenging task in materials science. With the emergence of machine learning methodologies, new avenues have opened up. In this study, we build upon our recently developed Graph Neural Network (GNN) approach to construct models that predict four distinct material properties. Our graph model represents materials as element graphs, with chemical formula serving as the only input. This approach ensures permutation invariance, offering a robust solution to prior limitations. By employing bootstrap methods to train on this individual GNN, we further enhance the reliability and accuracy of our predictions. With multi-task learning, we harness the power of extensive datasets to boost the performance of smaller ones. We introduce the inaugural version of the Materials Properties Prediction (MAPP) framework, empowering the prediction of material properties solely based on chemical formulas.