# Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

## TL;DR

This paper introduces a machine learning model that predicts DFT-calculated formation energies of materials using symmetry-labeled graphs, without requiring atomic position data, enabling faster materials screening.

## Contribution

The novel approach uses symmetry-labeled graphs and a message passing neural network to predict formation energies without atomic positions, improving screening efficiency.

## Key findings

- Achieved mean absolute error of 20 meV on OQMD database.
- Achieved mean absolute error of 40 meV on Materials Project database.
- Pretraining on large datasets improves prediction accuracy for specific material subsets.

## Abstract

Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with Density Functional Theory (DFT), but the necessary computer time sets limitations for the investigated material space. Therefore, the development of machine learning models for prediction of DFT calculated properties are currently of interest. A particular challenge for \emph{new} materials is that the atomic positions are generally not known. We present a machine learning model for the prediction of DFT-calculated formation energies based on Voronoi quotient graphs and local symmetry classification without the need for detailed information about atomic positions. The model is implemented as a message passing neural network and tested on the Open Quantum Materials Database (OQMD) and the Materials Project database. The test mean absolute error is 20 meV on the OQMD database and 40 meV on Materials Project Database. The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set. Pretraining on OQMD and subsequent training on 100 selenides result in a mean absolute error below 0.1 eV for the formation energy of the selenides.