Deep Neural Networks for Accurate Predictions of Garnet Stability

TL;DR

This paper demonstrates that deep neural networks using only two simple descriptors can accurately predict the stability of garnet crystals, significantly reducing computational costs and enabling rapid exploration of new materials.

Contribution

The study introduces a deep learning model that predicts crystal stability with high accuracy using minimal descriptors, outperforming previous machine learning approaches.

Findings

Deep neural networks achieve 7-8 meV/atom MAE in predicting garnet formation energies.

Model extends effectively to mixed garnets with minimal accuracy loss.

Descriptors based on electronegativity and ionic radii are sufficient for accurate predictions.

Abstract

Predicting the stability of crystals is one of the central problems in materials science. Today, density functional theory (DFT) calculations are the computational tool of choice to obtain energies of crystals with quantitative accuracy. Despite algorithmic and computing advances, DFT calculations remain comparatively expensive and scale poorly with system size. Here we show that deep neural networks utilizing just two descriptors - the Pauling electronegativity and ionic radii - can predict the DFT formation energies of C3A2D3O12 garnets with extremely low mean absolute errors of 7-8 meV/atom, an order of magnitude improvement over previous machine learning models and well within the limits of DFT accuracy. Further extension to mixed garnets with little loss in accuracy can be achieved using a binary encoding scheme that introduces minimal increase in descriptor dimensionality. Our results demonstrate that generalizable deep-learning models for quantitative crystal stability prediction can be built on a small set of chemically-intuitive descriptors. Such models provide the means to rapidly transverse vast chemical spaces to accurately identify stable compositions, accelerating the discovery of novel materials with potentially superior properties.