MATGANIP: Learning to Discover the Structure-Property Relationship in Perovskites with Generative Adversarial Networks

TL;DR

This paper introduces MATGANIP, a neural network framework combining GANs, graph, convolutional, and LSTM networks to predict structure-property relationships in perovskite materials, achieving high accuracy in quantum property predictions.

Contribution

The work presents a novel neural network architecture, MATGANIP, that effectively models the structure-property relationship in perovskites, extending the application of AI in materials discovery.

Findings

MAE < 0.3 meV/atom in DFT property prediction

MAER < 0.01% demonstrating high accuracy

Effective in modeling complex atomic arrangements in perovskites

Abstract

Accelerating the design of materials with artificial neural network draws more attention due to its magnitude potential. In the past works, some tools of materials information have been developed to promote the industrialize of state-of-the-art materials, such as the materials project, AFlow, and open quantum materials database. Else, more various endeavors are required for artificial general intelligence in the area of materials. In our works, we design neural networks named MATGANIP, which applies a combination of generative adversarial networks, graph networks, convolutional neural networks, and long short term memory for the perovskite materials. We adopt it for the building of a structure-property relationship, where the trained properties contain: the computational geometric property, tolerance factor; and the ground state property of Quantum theory, the vacuum energy. Moreover, the data-set about the ABX3 perovskites is used for the learning of tolerance factor, to extend its function of tolerance factor into the structural identification of the arrangement of disorder atoms; another data-set about the density functional theory (DFT) calculation results is for the ground state energy of quantum theory, to obtain the more accurate result than the training of DFT calculated results in similar works. In our training task of the DFT date-set, we adopt the intuitive criterion to evaluate its performance: the mean absolute error (MAE) is smaller than 0.3 meV/atom, and the mean absolute error rate (MAER) is smaller than 0.01%. These results prove the potential ability of our MATGANIP in developing the relationship between materials structure and their properties. Notably, it suits for some scenes involved massive possible structures and their properties, such as the arrangement of the atoms in the structural phase transition of the inorganic perovskites with mixed atoms.