Accelerating search for the polar phase stability of ferroelectric oxide by machine learning

Machine learning emerges to accelerate first-principles calculations in materials discovery and property prediction, but developing high-accuracy prediction models requires training on large datasets of relaxed structures, which can be computationally expensive. Besides, the crystal structure prediction algorithms show promising aspects but often deviate from the ground state, leading to poor predictions. Using a crystal graph convolutional neural network, we propose an alternative and efficient method of finding the ground state by utilizing the total energy of random unrelaxed structures. The prediction model is trained on a small dataset of unrelaxed energy obtained from first-principles calculations and effectively predicts the total energy of random structures with an accuracy almost equivalent to first-principles calculations. The resulting energy landscape is then reconstructed in terms of polarity. This leads to establishing an energy landscape of a perovskite oxide in a cost-effective way, enabling a polar phase stability map of strained BaTiO3, a representative ferroelectric oxide. The proposed methodology efficiently allows to obtain the ground state by eliminating the computationally intensive procedure of structure optimization, thus accelerating the ground state search to map out the phase stability of strained perovskite materials in a practical manner.