Exploring the energy landscape of aluminas through machine learning interatomic potential

作     者：Zhang, Lei Luo, Wenhao Liu, Renxi Chen, Mohan Yan, Zhongbo Cao, Kun 

作者机构：Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices State Key Laboratory of Optoelectronic Materials and Technologies Center for Neutron Science and Technology School of Physics Sun Yat-Sen University Guangzhou510275 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Academy for Advanced Interdisciplinary Studies Peking University Beijing90871 China AI for Science Institute Beijing100080 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Aluminum oxide 

摘      要：Aluminum oxide (alumina, Al2O3) exists in various structures and has broad industrial applications. While the crystal structure of α-Al2O3 is well-established, those of transitional aluminas remain highly debated. In this study, we propose a universal machine learning interatomic potential (MLIP) for aluminas, trained using the neuroevolution potential (NEP) approach. The dataset is constructed through iterative training and farthest point sampling, ensuring the generation of the most representative configurations for an exhaustive sampling of the potential energy surface. The accuracy and generality of the potential are validated through simulations under a wide range of conditions, including high temperatures and pressures. A phase diagram is presented that includes both transitional aluminas and α-Al2O3 based on the NEP. We also successfully extrapolate the phase diagram of aluminas under extreme conditions ([0, 4000] K and [0, 200] GPa ranges of temperature and pressure, respectively), while maintaining high accuracy in describing their properties under more moderate conditions. Furthermore, combined with our developed structure search workflow, the NEP provides an evaluation of existing γ-Al2O3 structure models. The NEP developed in this work enables highly accurate dynamic simulations of various aluminas on larger scales and longer timescales, while also offering new insights into the study of transitional aluminas structures. Copyright © 2024, The Authors. All rights reserved.