标题： 通过机器学习原子间势探索氧化铝的能量景观 显示英文标题

标题： Exploring the energy landscape of aluminas through machine learning interatomic potential

摘要： 氧化铝（刚玉，Al$_2$O$_3$）存在多种结构，并具有广泛的工业应用。 尽管 $\alpha$-Al$_2$O$_3$的晶体结构已得到确立，但过渡态氧化铝的结构仍存在很大争议。 在本研究中，我们提出了一种通用的机器学习原子间势（MLIP），该势能通过神经进化势（NEP）方法进行训练。 数据集通过迭代训练和最远点采样构建，确保生成最具代表性的构型，以对势能面进行全面采样。 通过在高温高压等多种条件下的模拟验证了该势能的准确性和普遍性。 一个相图被提出，该相图包括过渡氧化铝和基于NEP的$\alpha$-Al$_2$O$_3$。我们还成功地将氧化铝在极端条件下的相图外推（温度范围为[0, 4000] K，压力范围为[0, 200] GPa），同时保持在更温和条件下描述其性质的高准确性。此外，结合我们开发的结构搜索工作流程，NEP对现有的$\gamma$-Al$_2$O$_3$结构模型进行了评估。本工作中开发的NEP能够在更大尺度和更长时间尺度上对各种氧化铝进行高精度动态模拟，同时为过渡氧化铝结构的研究提供了新的见解。 显示英文摘要

摘要： Aluminum oxide (alumina, Al$_2$O$_3$) exists in various structures and has broad industrial applications. While the crystal structure of $\alpha$-Al$_2$O$_3$ 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 $\alpha$-Al$_2$O$_3$ 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 $\gamma$-Al$_2$O$_3$ 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.