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。 虽然 $\alpha$-Al$_2$O$_3$的晶体结构已经明确，但过渡型氧化铝的结构仍存在很大争议。 在本研究中，我们提出了一种适用于氧化铝的通用机器学习原子间势能模型（MLIP），该模型通过神经进化势能（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。 该势能的准确性和通用性通过在各种条件下的模拟得到验证，包括高温和高压条件。 相图被提出，其中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），同时在描述其在更温和条件下的性质时保持高准确性。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的动态模拟，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.