AI 中文总结
本研究采用机器学习原子间势从近500种A₂O₃型高熵氧化物组成中筛选出16种候选物，经实验发现3种新刚玉型高熵氧化物，证实其出现率远低于预期，且机器学习可有效助力材料发现。
AI 中文摘要
高熵氧化物（HEOs）领域的早期观点强调其可能具有丰富性，组合论证暗示存在大量新材料。但实验现实更具挑战性：仅依靠离子半径、晶格几何和电荷平衡考量无法直接预测HEOs的稳定性。本研究采用机器学习原子间势（MLIPs）预测源自选定三价阳离子的A₂O₃型HEOs的可合成性。从近500种可能组成中，识别出16种有前景的候选物，用于通过固态合成和燃烧合成进行实验验证。我们发现了三种刚玉结构的新HEOs，包括(Al,Cr,Fe,Rh,Sc)₂O₃，以及一种新型阳离子有序相(Al,Fe,Ga,Sc)₂O₃。迄今为止，最常见的合成结果是竞争相的混合物，有时还涉及氧化还原反应。我们的结果还表明，最终产物存在显著的合成方法依赖性，在测试的16种组成中，仅3种在两种合成方法下观察到定性等效结果。我们得出结论，HEOs的出现率远低于最初认为的水平，且机器学习方法能有效引导我们找到“沧海一粟”。
英文摘要
Early thinking in the field of high entropy oxides (HEOs) emphasized their likely abundance, with combinatorial arguments hinting at a myriad of new materials. The experimental reality has proven more challenging: the stability of HEOs cannot be straightforwardly predicted based on ionic radii, lattice geometry, and charge-balancing considerations alone. In this work, we employ machine learning interatomic potentials (MLIPs) to predict the synthesizability of HEOs of the form $A_2$O$_3$ derived from a selection of trivalent cations. From nearly 500 possible compositions, we identify 16 promising candidates for experimental validation with solid-state and combustion synthesis. We discover three new HEOs in the corundum structure, including (Al,Cr,Fe,Rh,Sc)$_2$O$_3$, and one novel cation-ordered phase, (Al,Fe,Ga,Sc)$_2$O$_3$. By far the most common synthesis outcome was a mixture of competing phases, sometimes involving redox reactions. Our results also reveal profound synthesis method dependence for the final product, where qualitatively equivalent outcomes between the two synthesis methods were only observed for 3 of the 16 tested compositions. We conclude that the occurrence rate of HEOs is far rarer than initially believed and that machine learning approaches can effectively guide us to the "needle in the haystack".
