Sivak Jacob T, Almishal Saeed S I, Caucci Mary Kathleen, Tan Yueze, Srikanth Dhiya, Petruska Joseph, Furst Matthew, Chen Long-Qing, Rost Christina M, Maria Jon-Paul, Sinnott Susan B
The Pennsylvania State University, Department of Chemistry, University Park, Pennsylvania 16802, USA.
The Pennsylvania State University, Department of Materials Science and Engineering, University Park, Pennsylvania 16802, USA.
Phys Rev Lett. 2025 May 30;134(21):216101. doi: 10.1103/PhysRevLett.134.216101.
High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and experiment to understand and explore single-phase rocksalt high-entropy oxides. By leveraging a machine-learning interatomic potential, we rapidly and accurately map high-entropy composition space using our two descriptors: bond length distribution and mixing enthalpy. The single-phase stabilities for all experimentally stabilized rocksalt compositions are correctly resolved, with dozens more compositions awaiting discovery.
高熵材料将传统的材料发现范式转变为一种利用无序性的范式，从而能够获得仅通过焓无法实现的独特化学性质。我们提出了一种将计算与实验相结合的自洽方法，以理解和探索单相岩盐高熵氧化物。通过利用机器学习原子间势，我们使用两个描述符——键长分布和混合焓，快速而准确地绘制高熵成分空间。所有实验稳定的岩盐成分的单相稳定性都得到了正确解析，还有几十种成分有待发现。
