AI 中文总结
开发高通量计算框架，结合密度泛函理论等筛选等摩尔五元NaCl结构型熵稳定氧化物。构建数据库，用特殊准随机结构超胞描述并训练机器学习模型预测稳定性，为多组分氧化物探索提供高效途径。
AI 中文摘要
熵稳定氧化物可进入广阔多组分组成空间，但识别有前景的候选物具有挑战性。本文结合密度泛函理论、特殊准随机结构、凸包热力学和监督机器学习，开发高通量计算框架来筛选等摩尔五元NaCl结构型熵稳定氧化物，建立数据库并训练模型预测稳定性，为多组分氧化物探索提供了有效途径。
英文摘要
Entropy-stabilized oxides (ESOs) open access to vast multicomponent compositional spaces, but identifying promising candidates remains challenging because of the large number of possible mixtures and the need to assess their stability against competing phases. In this work, we develop a high-throughput computational framework to screen equimolar quinary ESOs in the NaCl structure type by combining density functional theory (DFT), special quasirandom structures (SQS), convex-hull thermodynamics, and supervised machine learning. A consistent reference database of binary and ternary ordered oxides, including disordered phases such as all binary cation combinations in the NaCl-type oxide, is first constructed using GGA and meta-GGA calculations. Quinary disordered phases are then described by SQS supercells and used to train machine-learning models that predict the distance to the convex hull and the corresponding stabilization temperature over the full set of 4368 possible equimolar quinary compositions generated from 16 cation species. Among the tested models, an optimized multilayer perceptron provides the best predictive performance, with a test error of about 4 kJ/mol, while requiring explicit DFT calculations for only about 10% of the quinary systems. Comparison with experimental synthesis tests and computed decomposition paths further shows that the approach captures the main stability trends and the dominant competing phases, although absolute stabilization temperatures remain affected by systematic thermodynamic approximations. These results establish an efficient route for the data-driven exploration of multicomponent oxides and provide practical guidance for the experimental search for new ESOs.
Commentsregular article and supplementary materials
Journal refSolid State Sciences 2026
DOI：10.1016/j.solidstatesciences.2026.108443
