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Grain boundaries (GBs) can critically influence the microstructural evolutionand various materials properties. However, a fundamental understanding of GBsin high-entropy alloys (HEAs) is lacking because of the complex couplings ofthe segregations of multiple elements and interfacial disordering, which cangenerate new phenomena and challenge the classical theories. Here, by combininglarge-scale atomistic simulations and machine learning, we demonstrate thefeasibility of predicting the GB properties as functions of four independentcompositional degrees of freedoms and temperature in a 5D space. Subsequently,GB counterparts to bulk phase diagrams are constructed for the first time forquinary HEAs. A data-driven discovery further reveals new coupled segregationand disordering effects in HEAs. Notably, an analysis of a large datasetdiscovers a critical compensation temperature at which the segregations of allelements virtually vanish simultaneously. While the machine learning model canpredict GB properties via a black-box approach, a surrogate data-basedanalytical model (DBAM) is constructed to provide more physics insights andbetter transferability, with good accuracies. This study not only provides anew paradigm enabling prediction of GB properties in a 5D space, but alsouncovers new GB segregation phenomena in HEAs beyond the classical GBsegregation models.

Decoding Grain Boundary Thermodynamics in High-Entropy Alloys in a 5D Space: Coupled Segregation and Disordering