@Article{cmc.2025.067793,
AUTHOR = {Jize Zhang, T. P. C. Klaver, Songge Yang, Brajendra Mishra, Yu Zhong},
TITLE = {Investigation of TWIP/TRIP Effects in the CrCoNiFe System Using a High-Throughput CALPHAD Approach},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {3},
PAGES = {4299--4311},
URL = {http://www.techscience.com/cmc/v84n3/63209},
ISSN = {1546-2226},
ABSTRACT = {Designing high-performance high-entropy alloys (HEAs) with transformation-induced plasticity (TRIP) or twinning-induced plasticity (TWIP) effects requires precise control over stacking fault energy (SFE) and phase stability. However, the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods. A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy (SFE), FCC phase stability, and FCC-to-HCP transition temperatures (T<sub>0</sub>). The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions, enabling efficient extraction of metastable FCC-dominant alloys. The high-throughput results find 214 compositions with desired properties from 160,000 candidates. Detailed analysis of the Gibbs energy distributions, phase fraction trends, and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs. The results show that only a narrow region of the compositional space satisfies all screening criteria, emphasizing the necessity of an integrated approach. The screened compositions and trends provide a foundation for targeted experimental validation. Furthermore, this work demonstrates a scalable, composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.},
DOI = {10.32604/cmc.2025.067793}
}