boundary segregation
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Materialia ◽  
2022 ◽  
Vol 21 ◽  
pp. 101318
Author(s):  
Komal Syed ◽  
Mingjie Xu ◽  
Kenta K. Ohtakia David Kok ◽  
Keyur K. Karandikar ◽  
Olivia A. Graeve ◽  
...  

Author(s):  
A Woonbumroong ◽  
P D Brown ◽  
C B Boothroyd

2022 ◽  
Vol 207 ◽  
pp. 114239
Author(s):  
Peyman Asghari-Rad ◽  
Nhung Thi-Cam Nguyen ◽  
Alireza Zargaran ◽  
Praveen Sathiyamoorthi ◽  
Hyoung Seop Kim

2022 ◽  
Author(s):  
Chongze Hu ◽  
Jian Luo

Grain boundaries (GBs) can critically influence the microstructural evolution and various materials properties. However, a fundamental understanding of GBs in high-entropy alloys (HEAs) is lacking because of the complex couplings...


Author(s):  
Christoph Dösinger ◽  
Tobias Spitaler ◽  
Alexander Reichmann ◽  
Daniel Scheiber ◽  
Lorenz Romaner

AbstractIn today’s digitized world, large amounts of data are becoming available at rates never seen before. This holds true also for materials science where high-throughput simulations and experiments continuously produce new data. Data driven methods are required which can make best use of the information stored in large data repositories. In the present article, two of such data driven methods are presented. First, we apply machine learning to generalize and extend the results obtained from computationally intense density functional theory (DFT) simulations. We show how grain boundary segregation energies can be trained with gradient boosting regression and extended to many more positions in the grain boundary for a complete description. The second method relies on Bayesian inference, which can be used to calibrate models to give data and quantification of the model uncertainty. The method is applied to calibrate parameters in thermodynamic models of the Gibbs energy of Ti-W alloys. The uncertainty of the model parameters is quantified and propagated to the phase boundaries of the Ti-W system.


2021 ◽  
Vol 221 ◽  
pp. 117354
Author(s):  
Anna Sophie Ebner ◽  
Severin Jakob ◽  
Helmut Clemens ◽  
Reinhard Pippan ◽  
Verena Maier-Kiener ◽  
...  

2021 ◽  
Vol 119 (19) ◽  
pp. 194102
Author(s):  
A. Dangwal Pandey ◽  
T. F. Keller ◽  
M. Wenskat ◽  
A. Jeromin ◽  
S. Kulkarni ◽  
...  

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