Machine learning and Python assisted design and verification of Fe–based amorphous/nanocrystalline alloy
We report a machine learning (ML) and Python assisted strategy to accelerate the design and verification of Fe–based amorphous and nanocrystalline alloy with desired properties. Linear Regression (LR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) a...
Main Authors: | Yichuan Tang, Yuan Wan, Zhongqi Wang, Cong Zhang, Jiani Han, Chaohao Hu, Chengying Tang |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2022-07-01
|
Series: | Materials & Design |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127522003483 |
Similar Items
-
Corrosion Behavior of Amorphous-Nanocrystalline Ni50Ti50 Shape Memory Alloy
by: H. Aghabeygzadeh, et al.
Published: (2020-12-01) -
Accelerated discovery of Fe-based amorphous/nanocrystalline alloy through explicit expression and interpretable information based on machine learning
by: Bo Pang, et al.
Published: (2023-07-01) -
Mössbauer and magnetic studies of FeCoNiCuNbSiB nanocrystalline alloys
by: Grabias Agnieszka, et al.
Published: (2017-06-01) -
Strengthening mechanism of electrocatalytic properties of high activity Fe-based amorphous alloys by low escape work nanocrystals
by: Yaming Zhao, et al.
Published: (2024-03-01) -
Exceptional thermal stability and mechanical properties of dual-phase amorphous-nanocrystalline Al–Mo alloy films
by: Caixia Wang, et al.
Published: (2024-03-01)