Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Preparation
2.2. Visualization of Data
2.3. Model Construction and Training
3. Results and Discussion
3.1. Performance Metrics of the BPM during Training and Validation
3.2. GUI Interface on Top of Prediction Model
4. Conclusions
- A Python script was written to enable the automatic extraction of composition and property features from the name of the MPEAs in the dataset of 1301 MPEAs. The phase feature was simplified into two main labels—IM (presence of intermetallic phase) and Not IM (absence of intermetallic phase).
- Pairplots and principal component analyis were utilized for the data visualization. It has been observed that intermetallic phase data were more abundant in the materials with either components less than three (binary intermetallics and medium-entropy alloys) or high-entropy alloys with N > 6. In the multi-principal element alloys with N in the range four to six, the intermetallic phase was less prevalent. The significance of property features or variables in the computation of principal components was quantitatively assessed during PCA.
- An artificial neural network was trained upon the datasets of MPEAs. A model using ReLU and LeakyReLU activation functions at hidden layers, using the Adam optimizer function, and learning rate of 9.5 × 10 exhibited the training accuracy and validation accuracy of 0.9197 and 0.9096, respecctively, at epoch 10. This properly cross-validated model was then chosen as the prediction model.
- In order to ensure the easy usage of the libraries, a GUI software named “IMCATHEA” was built upon the automatic featurization library (preprocessor) blended together with the prediction machine learning model. The availability of an Automatic Featurizer enables the successful IM phase prediciton in the alloy without having the need to manually supply the input features to the prediction model.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AM | Amorphous phase |
ANN | Artificial Neural Network |
BCC | Body-Centered Cubic |
BCE | Binary Cross-Entropy |
BPM | Best Performing Model |
FCC | Face-Centered Cubic |
GUI | Graphical User Interface |
HEA | High-Entropy Alloy |
IM | Intermetallics |
IMC | Intermetallic Compounds |
LeakyReLU | Leaky Rectified Linear Unit |
MEA | Medium-Entropy Alloy |
ML | Machine Learning |
MPEA | Multi-Principal Element Alloy |
PC 1 | Principal Component 1 |
PC 2 | Principal Component 2 |
PCA | Principal Component Analysis |
ReLU | Rectified Linear Unit |
SS | Solid Solution |
SSS | Simple Solid Solution |
SGD | Stochastic Gradient Descent |
VEC | Valence Electron Concentration |
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Properties | Values (This Work) | Values (Ref. [23]) |
---|---|---|
VEC | 8.138 | — |
3.992 | 3.95 | |
Melting temperature (T) | 1792.68 K | 1785.66 K |
—6.6783 kJ/K | —6.75 kJ/K | |
14.87 J/(mol K) | 14.89 J/(mol K) | |
0.1332 | — | |
6.5 % | 5.26 % |
Varied Hyperparameters | Range of Values/Types/Design |
---|---|
Activation functions in HL | Varied combinations of ReLU and LeakyReLU |
Optimizers | Adam, SGD |
Learning rate | range (2.5 × 10–1.0 × 10: constant or stepped (step size = 2.5 × 10)) |
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Subedi, U.; Coutinho, Y.A.; Malla, P.B.; Gyanwali, K.; Kunwar, A. Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys. Metals 2022, 12, 964. https://doi.org/10.3390/met12060964
Subedi U, Coutinho YA, Malla PB, Gyanwali K, Kunwar A. Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys. Metals. 2022; 12(6):964. https://doi.org/10.3390/met12060964
Chicago/Turabian StyleSubedi, Upadesh, Yuri Amorim Coutinho, Prafulla Bahadur Malla, Khem Gyanwali, and Anil Kunwar. 2022. "Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys" Metals 12, no. 6: 964. https://doi.org/10.3390/met12060964