Machine Learning Model to Predict the Formation Energy of Copper-based Ternary Alloys
DOI:
https://doi.org/10.3126/jist.v29i2.70481Keywords:
Cu-based alloys, featurizer, formation energy, hyperparameters, machine learningAbstract
Formation energy plays a crucial role in material development, serving as a key metric for understanding material stability and behavior. In this study, machine learning algorithms, namely Random Forest Regressor (RFR) and Gradient Boosting Regressor (GBR), were employed to predict the formation energy of copper-based ternary alloys. The models were implemented using the Scikit-Learn library within the Anaconda distribution. A composition-based featurizer, Magpie elemental properties, from the Matminer toolkit, were utilized to represent the alloy's features. The results demonstrate that the composition-based featurizer effectively captures the relationship between alloy composition and formation energy. Among the models, GBR outperformed RFR, explaining 94% of the variance in formation energy using only five features, compared to 92.5% explained by RFR, which required ten features. These findings highlight the efficiency and accuracy of GBR in predicting formation energy with fewer input features. This work underscores the potential of machine learning models, particularly the GBR, as powerful tools for accelerating material discovery and design. By enabling reliable and efficient predictions, these models provide a pathway to streamline material development processes.
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Alade, I. O., Abd Rahman, M. A., Abbas, Z., Yaakob, Y., & Saleh, T. A. (2020). Application of support vector regression and artificial neural network for prediction of specific heat capacity of aqueous nanofluids of copper oxide. Solar Energy, 197, 485–490. https://doi.org/10.1016/j.solener.2020.01.022
Aldosari, M. N., Yalamanchi, K. K., Gao, X., & Sarathy, S. M. (2021). Predicting entropy and heat capacity of hydrocarbons using machine learning. Energy and AI, 4, 100054. https://doi.org/10.1016/j.egyai.2021.100054
Anaconda Inc. (2020). Anaconda software distribution. Retrieved September 02, 2024, from https://www.anaconda.com.
Bitencourt-Ferreira, G., & de Azevedo, W. F. (2018). Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophysical Chemistry, 240, 63–69. https://doi.org/10.1016/j.bpc.2018.07.002
Breiman, L. (1997). Arcing the edge. Technical Report No. 486. Statistics Department, University of California, Berkeley.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., & others. (2013). API design for machine learning software: Experiences from the Scikit-learn project. arXiv preprint arXiv:1309.0238.
Dasgupta, R. (2014). A look into Cu-based shape memory alloys: Present scenario and future prospects. Journal of Materials Research, 29(16), 1681–1698. https://doi.org/10.1557/jmr.2014.196
Desgranges, C., & Delhommelle, J. (2018). A new approach for the prediction of partition functions using machine learning techniques. Journal of Chemical Physics, 149(4), 044118. https://doi.org/10.1063/1.5031861
Dhungana, A., Yadav, S. K., Mehta, U., Novakovic, R., & Adhikari, D. (2023). Thermodynamic and surface properties of liquid Al-Cu-Ni alloys. Journal of Materials Engineering and Performance, 1–11. https://doi.org/10.1007/s11665-023-07715-y
Faber, F. A., Lindmaa, A., Von Lilienfeld, O. A., & Armiento, R. (2016). Machine learning energies of 2 million elpasolite (ABC2D6) crystals. Physical Review Letters, 117(13), 135502. https://doi.org/10.1103/PhysRevLett.117.135502
Faber, F. A., Lindmaa, A., Von Lilienfeld, O. A., & Armiento, R. (2015). Crystal structure representations for machine learning models of formation energies. International Journal of Quantum Chemistry, 115(16), 1094–1101. https://doi.org/10.1002/qua.24917
Ho, T. K. (1995). Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition (Vol. 1, pp. 278–282). IEEE. https://doi.org/10.1109/ICDAR.1995.598994
Huang, H., Chen, B., Hu, X., Jiang, X., Li, Q., Che, Y., Zu, S., & Liu, D. (2022). Research on Bi contents addition into Sn-Cu-based lead-free solder alloy. Journal of Materials Science: Materials in Electronics, 33(19), 15586–15603. https://doi.org/10.1007/s10854-022-08983-w
Inoue, A., Zhang, W., Zhang, T., & Kurosaka, K. (2001). High-strength Cu-based bulk glassy alloys in Cu-Zr-Ti and Cu-Hf-Ti ternary systems. Materials Transactions, JIM, 42(7), 1147–1152. https://doi.org/10.2320/matertrans1989.42.1147
Islam, M. N., Chan, Y., Rizvi, M. J., & Jillek, W. (2005). Investigations of interfacial reactions of Sn-Zn based and Sn-Ag-Cu lead-free solder alloys as replacement for Sn-Pb solder. Journal of Alloys and Compounds, 400(1–2), 136–144. https://doi.org/10.1016/j.jallcom.2005.04.065
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002. https://doi.org/10.1063/1.4812323
Jani, J. M., Leary, M., Subic, A., & Gibson, M. A. (2014). A review of shape memory alloy research, applications, and opportunities. Materials & Design, 56, 1078–1113. https://doi.org/10.1016/j.matdes.2013.11.084
Kauwe, S. K., Graser, J., Vazquez, A., & Sparks, T. D. (2018). Machine learning prediction of heat capacity for solid inorganics. Integrating Materials and Manufacturing Innovation, 7(2), 43–51. https://doi.org/10.1007/s40192-018-0118-7
Kosec, T., & Milosev, I. (2007). Comparison of a ternary Cu-18Ni-20Zn alloy and binary Cu-based alloys in alkaline solutions. Materials Chemistry and Physics, 104(1), 44–49. https://doi.org/10.1016/j.matchemphys.2007.03.015
Mazzer, E. M., Da Silva, M. R., & Gargarella, P. (2022). Revisiting Cu-based shape memory alloys: Recent developments and new perspectives. Journal of Materials Research, 37(1), 162–182. https://doi.org/10.1557/s43578-021-00409-7
Ohnuma, I., Miyashita, M., Anzai, K., Liu, X. J., Ohtani, H., Kainuma, R., & Ishida, K. (2000). Phase equilibria and the related properties of Sn-Ag-Cu-based Pb-free solder alloys. Journal of Electronic Materials, 29, 1137–1144. https://doi.org/10.1007/s11664-000-0078-2
Olsthoorn, B., Geilhufe, R. M., Borysov, S. S., & Balatsky, A. V. (2019). Band gap prediction for large organic crystal structures with machine learning. Advanced Quantum Technologies, 2(7–8), 1900023. https://doi.org/10.1002/qute.201900023
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., & others. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Ward, L., Dunn, A., Faghaninia, A., Zimmermann, N. E., Bajaj, S., Wang, Q., Montoya, J., Chen, J., Bystrom, K., Dylla, M., & others. (2018). Matminer: An open source toolkit for materials data mining. Computational Materials Science, 152, 60–69. https://doi.org/10.1016/j.commatsci.2018.05.018
Xia, Y., Xie, X., Xie, X., & Lu, C. (2006). Intermetallic compounds evolution between lead-free solder and Cu-based lead frame alloys during isothermal aging. Journal of Materials Science, 41, 2359–2364. https://doi.org/10.1007/s10853-006-7513-3
Zhang, Y., Dang, S., Chen, H., Li, H., Chen, J., Fang, X., Shi, T., & Zhu, X. (2024). Advances in machine learning methods in copper alloys: A review. Journal of Molecular Modeling, 30(12), 398. https://doi.org/10.1007/s00894-024-05549-3
Zhou, Y., Jing, G., Yiting, G., Jun, W., Yan, W., Xiaoxiao, H., Jun, C., Quanjin, L., Qiang, W., & Chenlong, W. (2022). Prediction of formation energies of UCr4C4-type compounds from Magpie feature descriptor-based machine learning approaches. Optical Materials: X, 16, 100196. https://doi.org/10.1016/j.omx.2022.100196
Zhuo, Y., Mansouri Tehrani, A., & Brgoch, J. (2018). Predicting the band gaps of inorganic solids by machine learning. Journal of Physical Chemistry Letters, 9(7), 1668–1673. https://doi.org/10.1021/acs.jpclett.8b00124
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